{"results":[{"id":"W1537640625","doi":"10.48550/arxiv.1305.5827","title":"Semantic Web Search based on Ontology Modeling using Protege Reasoner","date":"2013-05-24","year":2013,"type":"preprint","authors":"Monica Shekhar | R. A. K. Saravanaguru","authors_count":2,"venue":"arXiv (Cornell University)","publisher":"Cornell University","is_oa":1,"oa_status":"green","license":null,"citations":5,"is_retracted":0,"url":"http://arxiv.org/abs/1305.5827","pdf_url":"https://arxiv.org/pdf/1305.5827","topic":"Semantic Web and Ontologies","field":"Computer Science","keywords":"Semantic reasoner | Protégé | Computer science | Ontology | OWL-S | Semantic Web | Information retrieval | World Wide Web | Ontology Inference Layer | Semantic Web Stack | Natural language processing | Artificial intelligence","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"The Semantic Web works on the existing Web which presents the meaning of information as well-defined vocabularies understood by the people. Semantic Search, at the same time, works on improving the accuracy if a search by understanding the intent of the search and providing contextually relevant results. This paper describes a semantic approach toward web search through a PHP application. The goal was to parse through a user's browsing history and return semantically relevant web pages for the search query provided.","score":0.5125,"junk":0.007,"openalex":"https://openalex.org/W1537640625"},{"id":"W2922078383","doi":"10.33640/2405-609x.1009","title":"About one approach to implementing semantic search","date":"2019-03-12","year":2019,"type":"article","authors":"Murtadha N. Rasol | Jamal Challab | Adnan Al-saeedi","authors_count":3,"venue":"Karbala International Journal of Modern Science","publisher":null,"is_oa":1,"oa_status":"diamond","license":"cc-by-nc-nd","citations":0,"is_retracted":0,"url":"https://doi.org/10.33640/2405-609x.1009","pdf_url":"https://kijoms.uokerbala.edu.iq/cgi/viewcontent.cgi?article=1009&context=home","topic":"Semantic Web and Ontologies","field":"Computer Science","keywords":"Computer science | Semantic search | Information retrieval | Semantics (computer science) | Semantic similarity | Task (project management) | Search engine","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"Traditional search mechanisms are based on the keyword search, which does not consider the semantic links between different concepts. This leads to the loss of relevant documents due to inaccurate query formulation or using contextually close words and concepts in the query. To solve the problems of formulating user queries and interdisciplinarity of concepts, it is suggested to use semantic search. The proposed method for implementing semantic search is applicable to large scopes of text data and is based on using a genetic algorithm. Unlike standard methods for information search, the suggested method allows us to consider the semantics of interrelationships between concepts and to handle interdisciplinary concepts correctly. By the aid of semantic tagging, documents contain concepts that are not present in the user's initial query but are semantically close to the requested concepts. Semantic tagging is performed for each document separately, which provides parallel tagging in several subject areas. By the time of the document ontological profile formation is completed, all semantic distances between pairs of distinguished concepts are calculated. Concepts are considered contextually close if their semantic proximity value is above a certain threshold value that is specified in the search parameters. Building a document ontological profile is a multicriteria task, since it depends on a lot of characteristics, so genetic algorithms can be used to solve it effectively. The developed genetic algorithm is intended for more accurate distribution of weight coefficients and estimation of semantic proximity of concepts.","score":0.4898,"junk":0.005,"openalex":"https://openalex.org/W2922078383"},{"id":"W1594474675","doi":null,"title":"A Phrase-based Ontology Enabled Semantic Processing System for Web Search","date":"2013-07-09","year":2013,"type":"dissertation","authors":"Joseph F. Leone | Dong‐Guk Shin","authors_count":2,"venue":"OpenCommons - UConn (University of Connecticut)","publisher":"University of Connecticut","is_oa":0,"oa_status":"closed","license":null,"citations":0,"is_retracted":0,"url":"https://digitalcommons.lib.uconn.edu/dissertations/267","pdf_url":null,"topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","keywords":"Computer science | Information retrieval | Semantic search | Query expansion | Web search query | Relevance (law) | Search engine | tf–idf | Phrase | Ontology | Phrase search | Semantics (computer science) | Natural language processing | Concept search | Web search engine | Web query classification | Artificial intelligence | Term (time)","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"Semantic processing system (SPS) is a system that performs phrase search of web content. SPS takes a user query in natural language, converts it to a keyword query, expands the keyword query with synonyms, hypernyms, hyponyms, and meronyms, and presents the keyword query to a search engine. SPS then sifts through the search engine result pages extracting grammatical and semantic information from each page for computing the page's relevance to the natural language query. SPS' relevance computation uses semantic matching of phrases rather than term-and-document frequency weighting—a method that is most commonly used by existing web search engines. SPS consults an ontology that is both \"crowd-sourced,\" i.e., built collaboratively and incrementally by the large number of users and \"auto-learned,\" i.e., contextually inferred from sentences containing desired words. SPS would be suitable for the areas of biomedical literature mining, legal document review and discovery, and news/RSS feed monitoring because these are laden with prose text. We implemented a prototype SPS, experimented with it and demonstrate that SPS outperforms a representative keyword based search engine. The strength of SPS stems from its exploitation of phrase semantics, which is not used in the conventional search engines.","score":0.4853,"junk":0.002,"openalex":"https://openalex.org/W1594474675"},{"id":"W2803364173","doi":"10.4018/ijswis.2018070105","title":"Semantic Search Exploiting Formal Concept Analysis, Rough Sets, and Wikipedia","date":"2018-05-15","year":2018,"type":"article","authors":"Yuncheng Jiang | Mingxuan Yang","authors_count":2,"venue":"International Journal on Semantic Web and Information Systems","publisher":"IGI Global","is_oa":0,"oa_status":"closed","license":null,"citations":8,"is_retracted":0,"url":"https://doi.org/10.4018/ijswis.2018070105","pdf_url":null,"topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","keywords":"Semantic search | Computer science | Information retrieval | Semantics (computer science) | Semantic similarity | Ranking (information retrieval) | Similarity (geometry) | Set (abstract data type) | Rough set | Semantic Web | Artificial intelligence | Natural language processing","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"This article describes how the traditional web search is essentially based on a combination of textual keyword searches with an importance ranking of the documents depending on the link structure of the web. However, one of the dimensions that has not been captured to its full extent is that of semantics. Currently, combining search and semantics gives birth to the idea of the semantic search. The purpose of this article is to present some new methods to semantic search to solve some shortcomings of existing approaches. Concretely, the authors propose two novel methods to semantic search by combining formal concept analysis, rough set theory, and similarity reasoning. In particular, the authors use Wikipedia to compute the similarity of concepts (i.e., keywords). The experimental results show that the authors' proposals perform better than some of the most representative similarity search methods and sustain the intuitions with respect to human judgements.","score":0.4842,"junk":0.001,"openalex":"https://openalex.org/W2803364173","versions":[{"id":"W4251425542","type":"book-chapter","year":2019,"doi":"10.4018/978-1-7998-0951-7.ch069","venue":"Natural Language Processing","canonical":false}],"versions_count":2},{"id":"W2467775179","doi":"10.1561/1500000032","title":"Semantic Search on Text and Knowledge Bases","date":"2016-06-23","year":2016,"type":"article","authors":"Hannah Bast | Björn Buchhold | Elmar Haußmann","authors_count":3,"venue":"Foundations and Trends® in Information Retrieval","publisher":"Now Publishers","is_oa":0,"oa_status":"closed","license":null,"citations":157,"is_retracted":0,"url":"https://doi.org/10.1561/1500000032","pdf_url":null,"topic":"Semantic Web and Ontologies","field":"Computer Science","keywords":"Computer science | Semantic search | Information retrieval | Natural language processing | Text mining | Artificial intelligence | Semantic Web","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"This article provides a comprehensive overview of the broad area of semantic search on text and knowledge bases. In a nutshell, semantic search is “search with meaning”. This “meaning” can refer to various parts of the search process: understanding the query (instead of just finding matches of its components in the data), understanding the data (instead of just searching it for such matches), or representing knowledge in a way suitable for meaningful retrieval. Semantic search is studied in a variety of different communities with a variety of different views of the problem. In this survey, we classify this work according to two dimensions: the type of data (text, knowledge bases, combinations of these) and the kind of search (keyword, structured, natural language). We consider all nine combinations. The focus is on fundamental techniques, concrete systems, and benchmarks. The survey also considers advanced issues: ranking, indexing, ontology matching and merging, and inference. It also provides a succinct overview of natural language processing techniques that are useful for semantic search: POS tagging, named-entity recognition and disambiguation, sentence parsing, and word vectors. The survey is as self-contained as possible, and should thus also serve as a good tutorial for newcomers to this fascinating and highly topical field.","score":0.4794,"junk":0.001,"openalex":"https://openalex.org/W2467775179","versions":[{"id":"W4301466821","type":"book","year":2016,"doi":"10.1561/9781680831658","venue":"now publishers, Inc. eBooks","canonical":false}],"versions_count":2},{"id":"W6908602408","doi":"10.30481/lis.2023.379865.2040","title":"Semantic Search from Viewpoints of Iranian Authors of Scientific Publications on COVID-19 Indexed in PubMed","date":"2024-05-01","year":2024,"type":"article","authors":"Nahid Khooshian | Roya Baradar | Amir Ghaebi","authors_count":3,"venue":"DOAJ (DOAJ: Directory of Open Access Journals)","publisher":null,"is_oa":0,"oa_status":"closed","license":null,"citations":0,"is_retracted":0,"url":"https://doaj.org/article/8a872fee7bd84ea89bef546f2d16db08","pdf_url":null,"topic":"Cultural Identity and Heritage","field":"Arts and Humanities","keywords":"Semantic search | Meaning (existential) | Semantic computing | Semantic similarity | Context (archaeology) | Semantic Web | Web search query | Phrase search | Social Semantic Web","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"Objective: Various studies show that lexical or keyword search is not enough, because in this search the meaning of words and semantic relationships between keywords are ignored and semantic understanding is not achieved in this search. The purpose of this research is to investigate and measure the semantic search of users. Semantic search refers to a search that goes beyond the traditional search, i.e., the matching of the keywords of the search request with the keywords of the documents in the system and seeks to understand the meaning and semantic relationships between the keywords in the user's search request, which is used with the development and expansion of the semantic web and ontologies. The focus of this search is on obtaining a higher level of meaning from documents and inquiries and based on its content, which this high level meaning may be provided by ontologies. Semantic search expands the meaning and seeks machine understanding of the user's intent, query request and relationships between words and elevates the user's search strategy to a meaningful experience. In fact, semantic search describes the effort to produce accurate results by understanding the searcher's intent, texture and context of the query request, and the relationship between words. This search is of great interest to users and researchers due to its many advantages and applications. But besides the many advantages, it has over traditional and keyword search. It also has some problems and complications that elimination of these problems leads to the improvement and promotion of semantic search and helps users a lot.Methodology: The present study is quantitative in terms of approach and applied in terms of purpose, which has been done by survey method. The data collection tool was a researcher-made questionnaire. The statistical population of the study was 189 Iranian researchers who had published at least a research article on COVID-19, which Indexed in PubMed.Findings: Based on the research findings, from the perspective of Iranian researchers, the degree of recall and precision to semantic search in the PubMed is moderate (3). The amount of semantic gap in semantic search is relatively high (>3), the degree of accuracy and precision in retrieving results in semantic search is moderate (3), the degree of conceptual relevance in semantic search is moderate (3), the rate of retrieval of meaningful multi-word concepts in semantic search is moderate (3) and the rate of improvement and upgrade of overall performance in semantic search is relatively high (>3) in semantic searches at the database has been extensive.Conclusion: The results of the research in general showed that semantic search is a dynamic and challenging search that despite its various strengths and advantages for users in research, but still has its weaknesses and drawbacks that should be reviewed and revised by semantic search support experts such as ontology designers for the semantic search system. Understanding, identifying, and eliminating these weaknesses and problems, make semantic search to be a better way to respond to researchers for their research, as a result, researchers and users can achieve to richer results for compile their research work.","score":0.4883,"junk":0.042,"openalex":"https://openalex.org/W6908602408"},{"id":"W2736741353","doi":"10.17148/ijarcce.2017.65122","title":"Semantic Search on Applicant Tracking System","date":"2017-05-30","year":2017,"type":"article","authors":"Le Quan Ha | M Tareq Rahman","authors_count":2,"venue":"IJARCCE","publisher":null,"is_oa":1,"oa_status":"bronze","license":null,"citations":0,"is_retracted":0,"url":"https://doi.org/10.17148/ijarcce.2017.65122","pdf_url":"https://doi.org/10.17148/ijarcce.2017.65122","topic":"Peer-to-Peer Network Technologies","field":"Computer Science","keywords":"Computer science | Artificial intelligence | Information retrieval | Computer vision | Natural language processing","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"Web search engines often federate many user queries to relevant structured databases.For example, a recruitment-related query might be federated to a jobseekers-and-employers database containing their resumes and skills.The relevant structured data items are then returned to the user along with web search results.Though each structured database is searched in isolation, the search often produces empty / incomplete results as the database may not contain the required information to answer the query.Starting from our Applicant Tracking System (ATS), we have 16 development databases of over 650,000 profile documents of resumes / cover letters / skills.There are on average 238 keywords per document.In fact, per minute there can be up to 200,000 transactions within all these databases.Our existing traditional database search technique (by full-text keyword PostgreSQL search) can be frozen or taking very long to respond unless if we cut off / only search from top profiles, we will not have the search results ready by thirty seconds, but this cut-off limitation returned incorrect results; for example for a query \"Jet fuel Thermal Oxidation\" to request information about job seekers whose resumes contain skills in Oil and Gas industry, in the top ten results there was a conflict in relevance ranking.In order to research a more suitable full-text keyword search technique better than the existing database search, we considered employment of semantic search models.Our semantic search technique has 88% -91.22% accuracy with very much quicker queries that can help users to make a search of 4 keywords of skills completed from 1 second to 28 seconds.Furthermore, the semantic search engine becomes very strong that users can search by entering a whole text paragraph.We designed a combination of semantic search that look for web pages per search and database search.","score":0.479,"junk":0.011,"openalex":"https://openalex.org/W2736741353"},{"id":"W2295393218","doi":"10.1109/bigcomp.2016.7425818","title":"A semantic search technique with Wikipedia-based text representation model","date":"2016-01-01","year":2016,"type":"conference-paper","authors":"Ki-Joo Hong | Hanjoon Kim","authors_count":2,"venue":"2016 International Conference on Big Data and Smart Computing (BigComp)","publisher":null,"is_oa":0,"oa_status":"closed","license":null,"citations":5,"is_retracted":0,"url":"https://doi.org/10.1109/bigcomp.2016.7425818","pdf_url":null,"topic":"Semantic Web and Ontologies","field":"Computer Science","keywords":"Computer science | Information retrieval | Semantic search | Metadata | Semantic grid | Ontology | Semantic computing | Semantic integration | Semantic similarity | Semantic Web Stack | World Wide Web | Search engine | Semantic Web","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"Semantic search is known as a series of activities and techniques to improve the search accuracy by clearly understanding users' search intent. Usually, semantic search engines requires ontology and semantic metadata to analyze user queries. However, building a particular ontology and semantic metadata intended for large amounts of data is a very time-consuming and costly task. In order to resolve this problem, we propose a novel semantic search method that does not require ontologies and semantic metadata by taking advantage of semantically enriched text model. Through extensive experiments using the OSHUMED document collection and SCOPUS library data, we show that our proposed method improves users' search satisfaction.","score":0.4792,"junk":0.016,"openalex":"https://openalex.org/W2295393218"},{"id":"W2018505762","doi":"10.1016/j.jksuci.2013.10.006","title":"Effective semantic search using thematic similarity","date":"2013-10-22","year":2013,"type":"article","authors":"Sharifullah Khan | Jibran Mustafa","authors_count":2,"venue":"Journal of King Saud University - Computer and Information Sciences","publisher":"Elsevier BV","is_oa":1,"oa_status":"hybrid","license":"cc-by-nc-nd","citations":4,"is_retracted":0,"url":"https://doi.org/10.1016/j.jksuci.2013.10.006","pdf_url":null,"topic":"Web Data Mining and Analysis","field":"Computer Science","keywords":"Computer science | Information retrieval | Semantic search | Semantic similarity | Relevance (law) | Web search query | Ontology | Similarity (geometry) | Context (archaeology) | Thematic map | Concept search | Focus (optics) | Search engine | Artificial intelligence | Image (mathematics)","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"Most existing semantic search systems expand search keywords using domain ontology to deal with semantic heterogeneity . They focus on matching the semantic similarity of individual keywords in a multiple-keywords query; however, they ignore the semantic relationships that exist among the keywords of the query themselves. The systems return less relevant answers for these types of queries. More relevant documents for a multiple-keywords query can be retrieved if the systems know the relationships that exist among multiple keywords in the query. The proposed search methodology matches patterns of keywords for capturing the context of keywords, and then the relevant documents are ranked according to their pattern relevance score. A prototype system has been implemented to validate the proposed search methodology. The system has been compared with existing systems for evaluation. The results demonstrate improvement in precision and recall of search.","score":0.4758,"junk":0.005,"openalex":"https://openalex.org/W2018505762"},{"id":"W2124564444","doi":"10.5120/18789-0125","title":"Semantic Search based on Ontology Alignment for Information Retrieval","date":"2014-12-18","year":2014,"type":"article","authors":"Manju Mony | Jyothi M. Rao | Manish M. Potey","authors_count":3,"venue":"International Journal of Computer Applications","publisher":"Foundation of Computer Science","is_oa":1,"oa_status":"bronze","license":null,"citations":7,"is_retracted":0,"url":"http://doi.org/10.5120/18789-0125","pdf_url":"https://doi.org/10.5120/18789-0125","topic":"Semantic Web and Ontologies","field":"Computer Science","keywords":"Computer science | Information retrieval | Ontology | World Wide Web | Natural language processing","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"Traditional search is keyword based search and does not focus on relationship between the words.In semantic search, the search is performed on basis of the meaning of the terms and concepts.The semantics i.e. meaning is expressed through structured knowledge representation or Ontologies.Resource Description Framework (RDF) is used as the data model and SPARQL query language is used to query the RDF data.Currently, there is lot of RDF based data set, e.g.Dbpedia, Freebase, Geonames etc. Semantic search is gaining popularity in recent times.As systems cannot exist in isolation but need to interact with each other, complex systems may require integration of multiple systems.Semantic search systems may take input from heterogeneous systems, in which case, the common and shared entities have to be identified and mapped properly.Also, in semantic search, user has to enter query in formal query language SPARQL, which is quite difficult to learn and use for laymen.We are presenting herewith, an application in which we have constructed multiple ontologies that are inherently unique but are related to each other.The system performs ontology alignment to allow for inter-operation between them.Also, it provides a natural language query (NLQ) interface.It converts the Natural Language (NL) input to SPARQL query.It answers queries across these multiple ontologies and abstracts them as a single linked unit.Currently, it is working for simple as well as some type of complex queries.","score":0.4776,"junk":0.014,"openalex":"https://openalex.org/W2124564444"},{"id":"W6903630308","doi":"10.13016/m2mw28j7t","title":"Search on the Semantic Web","date":"2005-09-01","year":2005,"type":"article","authors":"Ding, Li | Joshi, Anupam | Peng, Yun | Pan, Rong | Reddivari, Pavan","authors_count":5,"venue":"Maryland Shared Open Access Repository (USMAI Consortium)","publisher":null,"is_oa":1,"oa_status":"green","license":null,"citations":0,"is_retracted":0,"url":"http://hdl.handle.net/11603/12189","pdf_url":"http://hdl.handle.net/11603/12189","topic":"Semantic Web and Ontologies","field":"Computer Science","keywords":"Social Semantic Web | Semantic Web Stack | Data Web | Semantic search | Semantic Web | Semantic analytics | Web standards | Web search engine","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"The Semantic Web provides a way to encode information and knowledge on web pages in a form that is easier for computers to understand and process. This article discusses the issues underlying the discovery, indexing and search over web documents that contain semantic web markup. Unlike conventional Web search engines, which use information retrieval techniques designed for documents of unstructured text, Semantic Web search engines must handle documents comprised of semi-structured data. Moreover, the meaning of data is defined by associated ontologies that are also encoded as semantic web documents whose processing may require significant amount of reasoning. We describe Swoogle, an implemented semantic web search engine that discovers, analyzes, and indexes knowledge encoded in semantic web documents throughout the Web, and illustrate its use to help human users and software agents find relevant knowledge.","score":0.483,"junk":0.036,"openalex":"https://openalex.org/W6903630308"},{"id":"W1972465254","doi":"10.1109/itsim.2010.5561476","title":"Semantic search in digital library semantic technology","date":"2010-06-01","year":2010,"type":"conference-paper","authors":"Nurul Aida Osman | Shahrul Azman Mohd Noah | Nazlia Omar","authors_count":3,"venue":"2010 International Symposium on Information Technology","publisher":null,"is_oa":0,"oa_status":"closed","license":null,"citations":5,"is_retracted":0,"url":"https://doi.org/10.1109/itsim.2010.5561476","pdf_url":null,"topic":"Semantic Web and Ontologies","field":"Computer Science","keywords":"Information retrieval | Computer science | Semantic search | Explicit semantic analysis | Semantic computing | Search engine indexing | Concept search | Ontology | Digital library | Semantic grid | Semantic Web Stack | Semantic similarity | Vector space model | Semantic technology | Ranking (information retrieval) | Human–computer information retrieval | World Wide Web | Search engine | Semantic Web | Web search query","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"Semantic search tries to concentrate on the boundaries of text search by performing search in a conceptual space, based on disambiguated concepts rather than exact words, and by exploiting semantic networks of concepts underlying the texts. This paper discusses an on-going research project in developing a semantic digital library in academic institution. It provides another view of semantic information retrieval for a digital library from the perspective of semantic technology. It then proposes a semantic indexing and retrieval approach which includes an ontology based information retrieval framework. The proposed ontology based retrieval model will be based on an adaptation of the classic vector space model, which includes document annotation, weighting and concept-based ranking algorithm.","score":0.4748,"junk":0.009,"openalex":"https://openalex.org/W1972465254"},{"id":"W7098776838","doi":null,"title":"Search with Meanings: An Overview of Semantic Search Systems","date":"2013-08-12","year":2013,"type":"article","authors":"Wang Wei | Payam M. Barnaghi | Andrzej Bargiela","authors_count":3,"venue":"http://baggins.nottingham.edu.my/~eyx6ww/pub/siwn2008Survey.pdf","publisher":null,"is_oa":0,"oa_status":"closed","license":null,"citations":0,"is_retracted":0,"url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.324.2897","pdf_url":null,"topic":"Plant Ecology and Taxonomy Studies","field":"Agricultural and Biological Sciences","keywords":"Semantic search | Concept search | Semantic data model | Search engine | Semantic computing | Semantics (computer science) | Semantic analysis (machine learning) | Human–computer information retrieval","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"Abstract: Research on semantic search aims to improve conventional information search and retrieval methods, and facilitate information acquisition, processing, storage and retrieval on the semantic web. The past ten years have seen a number of implemented semantic search systems and various proposed frameworks. A comprehensive survey is needed to gain an overall view of current research trends in this field. We have investigated a number of pilot projects and corresponding practical systems focusing on their objectives, methodologies and most distinctive characteristics. In this paper, we report our study and findings based on which a generalised semantic search framework is formalised. Further, we describe issues with regards to future research in this area.","score":0.4758,"junk":0.016,"openalex":"https://openalex.org/W7098776838"},{"id":"W2125397372","doi":"10.5120/8771-2697","title":"Adorning Web Search based on Ontology Semantics Spaces Provisioned through Web Services","date":"2012-10-19","year":2012,"type":"article","authors":"P. Nandhakumar | Mrs. S. Hemalatha | Kashyap Dhruve","authors_count":3,"venue":"International Journal of Computer Applications","publisher":"Foundation of Computer Science","is_oa":1,"oa_status":"bronze","license":null,"citations":1,"is_retracted":0,"url":"http://doi.org/10.5120/8771-2697","pdf_url":"https://doi.org/10.5120/8771-2697","topic":"Semantic Web and Ontologies","field":"Computer Science","keywords":"Computer science | World Wide Web | Provisioning | Ontology | Semantics (computer science) | Semantic Web | OWL-S | Web service | Information retrieval | Semantic Web Stack | Programming language | Telecommunications","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"The data available on the World Wide Web is assumed to be humongous and infinite.Search Engines have emerged to be integral tools of information retrieval from the web.The current search engines available process queries and produce results based on the location and information occurrence on the web pages providing unsatisfactory results [8].Web 3.0 and the semantic web incorporated data on the web in machine readable form providing better search results.This paper introduces a semantic search framework named Semantic Search Framework using Semantic Web Services (SSFSWS) built on service oriented architecture (SOA) targeted to enhancing search responses.The framework consists of semantic search providers offering semantic search services.The semantic search services are composed using the depth first search algorithm.The semantic search services offered relyon the RDF data and its corresponding Ontologies built to provide search responses.An OWL2 language namely SROIQ-DL is considered to build the ontologies and represent complex description logic that exists in the RDF data.The framework also introduces effective caching strategies adopted to improve response times.The framework introduced provides ranking schemes based on the ontology relevance scores of the responses observed.A prototype implementation of the SSFSWS is discussed and its benefits over the existing semantic search engine are clearly discussed in this paper.","score":0.4715,"junk":0.002,"openalex":"https://openalex.org/W2125397372"},{"id":"W2296157284","doi":"10.1145/775152.775250","title":"Semantic search","date":"2003-01-01","year":2003,"type":"conference-abstract","authors":"R. Guha | Rob McCool | Eric F. Miller","authors_count":3,"venue":"Proceedings of the twelfth international conference on World Wide Web - WWW '03","publisher":null,"is_oa":0,"oa_status":"closed","license":null,"citations":760,"is_retracted":0,"url":"https://doi.org/10.1145/775152.775250","pdf_url":null,"topic":"Semantic Web and Ontologies","field":"Computer Science","keywords":"Computer science | Semantic search | Social Semantic Web | Semantic Web Stack | Information retrieval | Semantic Web | Semantic analytics | Data Web | World Wide Web | Semantic computing | Web search query | Semantic grid | Semantics (computer science) | Web standards | Denotation (semiotics) | Search engine | Web service | Programming language","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"Activities such as Web Services and the Semantic Web are working to create a web of distributed machine understandable data. In this paper we present an application called 'Semantic Search' which is built on these supporting technologies and is designed to improve traditional web searching. We provide an overview of TAP, the application framework upon which the Semantic Search is built. We describe two implemented Semantic Search systems which, based on the denotation of the search query, augment traditional search results with relevant data aggregated from distributed sources. We also discuss some general issues related to searching and the Semantic Web and outline how an understanding of the semantics of the search terms can be used to provide better results.","score":0.4735,"junk":0.012,"openalex":"https://openalex.org/W2296157284"},{"id":"W816855285","doi":"10.13088/jiis.2011.17.4.019","title":"Semantic Search : A Survey","date":"2011-12-01","year":2011,"type":"article","authors":"Jin‐Soo Park | Nam‐Won Kim | Min-Jung Choi | Zhe Jin | Youngseok Thomas Choi","authors_count":5,"venue":"Journal of Intelligence and Information Systems","publisher":null,"is_oa":0,"oa_status":"closed","license":null,"citations":1,"is_retracted":0,"url":"http://www.koreascience.or.kr/article/ArticleFullRecord.jsp?cn=JJSHBB_2011_v17n4_19","pdf_url":null,"topic":"Semantic Web and Ontologies","field":"Computer Science","keywords":"Semantic search | Computer science | Information retrieval | Semantic Web | Semantic computing | Semantic grid | Process (computing) | Ranking (information retrieval) | Social Semantic Web | Declaration | Data science | World Wide Web","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"Since the ambitious declaration of the vision of the Semantic Web, a growing number of studies on semantic search have recently been made. However, we recognize that our community has not so much accomplished despite those efforts. We analyze two underlying problems : a lack of a shared notion of semantic search that guides current research, and a lack of a comprehensive view that envisions future work. Based on this diagnosis, we start by defining semantic search as the process of retrieving desired information in response to user's input using semantic technologies such as ontologies. Then, we propose a classification framework in order for the community to obtain the better understanding of semantic search. The proposed classification framework consists of input processing, target source, search methodology, results ranking, and output data type. Last, we apply our proposed framework to prior studies and suggest future research directions.","score":0.4742,"junk":0.016,"openalex":"https://openalex.org/W816855285"},{"id":"W2888269900","doi":"10.1109/confluence.2018.8442792","title":"Semantic Search Revisited","date":"2018-01-01","year":2018,"type":"conference-paper","authors":"Aatif Ahmad Khan | Sanjay Kumar Malik","authors_count":2,"venue":"2018 8th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","publisher":null,"is_oa":0,"oa_status":"closed","license":null,"citations":6,"is_retracted":0,"url":"https://doi.org/10.1109/confluence.2018.8442792","pdf_url":null,"topic":"Semantic Web and Ontologies","field":"Computer Science","keywords":"Semantic search | Computer science | Social Semantic Web | Semantic Web Stack | Semantic Web | Information retrieval | Semantic analytics | World Wide Web | Data Web | Semantic computing | Semantic technology | Ontology | Key (lock) | Semantics (computer science) | Web service","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"Accuracy of search results over web is improving steadily due to ongoing incorporation of semantic web technologies into traditional information retrieval approaches of the present web. These semantic technologies along with knowledge base they contain are being exploited at large scale by all mainstream web search engines to provide conceptually precise search results to satisfy user's efficient information needs on web. In this paper, first, semantic search and its key concerns have been explored and discussed. Second, it revisits various semantic search approaches which process and consume knowledge from semantic technologies to improve precision of search results. Thirdly, keyword to concept mapping is revisited and illustrated using domain Ontology. Finally, key design issues of search engines incorporating semantics have been revisited.","score":0.4722,"junk":0.014,"openalex":"https://openalex.org/W2888269900"},{"id":"W3173270499","doi":"10.17762/ijritcc.v5i1.98","title":"Semantic Search and Social-Semantic Search as Cooperative Approach","date":"2017-01-31","year":2017,"type":"article","authors":"Priya Bhaskar Pandharbale | Mr.Rahul S.Kadam | Mr.Jaydeep B. Patil","authors_count":3,"venue":"International Journal on Recent and Innovation Trends in Computing and Communication","publisher":null,"is_oa":1,"oa_status":"green","license":null,"citations":1,"is_retracted":0,"url":"https://ijritcc.org/index.php/ijritcc/article/download/98/98","pdf_url":null,"topic":"Semantic Web and Ontologies","field":"Computer Science","keywords":"Semantic search | Computer science | Information retrieval | Search engine | Social Semantic Web | Semantics (computer science) | Semantic Web | World Wide Web | Semantic Web Stack | Semantic computing | Semantic matching | Matching (statistics) | Mathematics","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"Social and semantic web can be combined for searching web resources. A semantic search engine can find accurate results and annotate web resources using this cooperative approach.As the volume of information is growing, the syntactically correct outputs given by traditional search engines for the user queries have enlarged directly. In order to find exact answers for user queries many more Semantic Search Engines (SSE) are developed now a day. The Semantic Search Engines use a wide range of methods for matching the semantics behind user queries and the indexed collection of resources. The survey shows the semantic search engines domain, and presents a miscellaneous of perspectives about the different classification of approaches. A comparative scheme is presented here and the prevalent research directions in SSE with the advancements in it are identified for the efficient searching techniques.","score":0.4795,"junk":0.039,"openalex":"https://openalex.org/W3173270499"},{"id":"W2770899718","doi":"10.22159/ajpcr.2017.v10s1.23490","title":"PRIOR ONTOLOGY SELECTION AND QUERY TRANSLATION FOR INFORMATION SEARCH","date":"2017-04-01","year":2017,"type":"article","authors":"N Poornima | Shivam Agrawal | Shivam Agrawal | B. Saleena | B. Saleena","authors_count":5,"venue":"Asian Journal of Pharmaceutical and Clinical Research","publisher":"Innovare Academic Sciences","is_oa":1,"oa_status":"diamond","license":"cc-by-nc","citations":0,"is_retracted":0,"url":"https://doi.org/10.22159/ajpcr.2017.v10s1.23490","pdf_url":"https://innovareacademics.in/journals/index.php/ajpcr/article/download/23490/13182","topic":"Semantic Web and Ontologies","field":"Computer Science","keywords":"Computer science | Information retrieval | SPARQL | Semantic search | Ontology | Query expansion | Search engine | Web search query | Selection (genetic algorithm) | Keyword search | Query language | Concept search | World Wide Web | Semantic Web | Artificial intelligence | RDF","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"Objective: Most of the current search engines follow informal keyword based search. Finding the user intention and improving the relevancy of results are the major issues faced by the current traditional keyword based search. Targeting to solve the problems of traditional search and to boost the retrieval process, a framework for semantic based information retrieval is planned. Methods: Social and wine ontologies are used to find the user intention and retrieving it. User’s natural language queries are translated into SPARQL (SPARQL Protocol and Resource Description Framework query language) query for finding related items from those ontologies.Results: The proposed method makes a significant improvement over traditional search in terms of some searches required for searching a particular number of pages using performance graph.Conclusion: Semantic based search can understand the user intention and gives better results than traditional search.","score":0.468,"junk":0.008,"openalex":"https://openalex.org/W2770899718"},{"id":"W2185656306","doi":null,"title":"SEMANTIC META SEARCH ENGINE USING SEMANTIC SIMILARITY MEASURE","date":"2013-01-01","year":2013,"type":"article","authors":"Anand Mariappan | R. Suresh | V. Subbiah Bharathi","authors_count":3,"venue":"http://www.jatit.org/volumes/Vol50No3/2Vol50No3.pdf","publisher":null,"is_oa":0,"oa_status":"closed","license":null,"citations":3,"is_retracted":0,"url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.818.1770","pdf_url":null,"topic":"Web Data Mining and Analysis","field":"Computer Science","keywords":"Information retrieval | Computer science | Semantic search | Web search query | Web query classification | Semantic similarity | Ranking (information retrieval) | Search engine | Metasearch engine | Query expansion | Semantic Web Stack | Web search engine | Semantic Web","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"Many people use search engines to find their requirements on the web. But, research showed that each search engines covers some parts of the web. Therefore, Meta search engines are invented to combine results of different search engines and increase web search effectiveness due to a larger coverage of indexed web. Additionally, given query should be more specific to retrieve the more relevant web pages. By considering all these factors, semantic Meta search engine is proposed using semantic similarity measure that refines the input query in a more specific way. Initially, query given by the user is input to Wordnet ontology to obtain the neighbor keywords. Then, the query and neighbors are given to semantic similarity measure to choose the most suitable query words. Then, the selected query is given to different search engine like Google, Bing and Yahoo. After retrieving web pages from the web, the ranking of those pages are carried out using the ranking measure. Finally, the experimentation is carried out to prove the efficiency of the semantic Meta search engine using precision, recall and F-measure.","score":0.4716,"junk":0.022,"openalex":"https://openalex.org/W2185656306"},{"id":"W2612381126","doi":null,"title":"Search with Meanings:An Overview of Semantic Search Systems","date":"2008-06-01","year":2008,"type":"article","authors":"W Wang | Payam Barnaghi | A Bargiela","authors_count":3,"venue":"Open Research (University of Surrey)","publisher":"Wiley","is_oa":0,"oa_status":"closed","license":null,"citations":40,"is_retracted":0,"url":"https://openresearch.surrey.ac.uk/esploro/outputs/journalArticle/Search-with-MeaningsAn-Overview-of-Semantic/99513835102346","pdf_url":null,"topic":"Semantic Web and Ontologies","field":"Computer Science","keywords":"Semantic search | Computer science | Information retrieval | Semantic Web | Field (mathematics) | Concept search | Semantic computing | Data science | World Wide Web | Search engine | Web search query","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"Research on semantic search aims to improve conventional information search and retrieval methods, and facilitate information acquisition, processing, storage and retrieval on the semantic web. The past ten years have seen a number of implemented semantic search systems and various proposed frameworks. A comprehensive survey is needed to gain an overall view of current research trends in this field. We have investigated a number of pilot projects and corresponding practical systems focusing on their objectives, methodologies and most distinctive characteristics. In this paper, we report our study and findings based on which a generalised semantic search framework is formalised. Further, we describe issues with regards to future research in this area.","score":0.4693,"junk":0.015,"openalex":"https://openalex.org/W2612381126"},{"id":"W6950032333","doi":"10.5281/zenodo.4514980","title":"Semantic Search System with Metagraph Knowledge Base and Natural Language Processing","date":"2021-01-29","year":2021,"type":"article","authors":"Kanev, Anton | Terekhov, Valery","authors_count":2,"venue":"Zenodo (CERN European Organization for Nuclear Research)","publisher":"European Organization for Nuclear Research","is_oa":1,"oa_status":"green","license":"cc-by","citations":0,"is_retracted":0,"url":"https://doi.org/10.5281/zenodo.4514980","pdf_url":null,"topic":"Oceanographic and Atmospheric Processes","field":"Earth and Planetary Sciences","keywords":"Semantic search | Ranking (information retrieval) | Information extraction | Search engine indexing | Precision and recall | Semantics (computer science) | Semantic compression | Semantic computing | Natural language | Knowledge base","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"Currently, various investigations are actively carried out to improve the precision and recall of information retrieval. Many authors associate this process with the need to analyze the meaning of words. The authors of this paper have proposed a semantic search method using natural language processing and the metagraph knowledge base. The general model and main algorithms of the proposed method for indexing and information extraction are described. Natural language processing capabilities affect the amount of data available for search, thus, the recall of the information extraction system was measured. Marking up a dataset according to meaning depends on the situation and is subjective. Therefore, the precision of semantic search was assessed on an unlabeled dataset using the methodology proposed by the authors. To increase recall, semantic search is complemented by keyword search, and semantics results are used to change the ranking of user query results. The authors suggested set of queries for this investigation. The ranking order for semantic and regular keyword searches was estimated using the metric proposed by the authors.","score":0.4682,"junk":0.011,"openalex":"https://openalex.org/W6950032333","versions":[{"id":"W4403581306","type":"conference-paper","year":2021,"doi":"10.5281/zenodo.4514981","venue":"DOAJ (DOAJ: Directory of Open Access Journals)","canonical":false}],"versions_count":2},{"id":"W3165924184","doi":"10.1145/3442442.3458607","title":"Semantic Search via Entity-Types: The SEMANNOREX Framework","date":"2021-04-19","year":2021,"type":"conference-paper","authors":"Amit Kumar | Govind Govind | Marc Spaniol","authors_count":3,"venue":"Companion Proceedings of the Web Conference 2021","publisher":null,"is_oa":1,"oa_status":"hybrid","license":"cc-by","citations":4,"is_retracted":0,"url":"https://doi.org/10.1145/3442442.3458607","pdf_url":null,"topic":"Semantic Web and Ontologies","field":"Computer Science","keywords":"Computer science | Information retrieval | Ontology | Semantic search | Semantics (computer science) | Semantic Web | Semantic Web Stack | Key (lock) | Representation (politics) | World Wide Web | Semantic computing | Programming language","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"Capturing and exploiting a content’s semantic is a key success factor for Web search. To this end, it is crucial to - ideally automatically - extract the core semantics of the data being processed and link this information with some formal representation, such as an ontology. By intertwining both, search becomes semantic by simultaneously allowing end-users a structured access to the data via the underlying ontology. Connecting both, we introduce the SEMANNOREX framework in order to provide semantically enriched access to a news corpus from Websites and Wikinews.","score":0.4646,"junk":0.0,"openalex":"https://openalex.org/W3165924184"},{"id":"W2121069121","doi":"10.1145/1526709.1526854","title":"An effective semantic search technique using ontology","date":"2009-04-20","year":2009,"type":"conference-paper","authors":"Jihyun Lee | Jun‐Ki Min | Chin‐Wan Chung","authors_count":3,"venue":"Proceedings of the 18th international conference on World wide web","publisher":null,"is_oa":0,"oa_status":"closed","license":null,"citations":16,"is_retracted":0,"url":"https://doi.org/10.1145/1526709.1526854","pdf_url":null,"topic":"Semantic Web and Ontologies","field":"Computer Science","keywords":"Computer science | Ranking (information retrieval) | Information retrieval | Ontology | Semantic search | Semantic Web | Semantic similarity | Resource (disambiguation) | Search engine | Semantic grid | Semantic Web Stack","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"In this paper, we present a semantic search technique considering the type of desired Web resources and the semantic relationships between the resources and the query keywords in the ontology. In order to effectively retrieve the most relevant top-k resources, we propose a novel ranking model. To do this, we devise a measure to determine the weight of the semantic relationship. In addition, we consider the number of meaningful semantic relationships between a resource and keywords, the coverage of keywords, and the distinguishability of keywords. Through experiments using real datasets, we observe that our ranking model provides more accurate semantic search results compared to existing ranking models.","score":0.4679,"junk":0.015,"openalex":"https://openalex.org/W2121069121"},{"id":"W2526459354","doi":"10.5815/ijitcs.2016.10.10","title":"A Study on Semantic Searching, Semantic Search Engines and Technologies Used for Semantic Search Engines","date":"2016-09-30","year":2016,"type":"article","authors":"Junaid Rashid | Muhammad Wasif Nisar","authors_count":2,"venue":"International Journal of Information Technology and Computer Science","publisher":null,"is_oa":1,"oa_status":"diamond","license":null,"citations":12,"is_retracted":0,"url":"https://doi.org/10.5815/ijitcs.2016.10.10","pdf_url":"http://www.mecs-press.org/ijitcs/ijitcs-v8-n10/IJITCS-V8-N10-10.pdf","topic":"Semantic Web and Ontologies","field":"Computer Science","keywords":"Semantic search | Computer science | Search engine | Information retrieval | Semantic Web | Semantic computing | Semantic technology | Metasearch engine | Search analytics | Semantic Web Stack | World Wide Web | Web search query","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"Semantic search engines (SSE) are more efficient than other web engines because in this era of busy life everyone wants an exact answer to his question which only semantic engines can provide.The immense increase in the volume of data, trad itional search engines has increased the number of answers to satisfy the user.This creates the problem to search for the desired answer.To solve this problem, the t rend of developing semantic search engines is increasing day by day.Semantic search engines work to extract the best answer of user queries which exactly fits with it.Trad itional search engines are keyword based which means that they do not know the mean ing of the words which we type in our queries.Due to this reason, the semantic search engines super pass the conventional search engines because they give us mean ingful and well-defined informat ion.In this paper, we will discuss the background of Semantic searching, about semantic search engines; the technology used for the semantic search engines and some of the existing semantic search engines on various factors are compared.","score":0.4655,"junk":0.008,"openalex":"https://openalex.org/W2526459354"},{"id":"W2903390060","doi":"10.29042/2018-3927-3931","title":"Semantic Information Search Based on Graph Analysis","date":"2018-08-31","year":2018,"type":"article","authors":"Kalyani M. Dakhare","authors_count":1,"venue":"Helix","publisher":null,"is_oa":1,"oa_status":"diamond","license":null,"citations":0,"is_retracted":0,"url":"https://doi.org/10.29042/2018-3927-3931","pdf_url":"http://helix.dnares.in/wp-content/uploads/2018/09/3927-3931.pdf","topic":"Semantic Web and Ontologies","field":"Computer Science","keywords":"Computer science | Semantic search | Information retrieval | Graph | Natural language processing | Artificial intelligence | Search engine | Theoretical computer science","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"Purpose of this paper is to resolve the problem of traditional search engines which hardly provide the essential content relevant to the user query.Current techniques for processing of query are mostly based on keywords.Thus, they have limited capabilities to understand the concepts and meaning involved in the user query.The solution for this is a semantic information search.A semantic search overcomes the drawbacks of mismatch associated with traditional keyword-based search.We create a semantic information retrieval system by combining Natural Language Processing and Graph Analysis.The idea of higher-level conceptual understanding of queries is developed to overcome the limitation of the traditional model.We present the method for semantic information retrieval by creating a semantic graph of the query.We then add synonyms and hyponyms of the query terms present in the semantic graph to retrieve the accurate documents.The level of accuracy will be enhanced since the query is analyzed semantically.","score":0.4711,"junk":0.027,"openalex":"https://openalex.org/W2903390060"},{"id":"W2143463036","doi":null,"title":"Semantic Search Using Content Based Search Technique with Spread Activation Technique","date":"2010-09-08","year":2010,"type":"article","authors":"R. Renuga | G. Sudhasadasivam | Vadugapalayam Ponnuvel Sakthivel | Chinnadurai Kathiravan | S. Arunkumar | Ramalingam Shanmugam","authors_count":6,"venue":"http://www.academypublisher.com/ijrte/vol01/no02/ijrte0102226229.pdf","publisher":null,"is_oa":0,"oa_status":"closed","license":null,"citations":2,"is_retracted":0,"url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.170.3447","pdf_url":null,"topic":"Hermeneutics and Narrative Identity","field":"Arts and Humanities","keywords":"Computer science | Content (measure theory) | Information retrieval | Algorithm | Artificial intelligence | Mathematics","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"Abstract—The Semantic Web, an extension of the current web, is an emerging field with the aim of building infrastructure, wherein software agents and people can work in cooperation by sharing knowledge. The emerging Semantic Web community has proposed ontology’s to express knowledge in a machine understandable way. Using this ontology, the search engine is proposed which combines content based search techniques with spread activation techniques applied to a semantic model of a given domain. It is based on a hybrid spread activation algorithm applied to the concept instances graph to find the related concepts in the ontology based on the given query.","score":0.4777,"junk":0.051,"openalex":"https://openalex.org/W2143463036"},{"id":"W3126902826","doi":"10.1088/1742-6596/1703/1/012051","title":"Using semantic field model to create information search engines","date":"2020-12-01","year":2020,"type":"conference-paper","authors":"V E Sachkov | Dmitry Zhukov | Elena Andrianova","authors_count":3,"venue":"Journal of Physics Conference Series","publisher":"IOP Publishing","is_oa":1,"oa_status":"diamond","license":"cc-by","citations":1,"is_retracted":0,"url":"https://doi.org/10.1088/1742-6596/1703/1/012051","pdf_url":"https://iopscience.iop.org/article/10.1088/1742-6596/1703/1/012051/pdf","topic":"Engineering Education and Technology","field":"Computer Science","keywords":"Semantic search | Computer science | Search engine | Field (mathematics) | Semantic computing | Information retrieval | Semantic data model | Semantic Web | Natural language understanding | Search analytics | Semantic technology | Natural language | Artificial intelligence | Natural language processing | Web search query","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"Abstract In the web infrastructure of information search, the use of semantic methods is considered to be a new round of the technology development. With the emergence of big data, a relevant issue is processing large amounts of data to extract valuable knowledge, especially for text files in natural language. Practice shows that traditional natural language search engines cannot always extract the necessary data from such data sets, as they do not take into account several subtle aspects of the language used in human speech. To solve this problem, the possibilities of using semantic search engines for text processing are being explored. This paper discusses the possible use of the semantic field model developed by the authors, to create a semantic search engine. Experiments have shown that using this model can improve the search accuracy. This model can be additionally used in creation of interactive dialogue systems.","score":0.4678,"junk":0.019,"openalex":"https://openalex.org/W3126902826"},{"id":"W844936955","doi":null,"title":"A Study on the Implementation and Evaluation of a Semantic Search System","date":"2008-01-01","year":2008,"type":"article","authors":"Dongil Han | Hyeong-In Kwon | Hojoon Choi","authors_count":3,"venue":"Journal of the Korea society of IT services","publisher":null,"is_oa":0,"oa_status":"closed","license":null,"citations":0,"is_retracted":0,"url":"http://www.koreascience.or.kr/article/ArticleFullRecord.jsp?cn=OTSBB9_2008_v7n3_253","pdf_url":null,"topic":"Diverse Approaches in Healthcare and Education Studies","field":"Medicine","keywords":"Semantic search | Computer science | Semantic analytics | Semantic computing | Semantic Web | Social Semantic Web | Information retrieval | Semantic grid | Semantic technology | Semantic Web Stack | Search engine | World Wide Web | Data science","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"In this paper, we present an application called Semantic Search which is built on different supporting technologies and is designed to improve traditional web searching. The Semantic Search is becoming crucial challenges on semantic web. The assessment and the implementation of the research on Semantic Search is not full-fledged whereas its research is highly interested. Also there exists only little research that offers a commercial use Semantic Search System that should be taken into the account in measuring the effectiveness of a Semantic Search System. This paper proposes an implementation and evaluation for the Semantic Search System. Firstly, we built Semantic Search System which includes a case of development and it's procedure. Secondly, We presented the measurement of our Semantic Search System's effectiveness. Finally, the evaluation offers useful implications to the researchers and practitioners to improve the research level to the commercial use.","score":0.4649,"junk":0.011,"openalex":"https://openalex.org/W844936955"},{"id":"W4403736865","doi":"10.18280/isi.290518","title":"Expanding the User’s Query to Enhance Semantic Information Retrieval Using the Reasoning Mechanism Based on Homomorphism Between Semantic Annotations","date":"2024-10-24","year":2024,"type":"article","authors":"Djamel Nessah | Sofiane Mounine Hemam | Mohamed Lamin Laouadi","authors_count":3,"venue":"Ingénierie des systèmes d information","publisher":"Lavoisier publishing","is_oa":1,"oa_status":"hybrid","license":"cc-by","citations":0,"is_retracted":0,"url":"https://doi.org/10.18280/isi.290518","pdf_url":"https://iieta.org/download/file/fid/145225","topic":"Cognitive Computing and Networks","field":"Computer Science","keywords":"Computer science | Information retrieval | Mechanism (biology) | Semantic query | Homomorphism | Semantic analytics | Semantic computing | Semantic Web | World Wide Web | Web search query | Search engine | Mathematics","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"Semantic search encompasses advanced technological approaches to information discovery and retrieval, employing semantic techniques to extract information from intricately structured data sources.An effective search engine must have the ability of accessing and retrieving information of interest by employing reasoning with conceptual models.However, the structural and semantic information intrinsic in conceptual models is not readily amenable to reasoning and AI-enhanced semantic processing.Therefore, the chosen model should enable understanding the meaning of concepts and the relationships between them.Il should also carefully consider the context of the search, ultimately enhancing the accuracy of the returned results.Among the models that fulfill these objectives, conceptual graphs stand out as particularly interesting.They are built upon a robust theoretical framework that spans multiple domains, including philosophical, psychological, linguistic, and artificial intelligence disciplines.In this paper, we describe a method for semantic search driven by conceptual graph-based representation, and a powerful matching reasoning supported by a projection operation between the semantic annotations associated with the document and the submitted query.","score":0.4667,"junk":0.017,"openalex":"https://openalex.org/W4403736865"},{"id":"W2755333349","doi":"10.4018/ijswis.2017100108","title":"Social Semantic Search","date":"2017-09-15","year":2017,"type":"article","authors":"Laurens De Vocht | Selver Softic | Ruben Verborgh | Erik Mannens | Martin Ebner","authors_count":5,"venue":"International Journal on Semantic Web and Information Systems","publisher":"IGI Global","is_oa":1,"oa_status":"bronze","license":null,"citations":7,"is_retracted":0,"url":"https://doi.org/10.4018/ijswis.2017100108","pdf_url":"https://www.igi-global.com/ViewTitle.aspx?TitleId=189769&isxn=9781522511601","topic":"Semantic Web and Ontologies","field":"Computer Science","keywords":"Computer science | Semantic search | Information retrieval | Metadata | Search engine | World Wide Web | Semantic Web | Controlled vocabulary | Social Semantic Web","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"When researchers formulate search queries to find relevant content on the Web, those queries typically consist of keywords that can only be matched in the content or its metadata. The Web of Data extends this functionality by bringing structure and giving well-defined meaning to the content and it enables humans and machines to work together using controlled vocabularies. Due the high degree of mismatches between the structure of the content and the vocabularies in different sources, searching over multiple heterogeneous repositories of structured data is considered challenging. Therefore, the authors present a semantic search engine for researchers facilitating search in research related Linked Data. To facilitate high-precision interactive search, they annotated and interlinked structured research data with ontologies from various repositories in an effective semantic model. Furthermore, the authors' system is adaptive as researchers can synchronize using new social media accounts and efficiently explore new datasets.","score":0.4616,"junk":0.001,"openalex":"https://openalex.org/W2755333349"},{"id":"W2048336606","doi":"10.3991/ijes.v1i2.2711","title":"Semantic Search Engine","date":"2013-11-02","year":2013,"type":"article","authors":"Senthil Jayavel | S. Margret Anouncia | Abhinav Kapoor","authors_count":3,"venue":"International Journal of Recent Contributions from Engineering Science & IT (iJES)","publisher":"kassel university press","is_oa":1,"oa_status":"gold","license":null,"citations":2,"is_retracted":0,"url":"https://doi.org/10.3991/ijes.v1i2.2711","pdf_url":"https://online-journals.org/index.php/i-jes/article/download/2711/2929","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","keywords":"Information retrieval | Computer science | World Wide Web","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"The aim of this paper is to develop a ' Semantic Search Engine' which is basically used to search content by using an understanding of the user's intent and also the contextual meaning of the search query.Search results are made more relevant and accurate as compared to usual search engines by further involving factors such as query generalization and specialization and concept matching.It is an artificially trained search engine to make intelligent inferences and associations between various important properties of the response object.","score":0.4651,"junk":0.015,"openalex":"https://openalex.org/W2048336606"},{"id":"W7097228165","doi":null,"title":"Semantic Search","date":"2003-01-01","year":2003,"type":"article","authors":"R. Guha | Rob Mccool | Eric M","authors_count":3,"venue":"http://tap.stanford.edu/ess.pdf","publisher":null,"is_oa":0,"oa_status":"closed","license":null,"citations":0,"is_retracted":0,"url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1.2813","pdf_url":null,"topic":"Semantic Web and Ontologies","field":"Computer Science","keywords":"Semantic search | Social Semantic Web | Semantic Web Stack | Semantic Web | Semantic analytics | Semantic computing | Data Web | Semantic grid","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"Activities such as Web Services and the Semantic Web are working to create a web of distributed machine understandable data. In this paper we present an application called Semantic Search which is built on these supporting technologies and is designed to improve traditional web searching. We provide an overview of TAP, the application framework upon which the Semantic Search is built. We describe two implemented Semantic Search systems which, based on the denotation of the search query, augment traditional search results with relevant data aggregated from distributed sources. We also discuss some general issues related to searching and the Semantic Web and outline how an understanding of the semantics of the search terms can be used to provide better results.","score":0.4677,"junk":0.027,"openalex":"https://openalex.org/W7097228165"},{"id":"W4295925553","doi":"10.1201/9781003187974-5","title":"Information Retrieval and Semantic Search","date":"2022-09-15","year":2022,"type":"book-chapter","authors":"Anuradha Thakare | Shilpa Laddha | Ambika Pawar","authors_count":3,"venue":"Hybrid Intelligent Systems for Information Retrieval","publisher":null,"is_oa":0,"oa_status":"closed","license":null,"citations":0,"is_retracted":0,"url":"https://doi.org/10.1201/9781003187974-5","pdf_url":null,"topic":"Semantic Web and Ontologies","field":"Computer Science","keywords":"Information retrieval | Computer science | Semantic search | Search engine","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"Though semantic web (Web 3.0) is an expansion of the present web (Web 2.0), it is worth noting that replacement to Web 2.0 is not possible. Most of the traditional search engines get the answers syntactically correct but in large volume. Efforts are required to improve the traditional keyword-based search engines by considering the semantics in it so that any ordinary user can access precise results quickly and easily. This chapter discusses six different categories of semantic search on the basis of objectives, methodologies, and functionalities, which includes document-oriented search, entity and knowledge-oriented search, multimedia information search, relation-centered search, semantic analytics, and mining-based search.","score":0.4633,"junk":0.018,"openalex":"https://openalex.org/W4295925553"},{"id":"W2134774870","doi":"10.1109/isco.2013.6481186","title":"Web page indexing through page ranking for effective semantic search","date":"2013-01-01","year":2013,"type":"conference-paper","authors":"Robin Sharma | Ankita Kandpal | Priyanka Bhakuni | Rashmi Chauhan | Rayangouda H. Goudar | Asit Tyagi","authors_count":6,"venue":"2013 7th International Conference on Intelligent Systems and Control (ISCO)","publisher":null,"is_oa":0,"oa_status":"closed","license":null,"citations":17,"is_retracted":0,"url":"https://doi.org/10.1109/isco.2013.6481186","pdf_url":null,"topic":"Web Data Mining and Analysis","field":"Computer Science","keywords":"Computer science | Information retrieval | Web page | Semantic search | Search engine indexing | Ranking (information retrieval) | Static web page | HITS algorithm | Web search engine | World Wide Web | Semantic Web Stack | Web search query | Semantic Web | Search engine | Web navigation","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"The Semantic Web realization is based on the availability of a significant mass of metadata for the web content, associated with the appropriate information about the world. Searching the vast internet for small particular information leads to many anomalies in the results of present searching techniques such as imprecision, large search result, unable to interpret the sense of user's query and so on. With the purpose of overcoming the pitfalls of the existing approaches, we have proposed architecture of semantic information retrieval to enhance the relevancy of search results. An algorithm is proposed to compute the rank of the documents and then semantic indexing is being performed by these ranked web pages. In our algorithm, we are considering the two factors for calculating the page rank; one is the frequency of the keyword occurred in the web page and another is associative factor of the same keyword with the meaningful interrogative words which are generally ignored by the existing search schemes. We have tested our algorithm for various documents and hence the proposed approach provides the most relevant results on the top of the result set.","score":0.4586,"junk":0.003,"openalex":"https://openalex.org/W2134774870"},{"id":"W2082078846","doi":"10.1109/skg.2007.274","title":"The Research and Design of the Semantic Search Engine Based on Ontology","date":"2007-10-01","year":2007,"type":"conference-paper","authors":"Yong Qi | Hao Peijie | Yu Hou | Yanan Qiao","authors_count":4,"venue":"Third International Conference on Semantics, Knowledge and Grid (SKG 2007)","publisher":null,"is_oa":0,"oa_status":"closed","license":null,"citations":2,"is_retracted":0,"url":"https://doi.org/10.1109/skg.2007.274","pdf_url":null,"topic":"Advanced Computational Techniques and Applications","field":"Computer Science","keywords":"Computer science | Ontology | Information retrieval | Semantic search | Search engine | Semantic technology | World Wide Web | Upper ontology | Semantic Web Stack | Semantic Web","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"The paper brings forth a semantic search engine framework based on ontology, the technology overcomes traditional search engine's shortcomings such as poor semantic processing capability and understanding capability because of the adoption of text retrieval and greatly lifts the retrieval efficiency.","score":0.4545,"junk":0.0,"openalex":"https://openalex.org/W2082078846"},{"id":"W2546511942","doi":null,"title":"A novel approach for precise search results retrieval based on semantic web technologies","date":"2016-03-16","year":2016,"type":"conference-paper","authors":"Usha Yadav | Mayank Singh | Neelam Duhan | Vishal Kumar Jain","authors_count":4,"venue":"International Conference on Computing for Sustainable Global Development","publisher":null,"is_oa":0,"oa_status":"closed","license":null,"citations":2,"is_retracted":0,"url":"https://ieeexplore.ieee.org/abstract/document/7724487/","pdf_url":null,"topic":"Semantic Web and Ontologies","field":"Computer Science","keywords":"Computer science | Semantic search | Information retrieval | Search engine | Semantics (computer science) | Web search query | Relevance (law) | Context (archaeology) | Semantic Web | World Wide Web | Concept search | Semantic Web Stack | Search analytics | Metasearch engine | Social Semantic Web | Web search engine | Meaning (existential)","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"Web documents are growing exponentially and the results retrieved by traditional search engines are marked by irrelevant and inconsistent results. In order to alleviate this problem and increase degree of relevance, there is need to move towards development of semantic search engines. Such engines produce results by focusing on meaning of context rather than structure of content. The paper describes proposed architecture of semantic search engine that takes web search results as input and refines them with notion of semantics. The output produced would be precise and relevant results in context of user's query.","score":0.4535,"junk":0.0,"openalex":"https://openalex.org/W2546511942"},{"id":"W2181757899","doi":null,"title":"A Semantic Search Engine using Semantic Similarity Measure Between Words","date":"2013-01-01","year":2013,"type":"article","authors":"M. Karthiga | S Sankarananth","authors_count":2,"venue":"Automation and Autonomous Systems","publisher":null,"is_oa":0,"oa_status":"closed","license":null,"citations":3,"is_retracted":0,"url":"http://ijser.org/researchpaper/a-semantic-search-engine-using-semantic-similarity-measure-between-words.pdf","pdf_url":null,"topic":"Topic Modeling","field":"Computer Science","keywords":"Semantic similarity | Information retrieval | Computer science | Semantic search | Search engine | Explicit semantic analysis | Semantic computing | Similarity (geometry) | Semantic Web Stack | Web search query | Word (group theory) | Semantic Web | Semantic technology | Natural language processing | Artificial intelligence | Mathematics","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"Measuring semantic similarity between words is very useful in information retrieval. Semantic similarity measure is so useful in many applications, and in the proposed work it is used to create a model Semantic Search Engine. The Semantic Search Engine uses in one hand a Technical Database for computer technology and a Semantic Similarity database to retrieve the resultant Web page for the query word. When a query word is given in the user interface the search engine first searches for the word in the technical database if the word is present the respective Webpage is displayed. If the word is not present in the technical database then the query word is searched in the semantic similarity database. If there are any similar words for the query word those words are displayed as recommendations to the user. The user has to select one of the similar words from the recommendation and accordingly the result page is retrieved. The semantic similarity measure between the words is evaluated using both Pearson correlation coefficient and Spearman correlation coefficient. The time taken to retrieve the relevant Webpage in semantic search engine is compared with normal search engine. The Precision and Recall is calculated for semantic search engine and the results are compared with normal search engine","score":0.4585,"junk":0.017,"openalex":"https://openalex.org/W2181757899"},{"id":"W2183613242","doi":null,"title":"Improvised Web Search through Web Services on Ontology Based Semantics","date":"2014-01-01","year":2014,"type":"article","authors":"P. Nandhakumar | Mrs. S. Hemalatha","authors_count":2,"venue":null,"publisher":null,"is_oa":0,"oa_status":"closed","license":null,"citations":0,"is_retracted":0,"url":"https://www.ijser.org/researchpaper/Improvised-Web-Search-through-Web-Services-on-Ontology-Based-Semantics.pdf","pdf_url":null,"topic":"Service-Oriented Architecture and Web Services","field":"Computer Science","keywords":"Computer science | Semantic Web Stack | Social Semantic Web | Semantic search | Information retrieval | World Wide Web | Semantic Web | Data Web | Semantic analytics | OWL-S | Web standards | Web service | Web modeling","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"The internet plays a major role in transforming huge amount of information day by day in World Wide Web. Search engine is the important tool source to interact user for information retrieval. The traditional way of finding answers to the search queries provide irrelevant result for the searched quires. Web 3.0 collaborates with semantic web gives better result for searched queries. The main focus of this paper is to give a framework using semantic web services constructed with service oriented architecture (SOA) for improving searching capacity. The proposed methodology uses depth first search algorithm for semantic search services. The semantic search uses RDF data set and work with responses constructed with Ontologies. The proposed study not only improves response time, it also provides ranking schemes based ontology relevant score of the responses observed. Index Terms— Semantic Web, Semantic Web Search, SOA, Ontologies, Semantic Web Services, Semantic Web Service Composition. ——————————  ——————————","score":0.4551,"junk":0.006,"openalex":"https://openalex.org/W2183613242"},{"id":"W229074152","doi":null,"title":"A Study on the Conceptual Modeling and Implementation of a Semantic Search System","date":"2008-01-01","year":2008,"type":"article","authors":"Dong-Il Hana | Hyeong-In Kwonb | Hakjin Chong","authors_count":3,"venue":"Journal of Intelligence and Information Systems","publisher":null,"is_oa":0,"oa_status":"closed","license":null,"citations":2,"is_retracted":0,"url":"http://www.koreascience.or.kr/article/ArticleFullRecord.jsp?cn=JJSHBB_2008_v14n1_67","pdf_url":null,"topic":"Innovation in Digital Healthcare Systems","field":"Health Professions","keywords":"Computer science | Information retrieval | Semantic search | Semantic Web Stack | Semantic computing | Ontology | Semantic grid | Social Semantic Web | Semantic Web | Knowledge representation and reasoning | World Wide Web | Artificial intelligence","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"This paper proposes a design and realization for the semantic search system. The proposed model includes three Architecture Layers of a Semantic Search System ; (they are conceptually named as) the Knowledge Acquisition, the Knowledge Representation and the Knowledge Utilization. Each of these three Layers are designed to interactively work together, so as to maximize the users' information needs. The Knowledge Acquisition Layer includes index and storage of Semantic Metadata from various source of web contents(eg : text, image, multimedia and so on). The Knowledge Representation Layer includes the ontology schema and instance, through the process of semantic search by ontology based query expansion. Finally, the Knowledge Utilization Layer includes the users to search query intuitively, and get its results without the users'knowledge of semantic web language or ontology. So far as the design and the realization of the semantic search site is concerned, the proposedsemantic search system will offer useful implications to the researchers and practitioners so as to improve the research level to the commercial use.","score":0.4558,"junk":0.008,"openalex":"https://openalex.org/W229074152"},{"id":"W2148001776","doi":"10.1109/iciii.2010.559","title":"Research and Design of E-commerce Semantic Search","date":"2010-11-01","year":2010,"type":"conference-paper","authors":"Zhusong Liu | Yuqin Zhang","authors_count":2,"venue":"2010 3rd International Conference on Information Management, Innovation Management and Industrial Engineering","publisher":null,"is_oa":0,"oa_status":"closed","license":null,"citations":6,"is_retracted":0,"url":"https://doi.org/10.1109/iciii.2010.559","pdf_url":null,"topic":"Advanced Computational Techniques and Applications","field":"Computer Science","keywords":"Semantic search | Computer science | Semantic computing | Semantic Web Stack | Information retrieval | Semantic technology | Semantic Web | Semantic grid | Semantics (computer science) | Semantic analytics | Social Semantic Web | Semantic field | World Wide Web","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"After the traditional search techniques analyzed, we discovered the traditional grammar-level based search have led to low quality because of the lack of appropriate information on the semantics. For this reason, this in-depth study seeks for a solution to this problem, and explores new technologies to change the urgency of this situation, This article introduced semantic web technologies to e-commerce search field, and designed a semantic network structure for the new search system, and discussed the key technologies in e-commerce semantic search, such as semantic structure and semantic search algorithm. Compared with traditional search, semantic search can return more relevant semantic information and can extract users' search input more accurate.","score":0.4552,"junk":0.008,"openalex":"https://openalex.org/W2148001776"},{"id":"W2133267196","doi":"10.1109/icdmw.2008.101","title":"Semantic Full-Text Search with ESTER: Scalable, Easy, Fast","date":"2008-12-01","year":2008,"type":"conference-paper","authors":"Holger Bast | Fabian M. Suchanek | Ingmar G. Weber","authors_count":3,"venue":"2008 IEEE International Conference on Data Mining Workshops","publisher":null,"is_oa":0,"oa_status":"closed","license":null,"citations":6,"is_retracted":0,"url":"https://doi.org/10.1109/icdmw.2008.101","pdf_url":null,"topic":"Web Data Mining and Analysis","field":"Computer Science","keywords":"Computer science | Semantic search | Scalability | Search engine | Information retrieval | Ontology | Full text search | Semantic Web | Interface (matter) | Index (typography) | Field (mathematics) | World Wide Web | Database","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"We present a demo of ESTER, a search engine that combines the ease of use, speed and scalability of full-text search with the powerful semantic capabilities of ontologies. ESTER supports full-text queries, ontological queries and combinations of these, yet its interface is as easy as can be: A standard search field with semantic information provided interactively as one types. ESTER works by reducing all queries to two basic operations: prefix search and join, which can be implemented very efficiently in terms of both processing time and index space.We demonstrate the capabilities of ESTER on a combination of the English Wikipedia with the Yago ontology, with response times below 100 milliseconds for most queries, and an index size of about 4 GB. The system can be run both stand-alone and as a Web application.","score":0.4619,"junk":0.033,"openalex":"https://openalex.org/W2133267196"},{"id":"W2044634547","doi":"10.1109/icosc.2015.7050785","title":"Semantic search using a similarity graph","date":"2015-02-01","year":2015,"type":"conference-paper","authors":"Lubomir Stanchev","authors_count":1,"venue":"Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015)","publisher":null,"is_oa":0,"oa_status":"closed","license":null,"citations":6,"is_retracted":0,"url":"https://doi.org/10.1109/icosc.2015.7050785","pdf_url":null,"topic":"Topic Modeling","field":"Computer Science","keywords":"Computer science | Semantic similarity | Graph | Information retrieval | Similarity (geometry) | Artificial intelligence | Natural language processing | Theoretical computer science","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"Given a set of documents and an input query that is expressed in a natural language, the problem of document search is retrieving the most relevant documents. Unlike most existing systems that perform document search based on keywords matching, we propose a search method that considers the meaning of the words in the query and the document. As a result, our algorithm can return documents that have no words in common with the input query as long as the documents are relevant. For example, a document that contains the words “Ford”, “Chrysler” and “General Motors” multiple times is surely relevant for the query “car” even if the word “car” does not appear in the document. Our semantic search algorithm is based on a similarity graph that contains the degree of semantic similarity between terms, where a term can be a word or a phrase. We experimentally validate our algorithm on the Cranfield benchmark that contains 1400 documents and 225 natural language queries. The benchmark also contains the relevant documents for every query as determined by human judgment. We show that our semantic search algorithm produces a higher value for the mean average precision (MAP) score than a keywords matching algorithm. This shows that our approach can improve the quality of the result because the meaning of the words and phrases in the documents and the queries is taken into account.","score":0.4513,"junk":0.005,"openalex":"https://openalex.org/W2044634547"},{"id":"W2038146342","doi":"10.1109/ieem.2009.5373086","title":"Semantic association search and rank method based on spreading activation for the Semantic Web","date":"2009-12-01","year":2009,"type":"conference-paper","authors":"Myungjin Lee | Wooju Kim","authors_count":2,"venue":"2009 IEEE International Conference on Industrial Engineering and Engineering Management","publisher":null,"is_oa":0,"oa_status":"closed","license":null,"citations":18,"is_retracted":0,"url":"https://doi.org/10.1109/ieem.2009.5373086","pdf_url":null,"topic":"Semantic Web and Ontologies","field":"Computer Science","keywords":"Computer science | Semantic Web Stack | Semantic search | Social Semantic Web | Information retrieval | Semantic Web | World Wide Web | OWL-S | Semantic analytics | Web search query | Data Web | Web standards | Web search engine | Web page | Search engine","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"As the information on the Web dramatically increases, the existing web reveals more and more limitations in information search because Web pages are designed only for human consumption by mixing content with presentation. In order to improve this situation, the semantic Web based on ontology comes on the stage by W3C, and it will bring a significant advancement in Web search. To do this, the semantic Web must provide different search methods based on relationships between resources. In this paper, we propose a semantic association search methodology that consists of how to find relevant information for a given user's query in the ontology, that is, a semantic network of resources and properties. From this work, users can search the semantically associated resources with their query as valuable and important information.","score":0.4511,"junk":0.005,"openalex":"https://openalex.org/W2038146342"},{"id":"W2757619458","doi":"10.1504/ijac.2017.10008062","title":"A semi search algorithm towards semantic search using domain ontologies","date":"2017-01-01","year":2017,"type":"article","authors":"Sanjay Kumar Malik | Aatif Ahmad Khan","authors_count":2,"venue":"International Journal of Autonomic Computing","publisher":"Inderscience Publishers","is_oa":0,"oa_status":"closed","license":null,"citations":2,"is_retracted":0,"url":"https://doi.org/10.1504/ijac.2017.10008062","pdf_url":null,"topic":"Semantic Web and Ontologies","field":"Computer Science","keywords":"Semantic search | Computer science | Information retrieval | Semantic Web | Semantics (computer science) | Ontology | Semantic Web Stack | Search engine | Search algorithm | Domain (mathematical analysis) | Algorithm | Mathematics","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"In the current Web 2.0 (O'Reilly, 2005), the problem is of efficient retrieval of concerned specific information due to prevalent keyword-based search. Semantic web (Berners-Lee et al., 2001) provides a hope in this direction by providing search based on semantics. Semantic search plays a significant role in retrieving highly relevant search results considering both context and domain information related to posed queries. Semantic search (Guha et al., 2003) can be approached using ontology (Gruber, 1995), which acts as a foundation for incorporating semantics on the web. In this paper, an attempt has been made to propose an approach towards semantic search in the form of a semi search algorithm using domain ontologies for which a semi search tool has been developed for its implementation and evaluation. Key objective of this approach is to return the end user syntactic matching as well as semantically relevant search results. Evaluation results have been obtained which show significant improvements over precision while maintaining a high recall.","score":0.4524,"junk":0.01,"openalex":"https://openalex.org/W2757619458"},{"id":"W2166355715","doi":"10.1109/wism.2009.51","title":"Semantic Search Based on SVO Constructions in Chinese","date":"2009-11-01","year":2009,"type":"conference-paper","authors":"Ming Zhou Gao | Jicheng Wang","authors_count":2,"venue":"2009 International Conference on Web Information Systems and Mining","publisher":null,"is_oa":0,"oa_status":"closed","license":null,"citations":0,"is_retracted":0,"url":"https://doi.org/10.1109/wism.2009.51","pdf_url":null,"topic":"Advanced Computational Techniques and Applications","field":"Computer Science","keywords":"Computer science | Information retrieval | Query expansion | Concept search | Semantic query | Semantic search | Web query classification | Key (lock) | Web search query | Ontology | Natural language processing | Artificial intelligence | Search engine","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"In Chinese the SVO (subject-verb-object) construction is often appears in the query in information retrieval. The SVO construction will be cut into several separate key words, if the traditional retrieval algorithm is used, it will degenerate to a normal Boolean search, because the semantic meaning may be different when the order of the key words differs, thus the semantic implied in the SVO construction will not be exists, that is to say result of an text-retrieval algorithm will reflected by the order of the key words. For this question: taking the advantages of the structural features of forward and inverted index, the paper gives the definition of query step, document step, analyses their reflection on the retrieval, and proposed the pre-processing algorithm to form the new retrieval model based on VSM. Subsequently, The ontology and semantic web support was given, the procedure of translating user query into formal query for semantic search was also given in detail.","score":0.4512,"junk":0.007,"openalex":"https://openalex.org/W2166355715"},{"id":"W2120977445","doi":"10.1109/icsc.2008.52","title":"Semantic Search Meets the Web","date":"2008-08-01","year":2008,"type":"conference-paper","authors":"Miriam Fernández | Vanessa López | Marta Sabou | Victoria S. Uren | David Vallet | Enrico Motta | Pablo Castells","authors_count":7,"venue":"2008 IEEE International Conference on Semantic Computing","publisher":null,"is_oa":1,"oa_status":"green","license":"other-oa","citations":81,"is_retracted":0,"url":"https://doi.org/10.1109/icsc.2008.52","pdf_url":null,"topic":"Semantic Web and Ontologies","field":"Computer Science","keywords":"Computer science | Semantic search | Information retrieval | Semantic Web | Social Semantic Web | Semantic Web Stack | Semantic analytics | World Wide Web | Ontology | Data Web | Complement (music) | Web search query | Search engine | Web service","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"While semantic search technologies have been proven to work well in specific domains, they still have to confront two main challenges to scale up to the Web in its entirety. In this work we address this issue with a novel semantic search system that a) provides the user with the capability to query Semantic Web information using natural language, by means of an ontology-based Question Answering (QA) system [14] and b) complements the specific answers retrieved during the QA process with a ranked list of documents from the Web [3]. Our results show that ontology-based semantic search capabilities can be used to complement and enhance keyword search technologies.","score":0.4495,"junk":0.002,"openalex":"https://openalex.org/W2120977445","versions":[{"id":"W7014949719","type":"conference-paper","year":2008,"doi":null,"venue":"Open Research Online - ORO (The Open University)","canonical":false}],"versions_count":2},{"id":"W64231825","doi":"10.1145/2567948.2577331","title":"Semantically enhanced keyword search for smartphones","date":"2014-04-07","year":2014,"type":"conference-paper","authors":"Jihoon Ko | Sangjin Shin | Sungkwang Eom | Minjae Song | Dong-Hoon Shin | Kyong-Ho Lee | Yongil Jang","authors_count":7,"venue":"Proceedings of the 23rd International Conference on World Wide Web","publisher":null,"is_oa":0,"oa_status":"closed","license":null,"citations":6,"is_retracted":0,"url":"https://doi.org/10.1145/2567948.2577331","pdf_url":null,"topic":"Web Data Mining and Analysis","field":"Computer Science","keywords":"Computer science | Keyword search | Semantic search | Information retrieval | Android (operating system) | Ontology | Search engine | World Wide Web | Search analytics | Web search query","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"To apply semantic search to smartphones, we propose an efficient semantic search method based on a lightweight mobile ontology. Through a prototype implementation of a semantic search engine on an android smartphone, experimental results show that the proposed method provides more accurate search results and a better user experience compared to the conventional method.","score":0.4613,"junk":0.044,"openalex":"https://openalex.org/W64231825"},{"id":"W6949028125","doi":"10.5281/zenodo.11658260","title":"Khmer Semantic Search Engine (KSE): Digital Information Access and Document Retrieval","date":"2024-06-17","year":2024,"type":"article","authors":"Thuon, Nimol","authors_count":1,"venue":"Zenodo (CERN European Organization for Nuclear Research)","publisher":"European Organization for Nuclear Research","is_oa":1,"oa_status":"green","license":"cc-by","citations":0,"is_retracted":0,"url":"https://doi.org/10.5281/zenodo.11658260","pdf_url":null,"topic":"Information Retrieval and Search Behavior","field":"Computer Science","keywords":"Semantic search | Search engine | Semantics (computer science) | Semantic computing | Semantic technology | Semantic matching | Semantic analysis (machine learning) | Matching (statistics) | Semantic integration","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"The search engine process is crucial for document content retrieval. For Khmer documents, an effective tool is needed to extract essential keywords and facilitate accurate searches. Despite the daily generation of significant Khmer content, Cambodians struggle to find necessary documents due to the lack of an effective semantic searching tool. Even Google does not deliver high accuracy for Khmer content. Semantic search engines improve search results by employing advanced algorithms to understand various content types. With the rise in Khmer digital content-such as reports, articles, and social media feedback-enhanced search capabilities are essential. This research proposes the first Khmer Semantic Search Engine (KSE), designed to enhance traditional Khmer search methods. Utilizing semantic matching techniques and formally annotated semantic content, our tool extracts meaningful keywords from user queries, performs precise matching, and provides the best matching offline documents and online URLs. We propose three semantic search frameworks: semantic search based on a keyword dictionary, semantic search based on ontology, and semantic search based on ranking. Additionally, we developed tools for data preparation, including document addition and manual keyword extraction. To evaluate performance, we created a ground truth dataset and addressed issues related to searching and semantic search. Our findings demonstrate that understanding search term semantics can lead to significantly more accurate results.","score":0.4497,"junk":0.005,"openalex":"https://openalex.org/W6949028125","versions":[{"id":"W4399695849","type":"preprint","year":2024,"doi":"10.48550/arxiv.2406.09320","venue":"arXiv (Cornell University)","canonical":false},{"id":"W4402787042","type":"preprint","year":2024,"doi":"10.21203/rs.3.rs-5132282/v1","venue":"Research Square","canonical":false}],"versions_count":3},{"id":"W4412602225","doi":"10.1504/ijwgs.2025.147144","title":"Semantic search for Japanese sentences based on sentence embedding","date":"2025-01-01","year":2025,"type":"article","authors":"Yoshihiro Adachi | Minoru Uehara","authors_count":2,"venue":"International Journal of Web and Grid Services","publisher":"Inderscience Publishers","is_oa":0,"oa_status":"closed","license":null,"citations":2,"is_retracted":0,"url":"https://doi.org/10.1504/ijwgs.2025.147144","pdf_url":null,"topic":"Natural Language Processing Techniques","field":"Computer Science","keywords":"Computer science | Sentence | Embedding | Natural language processing | Artificial intelligence | Information retrieval","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"Research is progressing in semantic search, in which the meaning of a sentence is represented as a numerical vector called an embedding and sentences are searched for based on their similarity. The ability to process queries combined using logical operators is essential for semantic search. We developed an appropriate technique for processing queries containing logical operators with reference to fuzzy set operations. In this technique, AND and OR between queries take the minimum and maximum similarity of the search results, respectively. A NOT operation on a query subtracts the similarity to the query from 1 and scales the result to obtain the desired search results. We devised an example-based semantic search method that obtains search results that match the user's intention as closely as possible based on the positive and negative example sentences that should and should not be included in the search results, respectively, as specified by the user.","score":0.4495,"junk":0.005,"openalex":"https://openalex.org/W4412602225"},{"id":"W2157256185","doi":"10.1017/s0269888907001233","title":"The usability of semantic search tools: a review","date":"2007-12-01","year":2007,"type":"article","authors":"Victoria S. Uren | Yuangui Lei | Vanessa López | Haiming Liu | Enrico Motta | Marina Giordanino","authors_count":6,"venue":"The Knowledge Engineering Review","publisher":"Cambridge University Press","is_oa":0,"oa_status":"closed","license":null,"citations":65,"is_retracted":0,"url":"https://doi.org/10.1017/s0269888907001233","pdf_url":null,"topic":"Semantic Web and Ontologies","field":"Computer Science","keywords":"Semantic search | Computer science | Semantic computing | Semantic grid | Information retrieval | Exploratory search | Metadata | Usability | Semantic Web Stack | Semantic Web | Semantic analytics | World Wide Web | Concept search | Exploit | Search engine | Web search query | Human–computer interaction","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"Abstract The goal of semantic search is to improve on traditional search methods by exploiting the semantic metadata. In this paper, we argue that supporting iterative and exploratory search modes is important to the usability of all search systems. We also identify the types of semantic queries the users need to make, the issues concerning the search environment and the problems that are intrinsic to semantic search in particular. We then review the four modes of user interaction in existing semantic search systems, namely keyword-based, form-based, view-based and natural language-based systems. Future development should focus on multimodal search systems, which exploit the advantages of more than one mode of interaction, and on developing the search systems that can search heterogeneous semantic metadata on the open semantic Web.","score":0.4483,"junk":0.001,"openalex":"https://openalex.org/W2157256185"},{"id":"W2067350193","doi":"10.1109/sami.2014.6822424","title":"Intelligent searching using delay semantic network","date":"2014-01-01","year":2014,"type":"conference-paper","authors":"Stanislav Dvorscak | Kristína Machová","authors_count":2,"venue":"2014 IEEE 12th International Symposium on Applied Machine Intelligence and Informatics (SAMI)","publisher":null,"is_oa":0,"oa_status":"closed","license":null,"citations":0,"is_retracted":0,"url":"https://doi.org/10.1109/sami.2014.6822424","pdf_url":null,"topic":"Semantic Web and Ontologies","field":"Computer Science","keywords":"Computer science | Semantic search | Semantic computing | Semantic Web Stack | Information retrieval | Semantic Web | Inference | Semantic grid | Social Semantic Web | Semantic compression | Process (computing) | Semantic analytics | Search engine | Semantic network | World Wide Web | Semantic similarity | Semantic technology | Artificial intelligence","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"Article introduces different way how to implement semantic search, using semantic search agent over information obtained directly from web. The paper describes time delay form of semantic network, which we have used for providing of semantic search. Using of time-delay aspect inside semantic network has positive impact in several ways. It provides way how to represent knowledges dependent on time via semantic network, but also how to optimize a process of inference. That is all realized for Wikipedia articles in the form of search engine. The core's implementation is realized in way of massive multithread inference mechanism for massive semantic network.","score":0.4726,"junk":0.083,"openalex":"https://openalex.org/W2067350193"},{"id":"W2099908705","doi":"10.1109/iccsp.2013.6577226","title":"A semantic search engine for answering domain specific user queries","date":"2013-04-01","year":2013,"type":"conference-paper","authors":"S. Sowmya Kamath | Dhivya Piraviperumal | Garima Meena | Srijana Karkidholi | K. Saravana Kumar","authors_count":5,"venue":"2013 International Conference on Communication and Signal Processing","publisher":null,"is_oa":0,"oa_status":"closed","license":null,"citations":2,"is_retracted":0,"url":"https://doi.org/10.1109/iccsp.2013.6577226","pdf_url":null,"topic":"Semantic Web and Ontologies","field":"Computer Science","keywords":"Computer science | Semantic search | Information retrieval | Semantic Web Stack | World Wide Web | Search engine | Semantic Web | Semantics (computer science) | Search analytics | Social Semantic Web | Web search query | Process (computing)","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"With the exponential growth in web content and due to its sheer volume, the answers provided by traditional search engines by query specific keywords to content has resulted in markedly high recall and low precision. In order to alleviate this problem, the notion of incorporating semantics in content and in Search Engines, i.e., a Semantic Search Engine is increasingly crucial. Several Semantic Search Engines (SSEs) have been proposed and deployed till date and each is inherently different from the other. As such, the objective of this paper is to present a discussion on semantically enhanced search engines for intelligent web content discovery. We also present the architecture of a new SSE based on a bottom up approach that focuses on building a semantic base for Web content first and then carry out the process of querying it for attaining high precision and lower recall.","score":0.4494,"junk":0.006,"openalex":"https://openalex.org/W2099908705"},{"id":"W2007210830","doi":"10.1145/1980022.1980145","title":"Hypergeometric distribution based semantic searching technique","date":"2011-02-25","year":2011,"type":"conference-paper","authors":"Narina Thakur | Siddharth Gupta","authors_count":2,"venue":"Proceedings of the International Conference & Workshop on Emerging Trends in Technology","publisher":null,"is_oa":0,"oa_status":"closed","license":null,"citations":0,"is_retracted":0,"url":"https://doi.org/10.1145/1980022.1980145","pdf_url":null,"topic":"Data Management and Algorithms","field":"Computer Science","keywords":"Computer science | Information retrieval | Meaning (existential) | Semantics (computer science) | Semantic Web | Social Semantic Web | Semantic search | World Wide Web | Programming language","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"Semantic Web is, without a doubt, gaining momentum in both industry and academia. The word \"Semantic\" refers to \"meaning\" -- a semantic web is a web of meaning. A web that knows what the entities on the web mean can make use of that knowledge. Why do we think that the web would be improved, if it understood the meaning of its contents? Doesn't it understand it now? Google is very good at correcting typing mistakes, figuring out what I \"meant\" when I miss-typed a query or suggesting keyword to expand our search query. In this paper we redefine the idea of searching related text information on web. The syntactical characters of related keywords and texts are described in detail, but it doesn't involve semantics of the keywords. Hence computers are able to determine the related keywords and texts without actually understanding the meanings or relevance of the keywords. Here Hypergeometric distribution model is used which is a discrete probability distribution that describes the number of successes in a sequence of n keywords to be searched from a finite population without replacement.","score":0.4747,"junk":0.092,"openalex":"https://openalex.org/W2007210830"},{"id":"W6982382775","doi":null,"title":"Implementing semantic search for textual data in web applications","date":"2024-01-01","year":2024,"type":"report","authors":"Toh, Jeremy Gen Yang","authors_count":1,"venue":"DR-NTU (Nanyang Technological University)","publisher":"Nanyang Technological University","is_oa":0,"oa_status":"closed","license":null,"citations":0,"is_retracted":0,"url":"https://hdl.handle.net/10356/177123","pdf_url":null,"topic":"American Environmental and Regional History","field":"Environmental Science","keywords":"Semantic search | Sentence | Semantic similarity | Semantic Web | Semantic Web Stack | Semantic computing | Social Semantic Web | Web search query","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"Semantic search, also known as vector search, retrieves data based on their semantic similarity. It is enabled by sentence embeddings, which are high-dimension vectors that encapsulate the semantic meaning of sentences. Compared to traditional keyword search, semantic search accounts for the true intent of user queries which keyword search struggles to capture. \\n \\nThis paper explores the implementation of semantic search in web applications by processing sentences using Sentence-BERT (SBERT), which is a pre-trained deep learning language model for generating meaningful, high-dimension vectors called sentence embeddings. These embeddings are then stored in PostgreSQL database with an extension, vector, which enables efficient similarity comparisons during search queries. \\n \\nThis work details the findings from developing a vector search system, integrating it with a web application, and deploying it online.","score":0.4756,"junk":0.096,"openalex":"https://openalex.org/W6982382775"},{"id":"W4392385285","doi":"10.18280/ria.380125","title":"Building a Semantic Knowledge Graph Search Model for Finding a Causal Answer","date":"2024-02-29","year":2024,"type":"article","authors":"Gulnara Bektemyssova | Aidos Sabdenov","authors_count":2,"venue":"Revue d intelligence artificielle","publisher":"International Information and Engineering Technology Association","is_oa":1,"oa_status":"hybrid","license":"cc-by","citations":5,"is_retracted":0,"url":"https://doi.org/10.18280/ria.380125","pdf_url":"https://www.iieta.org/download/file/fid/121764","topic":"Semantic Web and Ontologies","field":"Computer Science","keywords":"Computer science | Semantic search | Graph | Knowledge graph | Artificial intelligence | Natural language processing | Information retrieval | Theoretical computer science | Semantic Web","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"This research focuses on developing effective algorithms for semantic knowledge graph searches in the context of finding causal answers, which is highly relevant due to the widespread use of semantic networks.The study's primary goal is to examine the construction principles of a semantic knowledge graph search model tailored for causal answers, with a focus on deep neural semantic search characteristics.The research methodology combines system analysis methods for building knowledge graphs with an analytical investigation of deep neural semantic search aspects.The results provide insights into the construction of a semantic knowledge graph search model for causal answers, addressing the challenges and methodologies involved in building such a model.It underscores the significance of knowledge graphs in modern information systems and their potential applications in various domains.However, further scientific research is essential to explore the practical applications of deep neural semantic search knowledge graphs in various information systems, which are used across different aspects of everyday life.The practical significance of this research extends to various applications in information retrieval, knowledge management, and problem-solving, making it a valuable contribution to the advancement of technology and understanding of natural language text.","score":0.4476,"junk":0.003,"openalex":"https://openalex.org/W4392385285"},{"id":"W1989237035","doi":"10.1109/icsmc.2011.6084089","title":"Calculating the strength of ties of a social network in a semantic search system using hidden Markov models","date":"2011-10-01","year":2011,"type":"conference-paper","authors":"Shimaa M. El-Sherif | Armin Eberlein | Behrouz H. Far","authors_count":3,"venue":"2011 IEEE International Conference on Systems, Man, and Cybernetics","publisher":null,"is_oa":0,"oa_status":"closed","license":null,"citations":9,"is_retracted":0,"url":"https://doi.org/10.1109/icsmc.2011.6084089","pdf_url":null,"topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","keywords":"Computer science | Semantic search | Social Semantic Web | Scalability | Semantic Web | Information retrieval | Semantic analytics | Semantics (computer science) | Semantic technology | Semantic computing | World Wide Web | Semantic Web Stack | Semantic grid | Software agent | Artificial intelligence | Database","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"The Web of information has grown to millions of independently evolved decentralized information repositories. Decentralization of the web has advantages such as no single point of failure and improved scalability. Decentralization introduces challenges such as ontological, communication and negotiation complexity. This has given rise to research to enhance the infrastructure of the Web by adding semantic to the search systems. In this research we view semantic search as an enabling technique for the general Knowledge Management (KM) solutions. We argue that, semantic integration, semantic search and agent technology are fundamental components of an efficient KM solution. This research aims to deliver a proof-of-concept for semantic search. A prototype agent-based semantic search system supported by ontological concept learning and contents annotation is developed. In this prototype, software agents, deploy ontologies to organize contents in their corresponding repositories; improve their own search capability by finding relevant peers and learn new concepts from each other; conduct search on behalf of and deliver customized results to the users; and encapsulate complexity of search and concept learning process from the users. A unique feature of this system is that the semantic search agents form a social network. We use Hidden Markov Model (HMM) to calculate the tie strengths between agents and their corresponding ontologies. The query will be forwarded to those agents with stronger ties and relevant documents are returned. We have shown that this will improve the search quality. In this paper, we illustrate the factors that affect the strength of the ties and how these factors can be used by HMM to calculate the overall tie strength.","score":0.4478,"junk":0.004,"openalex":"https://openalex.org/W1989237035"},{"id":"W2119874708","doi":"10.1145/2611040.2611057","title":"Web Search Results Visualization","date":"2014-05-27","year":2014,"type":"conference-paper","authors":"Kalliopi Kontiza | Antonis Bikakis","authors_count":2,"venue":"Proceedings of the 4th International Conference on Web Intelligence, Mining and Semantics (WIMS14)","publisher":null,"is_oa":0,"oa_status":"closed","license":null,"citations":2,"is_retracted":0,"url":"https://doi.org/10.1145/2611040.2611057","pdf_url":null,"topic":"Data Visualization and Analytics","field":"Computer Science","keywords":"Semantic search | Computer science | Visualization | Information retrieval | Search engine | Semantics (computer science) | Semantic Web | Context (archaeology) | Data visualization | World Wide Web | Information visualization | Data mining","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"Semantic search engines improve the accuracy of search results by understanding the meaning and context of terms as they appear in web documents. But do they also improve the presentation of search results? We discuss this research question and attempt to address it by evaluating two semantic search engines: Sig.ma and Kngine. Our analysis is based on the theory and methods of explorative evaluation studies, the inspection evaluation approach in information visualization, user-oriented evaluation studies, and on quantitative data analysis. Our main conclusion is that visualization used in semantic search engines improves the understanding of search results and the overall search experience by exploiting the semantics of web data.","score":0.4483,"junk":0.006,"openalex":"https://openalex.org/W2119874708"},{"id":"W1975878340","doi":"10.1504/ijaose.2007.014408","title":"Semantic search technology for information retrieval on the web","date":"2007-01-01","year":2007,"type":"article","authors":"Joan Lu | Umair Rahman","authors_count":2,"venue":"International Journal of Agent-Oriented Software Engineering","publisher":"Inderscience Publishers","is_oa":0,"oa_status":"closed","license":null,"citations":3,"is_retracted":0,"url":"https://doi.org/10.1504/ijaose.2007.014408","pdf_url":null,"topic":"Semantic Web and Ontologies","field":"Computer Science","keywords":"Computer science | Semantic Web | Information retrieval | Ontology | Social Semantic Web | RDF | Semantic Web Stack | World Wide Web | Semantic search | OWL-S | Data Web | Vocabulary | Web page","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"The fundamental issue of imprecise search results arises owing to the misrepresentation of data on the web. Although a large number of theoretical models have been reported in literature, their implementation in the real world is still at an initial stage. This research is a systematic investigation on the underlying basis of ontologies, intelligent agents and semantic mechanisms. A solution is to put into action, the envisioned, 'Semantic web', by annotating/manipulating web pages using XML-based technologies, creating ontology for providing an RDF vocabulary in a hierarchical class structure and developing an intelligent agent, which would be capable of database access, comparison and inference. The system designed here is to enable a machine to 'understand' the information it holds, which in turn would greatly facilitate smart searching. This paper presents that the system has been successfully implemented and achieves accurate search results.","score":0.4461,"junk":0.001,"openalex":"https://openalex.org/W1975878340","versions":[{"id":"W7024472675","type":"article","year":2007,"doi":null,"venue":"University of Huddersfield Repository (University of Huddersfield)","canonical":false}],"versions_count":2},{"id":"W2240739043","doi":null,"title":"Information retrieval in documents using semantic criteria","date":"1998-01-01","year":1998,"type":"conference-paper","authors":"Bj Mills | S. Venkatesh | Manoj Kumar | Lakshmi S. Narasimhan","authors_count":4,"venue":"Deakin Research Online (Deakin University)","publisher":"Deakin University","is_oa":0,"oa_status":"closed","license":null,"citations":0,"is_retracted":0,"url":"http://sydney.edu.au/engineering/it/research/tr/tr518.pdf","pdf_url":"http://sydney.edu.au/engineering/it/research/tr/tr518.pdf","topic":"Semantic Web and Ontologies","field":"Computer Science","keywords":"Information retrieval | Computer science | Relevance (law) | Concept search | Explicit semantic analysis | Document retrieval | Human–computer information retrieval | Semantic search | Search engine | Cognitive models of information retrieval | Semantics (computer science) | Artificial intelligence | Natural language processing | Semantic computing | Semantic Web | Semantic technology | Web search query","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"\\n\\t\\t\\t\\t\\tThe central problem of automatic retrieval from unformatted text is that computational devices are not adequately trained to look for associated information. However for complete understanding and information retrieval, a complete artificial intelligence would have to be built. This paper describes a method for achieving significant information retrieval by using a semantic search engine. The underlying semantic information is stored in a network of clarified words, linked by logical connections. We employ simple scoring techniques on collections of paths in this network to establish a degree of relevance between a document and a clarified search criterion. This technique has been applied with success to test examples and can be easily scaled up to search large documents.\\n\\t\\t\\t\\t","score":0.4536,"junk":0.028,"openalex":"https://openalex.org/W2240739043"},{"id":"W2148411874","doi":"10.1109/icis.2007.135","title":"Model of a Semantic Web Search Engine for Multimedia Content Retrieval","date":"2007-01-01","year":2007,"type":"conference-paper","authors":"Mohan Ponnada | Nalin K. Sharda","authors_count":2,"venue":"6th IEEE/ACIS International Conference on Computer and Information Science (ICIS 2007)","publisher":null,"is_oa":0,"oa_status":"closed","license":null,"citations":14,"is_retracted":0,"url":"https://doi.org/10.1109/icis.2007.135","pdf_url":null,"topic":"Web Data Mining and Analysis","field":"Computer Science","keywords":"Computer science | Information retrieval | World Wide Web | Search engine indexing | Semantic Web Stack | Metadata | Web search engine | Semantic Web | Semantic search | Search engine | Multimedia | Web search query","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"This paper presents the conceptual model of a semantic search engine that can locate required information on the web. This search engine makes use of semantics, or the meaning of queries, to retrieve relevant results. This model, called SCORDRA, is divided into four layers, namely semantic web, data, application and user interface layers. The SCORDRA engine makes use of the sharable content object reference model (SCORM) to bring together relevant data stored in the form of sharable content objects (SCOs). The Data Layer consists of data stored as SCO's. These SCO's are built on the SCORM standards, and their location is resolved by the content object repository discovery and registration/resolution architecture (CORDRA). SCORM defines how information is referenced and used, by packaging data with metadata. CORDRA aims to locate and reference SCORM repositories over the web. The application layer provides the reasoning platform of the system, it makes use of SCOs discovered by CORDRA by crawling and indexing the SCO's on the Web. The user interface layer allows the user to enter queries and view these as multimedia presentations. This semantic web search engine will allow the user to enter a query, and deliver a multimedia presentation that answers the query.","score":0.446,"junk":0.003,"openalex":"https://openalex.org/W2148411874"},{"id":"W7096616866","doi":null,"title":"ANALYSIS OF SEMANTIC SEARCH ENGINES","date":"2015-11-04","year":2015,"type":"article","authors":"","authors_count":0,"venue":"http://cirworld.org/journals/index.php/ijnpc/article/viewFile/IJN239/pdf_11/","publisher":null,"is_oa":0,"oa_status":"closed","license":null,"citations":0,"is_retracted":0,"url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.687.564","pdf_url":null,"topic":"Cognitive Computing and Networks","field":"Computer Science","keywords":"Semantic search | Social Semantic Web | Semantic Web | Semantic Web Stack | Semantics (computer science) | Semantic computing | Search engine | Data Web","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"The traditional information retrieval system don not formally capture the explicit meaning of a user queries. The superior measurement of traditional search it offers a lot of characteristics for web user, where they can use it as information access system. However paradigm of information web to intelligent web. Where users necessitate is enlarging rapidly. Current web user is finding more intelligent information on web with their intended meaning. Semantic search engine is most appropriate infrastructure in that instance. It assures to make environment where users are able to retrieving knowledge oriented information. It promises to make exact answers to user queries by taking advantage of the availability of explicit semantics of information in the context of the semantic web. The key objective to write this is highlights the significance of semantic technology and their attributes.","score":0.4664,"junk":0.072,"openalex":"https://openalex.org/W7096616866"},{"id":"W1483138003","doi":null,"title":"The quest for information retrieval on the semantic web","date":"2005-12-01","year":2005,"type":"article","authors":"David Vallet-Weadon | Miriam Fernández-Sánchez | Pablo Castells-Azpilicueta","authors_count":3,"venue":"Open Research Online (The Open University)","publisher":"The Open University","is_oa":0,"oa_status":"closed","license":null,"citations":8,"is_retracted":0,"url":"https://oro.open.ac.uk/28601/1/upgrade05.pdf","pdf_url":"https://oro.open.ac.uk/28601/1/upgrade05.pdf","topic":"Semantic Web and Ontologies","field":"Computer Science","keywords":"Information retrieval | Computer science | Semantic search | Ranking (information retrieval) | Vector space model | Ontology | Adaptation (eye) | Explicit semantic analysis | Semantic Web | Weighting | Semantic Web Stack | Search engine | Concept search | Web search query | Semantic technology","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"Semantic search has been one of the motivations of the Semantic Web since it was envisioned. We propose a model for the exploitation of ontology-based KBs to improve search over large document repositories. The retrieval model is based on an adaptation of the classic vector-space model, including an annotation weighting algorithm, and a ranking algorithm. Semantic search is combined with keyword-based search to achieve tolerance to KB incompleteness. Our proposal has been tested on corpora of significant size, showing promising results with respect to keyword-based search, and providing ground for further analysis and research.","score":0.4451,"junk":0.002,"openalex":"https://openalex.org/W1483138003"},{"id":"W2745205465","doi":"10.1109/cse-euc.2017.177","title":"Chinese-Uyghur-English Semantic Search Based on the Knowledge Graphs","date":"2017-07-01","year":2017,"type":"conference-paper","authors":"Lirong Qiu | Na Yang | Maierdan Maolimamuti","authors_count":3,"venue":"22017 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC)","publisher":null,"is_oa":0,"oa_status":"closed","license":null,"citations":2,"is_retracted":0,"url":"https://doi.org/10.1109/cse-euc.2017.177","pdf_url":null,"topic":"Natural Language Processing Techniques","field":"Computer Science","keywords":"Computer science | Semantic search | Information retrieval | Knowledge graph | Semantic similarity | Semantic Web Stack | Semantic Web | Social Semantic Web | Graph | Natural language processing | Artificial intelligence | Theoretical computer science","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"Semantic search is becoming a more independent search engine, which is capable of analyzing independently, understanding the essential needs and providing accurate answers. In this work, the basis of analyzing the content framework puts forward the theory on the basis of knowledge graph. In the semantic web, search engines use a knowledge graph to identify the query involved in the entity and its attributes, giving a precise answer by efficient web search. Based on the semantic web, we proposed the K-means algorithm that applied to construct knowledge graph, will be sorted in knowledge bases by classifying similarity word, building Uygur's knowledge graph architecture, and then, take the built knowledge graph to show the semantic searching.","score":0.4488,"junk":0.015,"openalex":"https://openalex.org/W2745205465"},{"id":"W2012271809","doi":"10.1109/icicee.2012.313","title":"An Intelligent Search Model and an Improved Matching Algorithm","date":"2012-08-01","year":2012,"type":"conference-paper","authors":"Shi Qing | Gong Jun-peng","authors_count":2,"venue":"2012 International Conference on Industrial Control and Electronics Engineering","publisher":null,"is_oa":0,"oa_status":"closed","license":null,"citations":0,"is_retracted":0,"url":"https://doi.org/10.1109/icicee.2012.313","pdf_url":null,"topic":"Service-Oriented Architecture and Web Services","field":"Computer Science","keywords":"Computer science | Semantic similarity | Semantic search | Semantic Web | Social Semantic Web | Information retrieval | Ontology | Semantic Web Stack | Scope (computer science) | Semantic grid | Semantic computing | Semantic matching | Search engine | Matching (statistics) | Semantic technology | OWL-S | Semantic analytics | Blossom algorithm | Data mining","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"With development of semantic web technology, the intelligent and semantic exploration is becoming an important discussion to the research of search engine. This paper proposes a model of semantic search engine based on analysis of Semantic Web and ontology, and considering inheritance relationship between nodes, improves the concept of semantic distance and similarity matching algorithm. The results show that these optimizations improve the accuracy and expand the scope of application.","score":0.4466,"junk":0.009,"openalex":"https://openalex.org/W2012271809"},{"id":"W6926443556","doi":"10.23683/2311-3103-2017-7-122-132","title":"SEMANTIC SEARCH WITH THE USE OF GENETIC OPERATORS","date":"2017-12-04","year":2017,"type":"article","authors":"Kravchenko, D. Yu. | Starkova, Yu. S. | Novikov, A. A. | Markov, V. V.","authors_count":4,"venue":"Известия Южного федерального университета. Технические науки","publisher":null,"is_oa":1,"oa_status":"bronze","license":null,"citations":0,"is_retracted":0,"url":"https://doi.org/10.23683/2311-3103-2017-7-122-132","pdf_url":null,"topic":"Maternal and Perinatal Health Interventions","field":"Medicine","keywords":"Semantics (computer science) | Semantic search | Search engine indexing | Semantic similarity | Meaning (existential) | Subject (documents) | Semantic computing","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"Traditional search mechanisms are based on keyword search, which does not take into ac-count the semantic links between different concepts. This leads to the loss of relevant documents due to inaccurate wording of the query or the use in the query of related words and concepts. To solve the problems of formulating user requests and interdisciplinarity of concepts, it is sug-gested to use the semantic search. The approach proposed in the article for the implementation of semantic search is applicable to large volumes of text data and is based on the use of a genetic algorithm. Unlike standard approaches to information retrieval, the described approach allows taking into account the semantics of interrelations between concepts, as well as correctly handle interdisciplinary concepts. Thanks to semantic indexing, documents define concepts not presented in the user\"s initial query, but semantically close to the concepts from the query. Semantic indexing is performed for each document separately, which allows parallel indexation on several subject areas. By the time of completion of the formation of the ontological profile of the document in question, all semantic distances between pairs of distinguished concepts are calculated. Concepts are considered close in meaning if their semantic proximity value is above a certain threshold value that is specified in the search parameters. Building an ontological document profile is a multicriteria task, since it depends on a lot of characteristics, so genetic algorithms can be used to solve it. The developed genetic algorithm is intended for more accurate distribution of weight coefficients and estimation of semantic proximity of concepts.","score":0.4534,"junk":0.031,"openalex":"https://openalex.org/W6926443556"},{"id":"W7098182697","doi":null,"title":"The Quest for Information Retrieval on the Semantic Web","date":"2008-04-02","year":2008,"type":"article","authors":"David Vallet | Miriam Fernández | Pablo Castells","authors_count":3,"venue":"http://www.acemedia.org/aceMedia/files/document/wp7/2005/upgrade05-uam.pdf","publisher":null,"is_oa":0,"oa_status":"closed","license":null,"citations":0,"is_retracted":0,"url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.101.3750","pdf_url":null,"topic":"Plant Ecology and Taxonomy Studies","field":"Agricultural and Biological Sciences","keywords":"Semantic search | Ranking (information retrieval) | Semantic Web | Concept search | Adaptation (eye) | Annotation | Semantic Web Stack | Search engine | Document retrieval","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"Abstract. Semantic search has been one of the motivations of the Semantic Web since it was envisioned. We propose a model for the exploitation of ontology-based KBs to improve search over large document repositories. The retrieval model is based on an adaptation of the classic vector-space model, including an annotation weighting algorithm, and a ranking algorithm. Semantic search is combined with keyword-based search to achieve tolerance to KB incompleteness. Our proposal has been tested on corpora of significant size, showing promising results with respect to keyword-based search, and providing ground for further analysis and research.","score":0.4469,"junk":0.01,"openalex":"https://openalex.org/W7098182697","versions":[{"id":"W7100792804","type":"article","year":2008,"doi":null,"venue":"http://www.l3s.de/kweb/kwepsy2006/FinalSubmissions/kwepsy2006_fernandez.pdf","canonical":false}],"versions_count":2},{"id":"W2087721914","doi":"10.1109/icica.2014.82","title":"Ontology Based Semantic Search: An Introduction and a Survey of Current Approaches","date":"2014-03-01","year":2014,"type":"conference-paper","authors":"A. Ramkumar | B. Poorna","authors_count":2,"venue":"2014 International Conference on Intelligent Computing Applications","publisher":null,"is_oa":0,"oa_status":"closed","license":null,"citations":16,"is_retracted":0,"url":"https://doi.org/10.1109/icica.2014.82","pdf_url":null,"topic":"Semantic Web and Ontologies","field":"Computer Science","keywords":"Computer science | Information retrieval | Ontology | Semantic search | Upper ontology | Conceptualization | Ontology-based data integration | OWL-S | Process ontology | Semantic Web | Ontology Inference Layer | World Wide Web | Semantic Web Stack | Artificial intelligence","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"Ontology based semantic search will lead to new generation of search based on the meaning of keyword rather than keyword and helps in finding correct information on the web. Here, ontology provides an explicit specification of conceptualization which helps to connect the information on the existing web pages with the background knowledge. Ontology based search overcomes the semantic gap between the keyword found in documents and those in query. This survey provides an introduction to ontology based semantic search and review the different details of selected ontology based search approaches and compare them by means of classification criteria. Based on this comparison, this survey attempts to identify the possible directions for future research.","score":0.4452,"junk":0.004,"openalex":"https://openalex.org/W2087721914"},{"id":"W1991769089","doi":"10.15439/2014f343","title":"Semantic sentence structure search engine","date":"2014-09-29","year":2014,"type":"conference-paper","authors":"Nikita Gerasimov | Maxim Mozgovoy | Alexey Lagunov","authors_count":3,"venue":"Annals of Computer Science and Information Systems","publisher":"Polskie Towarzystwo Informatyczne","is_oa":1,"oa_status":"diamond","license":"cc-by","citations":0,"is_retracted":0,"url":"https://doi.org/10.15439/2014f343","pdf_url":null,"topic":"Web Data Mining and Analysis","field":"Computer Science","keywords":"Computer science | Sentence | Natural language processing | Information retrieval | Artificial intelligence | Semantic search | Search engine","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"Many of current web search engines rely on inverted index-based data structures as document information store. Since and inverted index is a map from individual document words to their respective locations, such data structure destructs semantic links between the words, and thus does not support structural user queries. In other words, such systems can only find the documents that contain user-specified words. In this paper we propose to create semantic links between the terms contained in inverted index, and in such way create a semantic network. This network will preserve the internal structure of the stored documents, and will enable the users to perform structural queries. Both structural-saving indexation and structural user search query allow to save semantic speech meaning of the text while search process.","score":0.4437,"junk":0.001,"openalex":"https://openalex.org/W1991769089"},{"id":"W7050467923","doi":null,"title":"Concept Search","date":"2008-05-01","year":2008,"type":"article","authors":"Giunchiglia, Fausto | Kharkevich, Uladzimir | Zaihrayeu, Ilya","authors_count":3,"venue":"Unitn Eprints Research (Università Degli Studi di Trento)","publisher":"University of Trento","is_oa":0,"oa_status":"closed","license":null,"citations":0,"is_retracted":0,"url":"http://eprints.biblio.unitn.it/1434/1/037.pdf","pdf_url":"http://eprints.biblio.unitn.it/1434/1/037.pdf","topic":"Magnetic confinement fusion research","field":"Physics and Astronomy","keywords":"Semantic search | Exploit | Key (lock) | Semantic similarity | String (physics) | Beam search | Search algorithm | Computation | Function (biology)","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"In this paper we present a novel approach, called Concept Search, which extends syntactic search, i.e., search based on the computation of string similarity between words, with semantic search, i.e., search based on the computation of semantic relations between concepts. The key idea of Concept Search is to operate on complex concepts and to maximally exploit the semantic information available, reducing to syntactic search only when necessary, i.e., when no semantic information is available. The experimental results show that Concept Search performs at least as well as syntactic search, improving the quality of results as a function of the amount of available semantics.","score":0.4445,"junk":0.004,"openalex":"https://openalex.org/W7050467923"},{"id":"W635749513","doi":null,"title":"Ontology-Driven Query Reformulation in Semantic Search","date":"2007-01-01","year":2007,"type":"dissertation","authors":"Geir Solskinnsbakk","authors_count":1,"venue":"BIBSYS Brage (BIBSYS (Norway))","publisher":"Vilnius University","is_oa":1,"oa_status":"green","license":"other-oa","citations":9,"is_retracted":0,"url":"http://hdl.handle.net/11250/250524","pdf_url":null,"topic":"Semantic Web and Ontologies","field":"Computer Science","keywords":"Computer science | Information retrieval | Web search query | Semantic search | Query expansion | Ontology | Web query classification | Query language | Semantic query | Search engine","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"Semantic search is a research area in which the goal is to understand the users intended meaning of the query. This requires disambiguation of the user query and interpreting the semantics of the query. Semantic search would thus improve the users search experience through more precise result sets. Moreover, ontologies are explicit conceptualizations of domains, defining concepts, their properties, and the relations among them. This makes ontologies semantic representations of specific domains, suitable to use as a basis for semantic search applications. In this thesis we explore how such a semantic search system based on ontologies may be constructed. The system is built as a query reformulation module that uses an underlying search engine based on Lucene. We employ text mining techniques to semantically enrich an ontology by building feature vectors for the concepts of the ontology. The feature vectors are tailored to a specific document collection and domain, reflecting the vocabulary in the document collection and the domain. We propose four query reformulation strategies for evaluation. The interpretation and expansion of the user query is based on the ontology and the feature vectors. Finally the reformulated query is fired as a weighted query into the Lucene search engine. The evaluation of the implemented prototype reveals that search is in general improved by our reformulation approaches. It is however difficult to give any definite conclusion to which query types benefit the most from our approach, and which reformulation strategy improves the search result the most. All four of the reformulation strategies seem to on average perform quite equally.","score":0.4434,"junk":0.001,"openalex":"https://openalex.org/W635749513"},{"id":"W2024971720","doi":"10.5121/ijwest.2010.1401","title":"Semantic Query Optimisation with Ontology Simulation","date":"2010-10-30","year":2010,"type":"article","authors":"Siddharth Gupta | Narina Thakur","authors_count":2,"venue":"International journal of Web & Semantic Technology","publisher":null,"is_oa":1,"oa_status":"bronze","license":null,"citations":0,"is_retracted":0,"url":"https://doi.org/10.5121/ijwest.2010.1401","pdf_url":"https://doi.org/10.5121/ijwest.2010.1401","topic":"Semantic Web and Ontologies","field":"Computer Science","keywords":"Computer science | Social Semantic Web | Semantic Web | Semantic Web Stack | Data Web | Semantic analytics | Information retrieval | World Wide Web | Ontology | OWL-S | Semantic computing | Cliché | Web service","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"Semantic Web is, without a doubt, gaining momentum in both industry and academia.The word \"Semantic\" refers to \"meaning\" -a semantic web is a web of meaning.In this fast changing and result oriented practical world, gone are the days where an individual had to struggle for finding information on the Internet where knowledge management was the major issue.The semantic web has a vision of linking, integrating and analysing data from various data sources and forming a new information stream, hence a web of databases connected with each other and machines interacting with other machines to yield results which are user oriented and accurate.With the emergence of Semantic Web framework the naïve approach of searching information on the syntactic web is cliché.This paper proposes an optimised semantic searching of keywords exemplified by simulation an ontology of Indian universities with a proposed algorithm which ramifies the effective semantic retrieval of information which is easy to access and time saving.","score":0.4437,"junk":0.002,"openalex":"https://openalex.org/W2024971720"},{"id":"W7098003244","doi":null,"title":"From Web search to Semantic Web search","date":"2008-01-01","year":2008,"type":"article","authors":"Bettina Fazzinga | Giorgio Gianforme | Georg Gottlob | Thomas Lukasiewicz","authors_count":4,"venue":"http://www.kr.tuwien.ac.at/research/reports/rr0811.pdf","publisher":null,"is_oa":0,"oa_status":"closed","license":null,"citations":0,"is_retracted":0,"url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.219.1913","pdf_url":null,"topic":"Semantic Web and Ontologies","field":"Computer Science","keywords":"Social Semantic Web | Semantic Web Stack | Semantic search | Semantic Web | Web search query | Data Web | Web modeling | Web standards","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"Many experts predict that the next huge step forward in Web information technology will be achieved by adding semantics to Web data, and will possibly consist of (some form of) the Semantic Web. In this paper, we present an approach to Semantic Web search, which combines standard Web search with ontological background knowledge. In fact, we show how standard Web search engines can be used as the main inference motor for ontology-based search. To make this possible, lightweight software clients are used for annotation and query decomposition. We develop the formal model behind this approach and also provide an implementation in desktop search. Experiments show that the implementation","score":0.4469,"junk":0.014,"openalex":"https://openalex.org/W7098003244"},{"id":"W2625399941","doi":"10.5120/ijca2015906398","title":"The Use of Ontology in Semantic Search Techniques","date":"2015-10-15","year":2015,"type":"article","authors":"Prerna Parmeshwaran | Juilee Rege | Sindhu Nair","authors_count":3,"venue":"International Journal of Computer Applications","publisher":"Foundation of Computer Science","is_oa":1,"oa_status":"bronze","license":null,"citations":3,"is_retracted":0,"url":"https://doi.org/10.5120/ijca2015906398","pdf_url":"https://doi.org/10.5120/ijca2015906398","topic":"Semantic Web and Ontologies","field":"Computer Science","keywords":"Computer science | Semantic search | Information retrieval | Semantic Web | Ontology | World Wide Web | Semantic Web Stack | Search engine | Meaning (existential) | OWL-S | Hypertext | Social Semantic Web | Simple (philosophy) | Web search engine | Web search query","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"The World Wide Web has grown over the years from simple hypertext documents to highly interactive pages, where users can also contribute to the content by posting comments and so on.However, most data is extremely unstructured and cannot be easily automatically processed by machines.Presently, most search engines are keyword based and searches may also result in irrelevant results due to the mere presence of matching keywords.To eradicate this problem, the concept of semantic web has been introduced in which the data follows a uniform standard.Everything present in the document has a specific meaning attached to it.Such standardized documents can easily be understood by machines.Due to the concept of semantic web, search engines can be made to understand the meaning of the query and thus the most relevant links can be retrieved.To implement semantic web technologies, the concept of ontology is used.In this paper, an attempt is made to explore how semantic web and ontology are being used to implement efficient search engines.","score":0.4468,"junk":0.014,"openalex":"https://openalex.org/W2625399941"},{"id":"W2162427312","doi":"10.1109/icmlc.2005.1527258","title":"Toward a semantic search engine based on ontologies","date":"2005-01-01","year":2005,"type":"conference-paper","authors":"Weidong Fang | Ling Zhang | Yan-Xuan Wang | Shoubin Dong","authors_count":4,"venue":"2005 International Conference on Machine Learning and Cybernetics","publisher":null,"is_oa":0,"oa_status":"closed","license":null,"citations":37,"is_retracted":0,"url":"https://doi.org/10.1109/icmlc.2005.1527258","pdf_url":null,"topic":"Semantic Web and Ontologies","field":"Computer Science","keywords":"Information retrieval | Computer science | Web search query | Semantic search | Search engine | Relevance (law) | RDF | Search-oriented architecture | Web query classification | Query expansion | Metasearch engine | Concept search | Semantic query | Domain (mathematical analysis) | Parsing | Semantic Web | Natural language processing","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"Semantic search requires a search engine to properly interpret the meaning of a user's query and the inherent relations among the terms that a document contains with respect to a specific domain. We present the framework of such a search engine based on domain ontologies. In this framework, a search request, which can be either a keyword list as in traditional search methods or a query in complex form containing various restrictions to the search, is first processed by a query parser which then finds qualified RDF triples in domain ontologies. Web documents relevant to the requested concepts and individuals specified in these triples are then retrieved by a document retriever. And finally, the retrieved documents are ranked according to their relevance to the user's query. An extended term-document matrix is built to reflect the relevance between documents, concepts/individuals, and terms. Such a matrix makes it possible for the search engine to work even in case that there are no available domain ontologies for user requests.","score":0.4447,"junk":0.007,"openalex":"https://openalex.org/W2162427312"},{"id":"W1983709304","doi":"10.2991/iccnce.2013.92","title":"Keywords Semantic Extension in Semantic Search Model","date":"2013-01-01","year":2013,"type":"conference-paper","authors":"Xuejun Yu | Jing Lv","authors_count":2,"venue":"Proceedings of the International Conference on Computer, Networks and Communication Engineering (ICCNCE 2013)","publisher":null,"is_oa":1,"oa_status":"gold","license":"cc-by-nc","citations":0,"is_retracted":0,"url":"https://doi.org/10.2991/iccnce.2013.92","pdf_url":"https://www.atlantis-press.com/article/6548.pdf","topic":"Advanced Text Analysis Techniques","field":"Computer Science","keywords":"Computer science | Extension (predicate logic) | Semantic search | Semantic computing | Information retrieval | Semantic compression | Natural language processing | Semantic grid | Semantic similarity | Semantic technology | Artificial intelligence | Semantic Web | Programming language","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"The goal of semantic search is to abandon the search model that based on simple keyword matching, combined with semantic technologies, make full use of the characteristics of ontology-based semantic information relationship to provide users with more high-quality and efficient search results.The study of this paper focuses on the semantic extension of keywords in semantic search process, to consider the vocabulary expansion with different factors, thus providing a set of factors that affecting the expansion of the vocabulary and the reasonable weight for these factors that verified by experiment.","score":0.4493,"junk":0.024,"openalex":"https://openalex.org/W1983709304"},{"id":"W7099308154","doi":null,"title":"Categories and Subject Descriptors H.3.3 Information Search and Retrieval General Terms Experimentation","date":"2008-12-03","year":2008,"type":"article","authors":"R. Guha | Rob Mccool | Eric Miller","authors_count":3,"venue":"http://www.ischool.utexas.edu/~i385t-sw/readings/Guha_McCool-2003-Semantic_Search.pdf","publisher":null,"is_oa":0,"oa_status":"closed","license":null,"citations":0,"is_retracted":0,"url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.120.774","pdf_url":null,"topic":"Optimism, Hope, and Well-being","field":"Psychology","keywords":"Semantic search | Social Semantic Web | Semantic Web | Semantics (computer science) | Semantic Web Stack | Denotation (semiotics) | Data Web | Semantic computing","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"Activities such as Web Services and the Semantic Web are working to create a web of distributed machine understandable data. In this paper we present an application called Semantic Search which is built on these supporting technologies and is designed to improve traditional web searching. We provide an overview of TAP, the application framework upon which the Semantic Search is built. We describe two implemented Semantic Search systems which, based on the denotation of the search query, augment traditional search results with relevant data aggregated from distributed sources. We also discuss some general issues related to searching and the Semantic Web and outline how an understanding of the semantics of the search terms can be used to provide better results.","score":0.4521,"junk":0.034,"openalex":"https://openalex.org/W7099308154"},{"id":"W4393141096","doi":"10.1109/icnisc60562.2023.00033","title":"An Analysis of the Technical Trend of Semantic Search in Natural Language Processing","date":"2023-10-27","year":2023,"type":"conference-paper","authors":"Ying Zheng","authors_count":1,"venue":"2023 9th Annual International Conference on Network and Information Systems for Computers (ICNISC)","publisher":null,"is_oa":0,"oa_status":"closed","license":null,"citations":3,"is_retracted":0,"url":"http://dx.doi.org/10.1109/icnisc60562.2023.00033","pdf_url":null,"topic":"Technology and Data Analysis","field":"Computer Science","keywords":"Computer science | Natural language processing | Natural language | Semantic compression | Artificial intelligence | Information retrieval | Semantic computing | Semantic technology | Semantic Web","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"Semantic search is an important task in Natural language processing (NLP). It aims to improve the accuracy of search results by understanding the meaning of search queries. This paper introduces the basic concepts, development history, and application status of semantic search in different fields, and explains the current challenges of semantic search, such as polysemy and ambiguity problems, semantic understanding and semantic modeling.This paper mainly describes the main natural language processing technologies in semantic search, including deep learning-based methods, text representation, intent recognition, relationship extraction, and machine learning. Finally, this paper discusses the trends and prospects of semantic search development in the future.","score":0.4514,"junk":0.032,"openalex":"https://openalex.org/W4393141096"},{"id":"W6893740128","doi":"10.5281/zenodo.4536803","title":"Semantic Search Engine Optimisation (SSEO) for Dynamic Websites: A Review","date":"2021-02-12","year":2021,"type":"article","authors":"Sadeeq, M. J. | Zeebare, S. R. M.","authors_count":2,"venue":"RePEc: Research Papers in Economics","publisher":"Federal Reserve Bank of St. Louis","is_oa":1,"oa_status":"green","license":"cc-by","citations":8,"is_retracted":0,"url":"https://ijsab.com/volume-5-issue-3/3720","pdf_url":null,"topic":"Information Retrieval and Search Behavior","field":"Computer Science","keywords":"Semantic search | Search engine | Semantic Web | Social Semantic Web | Semantic computing | Meaning (existential) | Search analytics | Concept search | Perspective (graphical) | Semantic Web Stack","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"The billions of databases, worldwide, provide an increasing amount of information to the people. As a result, the researchers have to seek knowledge about the resources, which were generically known as the search engines. One such search technique that is popularly used is the semantic search technique which improves the search accuracy by determining the purpose of the search along with the contextual meaning of the terms which appeared in the data space or the web for generating accurate results. Many search engines exist today, which makes it difficult to collect useful data. In this paper, many types of research depended which prepared to describe the Semantic Search and Semantic Web techniques. Various types of semantic search engines are investigated and the differences between the Semantic Search and Semantic Search keywords are determined. Additionally, the benefits of using Semantic Search were highlighted. The literature review and the findings of the case study helped in understanding the new constructs. These researches also determined the relationship between the new and the previously existing constructs based on their perspective regarding the extension of Bedny's activity theory with regards to the SEO promotion techniques. They added the functional consequences of extending Bedny's activity theory faced by the promotion managers. The researchers have summarised the history of semantic search and its global position in search engine generation. The researchers also highlighted the role played by the search engines in the semantic search and smart web technologies.","score":0.4433,"junk":0.006,"openalex":"https://openalex.org/W6893740128","versions":[{"id":"W6968566710","type":"article","year":2021,"doi":"10.5281/zenodo.4536804","venue":"Zenodo (CERN European Organization for Nuclear Research)","canonical":false}],"versions_count":2},{"id":"W4386489521","doi":"10.1109/picst57299.2022.10238538","title":"Methods of Semantic Structured Search","date":"2022-10-10","year":2022,"type":"conference-paper","authors":"Yurii Pohuliaiev | Kirill Smelyakov | Anastasiya Chupryna | Ігор Рубан","authors_count":4,"venue":"2022 IEEE 9th International Conference on Problems of Infocommunications, Science and Technology (PIC S&T)","publisher":null,"is_oa":0,"oa_status":"closed","license":null,"citations":0,"is_retracted":0,"url":"http://dx.doi.org/10.1109/picst57299.2022.10238538","pdf_url":null,"topic":"Information Systems and Technology Applications","field":"Business, Management and Accounting","keywords":"Computer science | Documentation | Semantic search | Meaning (existential) | Information retrieval | Scheme (mathematics) | Random search | Work (physics) | Search engine | Algorithm | Mathematics","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"In this paper, we consider a method of documentary search based on a weighted assessment of the results of semantic and structured search methods. The main advantages and disadvantages of both approaches were considered, and a mathematical model describing the operation of the search method was highlighted. Experiments to test the effectiveness of the developed model were also described, their progress, output and resulting metrics necessary for evaluating the results of the work were determined. To analyze the practical work of the proposed scheme, experiments were carried out on scientific documentation from different fields (mathematics, technology, biology, medicine, social sciences, etc.). The work of the system was studied both in the conditions of semantic proximity of the collection of documents, and in the case of a random distribution of the semantic meaning of the works. The results obtained during the experiments, on the one hand, confirmed the effectiveness of the implementation of the structured approach, and on the other hand, demonstrated a direct correlation between the search efficiency and the semantic diversity of the document collection.","score":0.4416,"junk":0.002,"openalex":"https://openalex.org/W4386489521"},{"id":"W2146553785","doi":"10.1109/aiccsa.2010.5587013","title":"An overview of search methodologies in Semantic Web","date":"2010-05-01","year":2010,"type":"conference-paper","authors":"Thabet Slimani | Boutheina Ben Yaghlane","authors_count":2,"venue":"ACS/IEEE International Conference on Computer Systems and Applications - AICCSA 2010","publisher":null,"is_oa":0,"oa_status":"closed","license":null,"citations":3,"is_retracted":0,"url":"https://doi.org/10.1109/aiccsa.2010.5587013","pdf_url":null,"topic":"Semantic Web and Ontologies","field":"Computer Science","keywords":"Semantic search | Computer science | Information retrieval | Semantic Web | Field (mathematics) | Social Semantic Web | Meaning (existential) | Semantic computing | Semantic Web Stack | World Wide Web | Search engine | Data science","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"Several researches in Semantic Web are based on information search centered meaning. The common purpose of these researches is to improve current information search and retrieval methods. The last few years have seen a various number of developed semantic search systems. The requirement of a complete survey in this field is one of the main purpose of this paper. The approaches, methodologies and objectives of some recognized projects and their corresponding practical systems are exploited to constitute this overall view of semantic search. In this paper, we present and compare various research directions in semantic search. Further, we give discussion with regards to future research in this area.","score":0.4415,"junk":0.002,"openalex":"https://openalex.org/W2146553785"},{"id":"W2266093376","doi":"10.5815/ijmsc.2016.01.01","title":"Use of the Ontological Model for Personification of the Semantic Search","date":"2016-01-08","year":2016,"type":"article","authors":"Juliia V. Rogushina","authors_count":1,"venue":"International Journal of Mathematical Sciences and Computing","publisher":null,"is_oa":1,"oa_status":"diamond","license":null,"citations":4,"is_retracted":0,"url":"https://doi.org/10.5815/ijmsc.2016.01.01","pdf_url":"http://www.mecs-press.org/ijmsc/ijmsc-v2-n1/IJMSC-V2-N1-1.pdf","topic":"Artificial Intelligence and Decision Support Systems","field":"Computer Science","keywords":"Computer science | Semantic search | Ontology | Information retrieval | Semantic Web | Realization (probability) | Knowledge base | Component (thermodynamics) | Knowledge representation and reasoning | Representation (politics) | Software | Semantic Web Stack | Semantic technology | World Wide Web | Artificial intelligence | Programming language","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"Semantic search is an important component of modern intelligent applications oriented on work in open information environment.The intelligence level of application depends of it's capabilities in knowledge processing and defines it's facilities.Now applications widely use ontologies for knowledge representation.Therefore criteria of intelligence level estimation (that can analyze ontologies) of applications and retrieval systems as their particular case are proposed.In this paper, an ontological model of the intelligent interaction of the main objects and subjects of the semantic search (the Web information resources, information objects and information consumers etc.) is developed.Software realization of semantic search on base of this ontological model and integration of this search instrument with applied systems are describes.","score":0.4416,"junk":0.003,"openalex":"https://openalex.org/W2266093376"},{"id":"W4288374391","doi":null,"title":"What kind of semantics and why to use it in patents: part 1 – Semantic search","date":"2019-04-09","year":2019,"type":"preprint","authors":"Marco Ciaramella | Alberto Ciaramella","authors_count":2,"venue":"HAL (Le Centre pour la Communication Scientifique Directe)","publisher":"Centre National de la Recherche Scientifique","is_oa":1,"oa_status":"green","license":"other-oa","citations":0,"is_retracted":0,"url":"https://hal.science/hal-02092622","pdf_url":null,"topic":"Private Equity and Venture Capital","field":"Business, Management and Accounting","keywords":"Semantics (computer science) | Computer science | Semantic search | Information retrieval | World Wide Web | Programming language | Semantic Web","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"The term semantics means to add meanings to text. The world \"sematic\" entered in patent information circles about ten years ago, as \"semantic search\", i.e. a new query paradigm, which starts searching from a prototype text. The interest about this topic, is increasing now, as demonstrated by new presentations on recent patent events, as CEPIUG 2018 and EPOPIC 2018. This topic still attracts some concerns, mostly due to the fact that semantic searches are perceived as black box solutions. This contribution will try to \"open the box\" of semantic searches, summarizing different features of semantic searches, which can be used for a first assessment of specific solutions. Moreover, from these features we can infer that semantic searches available today are very different together, and can be differentiated into two categories or generations. Finally we suggest that the preferred use today for semantic searches is to increase the recall after a \"traditional\" search.","score":0.4504,"junk":0.034,"openalex":"https://openalex.org/W4288374391"},{"id":"W4308915439","doi":"10.1142/s0219649222500824","title":"A Double Metan-Semantic Search Model Based on Ontology and Semantic Similarity: Asthma Disease","date":"2022-11-09","year":2022,"type":"article","authors":"Mourad Belabed | Abdeslem Dennai","authors_count":2,"venue":"Journal of Information & Knowledge Management","publisher":"World Scientific","is_oa":0,"oa_status":"closed","license":null,"citations":3,"is_retracted":0,"url":"https://doi.org/10.1142/s0219649222500824","pdf_url":null,"topic":"Semantic Web and Ontologies","field":"Computer Science","keywords":"Computer science | Semantic search | Information retrieval | WordNet | Semantic similarity | Ontology | Semantic Web Stack | Semantic analytics | Semantics (computer science) | Semantic computing | Semantic Web | World Wide Web","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"With the exponential and rapid growth of online resources in recent years, there has been a huge increase in the use of search engines; these are also one of the most common ways to navigate the Web content without taking into account, in general, the request meaning by which was successfully added the user’s webpage provides us with a lot of results. This problem has led to the integration of semantics in the search for information on the Web (Semantic Web). The use of semantic tools, such as ontology, WordNet dictionary, semantic similarity measure, etc., has contributed to the semantic search development and more particularly, semantic Metan-search. The success of semantic search is closely linked to the availability of domain ontologies. The objective of this paper is to propose a double model of repetitive semantic search, called Double Metan-Semantic Search Model (2[Formula: see text]-SSM). On the one hand, it is assisted and based on the concepts extracted from the user’s search domain ontology, which will permit the user to choose a concept from this list of concepts and launch their search; on the other hand, it is free, in that the user enters their own concept and launches their search. This is based on WordNet tool, user’s same search domain ontology and the semantic similarity calculation techniques between concepts in the same ontology. The result of this model is a set of URL links. The term Metan indicates that the search is done in depth ([Formula: see text]-SS) via choosing each time a URL result by the user. Its experimentation in the asthma disease field gave very promising results in quantity and quality of information via the URL link results (semantic support).","score":0.4409,"junk":0.002,"openalex":"https://openalex.org/W4308915439"},{"id":"W2015883952","doi":"10.1145/2451176.2451217","title":"Semantic search for smart mobile devices","date":"2013-03-19","year":2013,"type":"conference-paper","authors":"Sangjin Shin | Jihoon Ko | Dong-Hoon Shin | Jooik Jung | Kyong-Ho Lee","authors_count":5,"venue":"Proceedings of the companion publication of the 2013 international conference on Intelligent user interfaces companion","publisher":null,"is_oa":0,"oa_status":"closed","license":null,"citations":7,"is_retracted":0,"url":"https://doi.org/10.1145/2451176.2451217","pdf_url":null,"topic":"Web Data Mining and Analysis","field":"Computer Science","keywords":"Computer science | Android (operating system) | Semantic search | Ontology | Keyword search | Search engine | Information retrieval | Mobile device | World Wide Web | Human–computer interaction | Operating system","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"To enhance the incorrectness of keyword based search, we propose an efficient semantic search method based on a lightweight mobile ontology designed for smart mobile devices. In addition, we implement a prototype of semantic search engine working on Android smartphones and our prototype engine provides better user experience compared with keyword based search.","score":0.4544,"junk":0.048,"openalex":"https://openalex.org/W2015883952"},{"id":"W2161481410","doi":"10.1109/optim.2014.6851023","title":"User experience and efficiency for semantic search engine","date":"2014-05-01","year":2014,"type":"conference-paper","authors":"Arooj Fatima | Cristina Luca | George B. Wilson","authors_count":3,"venue":"2014 International Conference on Optimization of Electrical and Electronic Equipment (OPTIM)","publisher":null,"is_oa":0,"oa_status":"closed","license":null,"citations":12,"is_retracted":0,"url":"https://doi.org/10.1109/optim.2014.6851023","pdf_url":null,"topic":"Semantic Web and Ontologies","field":"Computer Science","keywords":"Semantic search | Computer science | Information retrieval | Search engine | Search analytics | Ontology | Web crawler | Phrase search | World Wide Web | Metasearch engine | Semantic Web | Search-oriented architecture | Web search query","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"Search tools are essential for an information system. Being syntax based, the existing search engines have a number of limitations in particular their difficulty in returning relevant results. Typically these limitations are partly mitigated through use of various customised techniques in order to provide the best possible user experience. Unlike conventional search engines, a semantic web search engine attaches meanings to key words. One of the biggest challenges for such a semantic search engine is to maintain the standard of user experience while serving its purpose of finding relevant data with meanings. A semantic search tool needs to both link meanings to search keywords and be able to efficiently retrieve information. The purpose of this work is to investigate the techniques that can be used for efficient information retrieval and effective user experience within the context of a semantic search engine. We propose two algorithms for query optimisation and ontology crawler.","score":0.4423,"junk":0.007,"openalex":"https://openalex.org/W2161481410"},{"id":"W2144350201","doi":"10.1108/prog-07-2013-0037","title":"Towards an evaluation of semantic searching in digital repositories: a DSpace case-study","date":"2015-01-22","year":2015,"type":"article","authors":"Georgia D. Solomou | Dimitrios A. Koutsomitropoulos","authors_count":2,"venue":"Data Technologies and Applications","publisher":"Emerald Publishing Limited","is_oa":0,"oa_status":"closed","license":null,"citations":5,"is_retracted":0,"url":"https://doi.org/10.1108/prog-07-2013-0037","pdf_url":null,"topic":"Semantic Web and Ontologies","field":"Computer Science","keywords":"Computer science | DSPACE | Information retrieval | Metadata | World Wide Web | Digital library | Semantic search | Construct (python library) | Originality | Data science | Semantic Web","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"Purpose – Successful learning infrastructures and repositories often depend on well-organized content collections for effective dissemination, maintenance and preservation of resources. By combining semantic descriptions already lying or implicit within their descriptive metadata, reasoning-based or semantic searching of these collections can be enabled and produce novel possibilities for content browsing and retrieval. The specifics and necessities of such an approach, however, make it hard to assess and measure its effectiveness. The paper aims to discuss these issues. Design/methodology/approach – Therefore in this paper the authors introduce a concrete methodology toward a pragmatic evaluation of semantic searching in such scenarios, which is exemplified through the semantic search plugin the authors have developed for the popular DSpace repository system. Findings – The results reveal that this approach can be appealing to expert as well as novice users alike, improve the effectiveness of content discovery and enable new retrieval possibilities in comparison to traditional, keyword-based search. Originality/value – This paper presents applied research efforts to employ semantic searching techniques on digital repositories and to construct a novel methodology for evaluating the outcomes against various perspectives. Although this is original in itself, value lies also within the concrete and measurable results presented, accompanied by an analysis, that would be helpful to assess similar (i.e. semantic query answering and searching) techniques in the particular scenario of digital repositories and libraries and to evaluate corresponding investments. To the knowledge there has been hardly any other evaluation effort in the literature for this particular case; that is, to assess the merit and usage of advanced semantic technologies in digital repositories.","score":0.4414,"junk":0.005,"openalex":"https://openalex.org/W2144350201"},{"id":"W4205960773","doi":"10.31590/ejosat.1043441","title":"Designing An Information Framework For Semantic Search","date":"2022-01-02","year":2022,"type":"article","authors":"İsmail Burak Parlak","authors_count":1,"venue":"European Journal of Science and Technology","publisher":"European Journal of Science and Technology","is_oa":1,"oa_status":"diamond","license":"cc-by-nc","citations":0,"is_retracted":0,"url":"https://doi.org/10.31590/ejosat.1043441","pdf_url":"https://dergipark.org.tr/en/download/article-file/2152479","topic":"Topic Modeling","field":"Computer Science","keywords":"Computer science | Semantic search | Information retrieval | Semantics (computer science) | Semantic computing | Artificial intelligence | Natural language processing | Context (archaeology) | Focus (optics) | Semantic similarity | Search engine | Deep learning | Semantic Web","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"New generation information retrieval procedures provide complex tools to remodel the design of search engines. Even though semantic analysis is gradually adopted by corporations, complex behavior of knowledge behind the information entails subsequent data learning models. Text models are currently in use through lexical features. Search engines with lexical methods lack contextual and semantic information. This barrier has been overcome with the development of deep learning methods. More accurate results can be retrieved by obtaining contextual information of different types of content such as text, image, video with neural models. In this study, a broad perspective of search engines was considered through lexical and semantic features. Semantic search methods were experimented then compared with lexical methods in data sets consisting of scientific documents. Since scientific documents are relatively well-formatted datasets and do not contain irrelevant content, the focus was on comparing semantic search methods and neural models throughout the study, without dealing with out-of-context data and semantic conflicts. As a result, semantic search methods performed better than lexical search. We conclude that current search-retrieval tasks require new perspectives in semantics where multimodal information is handled with deep learning strategies.","score":0.4399,"junk":0.004,"openalex":"https://openalex.org/W4205960773"},{"id":"W2156293180","doi":"10.1109/fskd.2007.189","title":"Approach of Text Search Based on Semantic Parsing Model","date":"2007-01-01","year":2007,"type":"conference-paper","authors":"Xiangfeng Wei | Quan Zhang","authors_count":2,"venue":"Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007)","publisher":null,"is_oa":0,"oa_status":"closed","license":null,"citations":2,"is_retracted":0,"url":"https://doi.org/10.1109/fskd.2007.189","pdf_url":null,"topic":"Web Data Mining and Analysis","field":"Computer Science","keywords":"Computer science | Semantic search | Parsing | Sentence | Natural language processing | Artificial intelligence | Information retrieval | Semantic computing | Natural language | Natural language understanding | Semantic matching | Knowledge base | Matching (statistics) | Semantic Web","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"Keyword-based search is one of the most important technologies of text search. But with the development of World Wide Web, it is not enough only relying on matching the form of keywords. This paper introduced a semantic parsing model constructed above a symbolic system of concepts for understanding natural language. Based on the model a sentence can be mapped into the corresponding semantic category. An approach named 'hypothesis testing' was used to analyze a sentence according to the conceptual knowledge base and rules. All rules were formalized and executed in computer program. There is also a semantic model for analyzing text. Therefore, both sentence and text can be mapped into semantic structures. A text search prototype based on the semantic model represented more accurate result by comparing the conceptual structures between search condition and text.","score":0.4416,"junk":0.012,"openalex":"https://openalex.org/W2156293180"},{"id":"W4404032369","doi":"10.1109/icccnt61001.2024.10724185","title":"Incorporating Knowledge Graphs in Semantic Search","date":"2024-06-24","year":2024,"type":"conference-paper","authors":"Aryan Aryan | Lenin Thingbaijam | Kowstubha Palle | P.V.N. Prasad | Balasubbareddy Mallala | S. T. Patil","authors_count":6,"venue":"2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT)","publisher":null,"is_oa":0,"oa_status":"closed","license":null,"citations":3,"is_retracted":0,"url":"https://doi.org/10.1109/icccnt61001.2024.10724185","pdf_url":null,"topic":"Semantic Web and Ontologies","field":"Computer Science","keywords":"Computer science | Knowledge graph | Semantic search | Information retrieval | Artificial intelligence | Natural language processing | Semantic Web","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"Information Graphs are a powerful mechanism for incorporating dependent facts into semantic search. They are a form of graph database that represents standards and their relationships, as opposed to just information and their attributes. This additional layer of abstraction enables greater and a sophisticated inferencing and querying, making them perfect to be used in semantic search. Incorporating knowledge Graphs into semantic search entails numerous technical steps: 1. Information Extraction: The first step is to pick out and extract applicable facts from various resources, such as databases, text documents, and APIs. This fact can encompass entities, relationships, and their attributes. 2. Information Alignment: Once the records have been extracted, it needs to be aligned with the ideas in the knowledge graph. This includes mapping the extracted records to the suitable entities and their relationships in the graph. 3. Information Enrichment: To improve the first-class and completeness of the knowledge graph, the extracted statistics may additionally need to be enriched with additional statistics. This will involve incorporating information from external sources or leveraging device mastering techniques to deduce lacking facts. 4. Entity Disambiguation: Considering that entities in the understanding graph may additionally have comparable or ambiguous names, it is very much essential to disambiguate them to ensure correct search outcomes. This can be done through strategies inclusive of named entity recognition.","score":0.4605,"junk":0.075,"openalex":"https://openalex.org/W4404032369"},{"id":"W7098883252","doi":null,"title":"Semantic Search and Relationship Ranking using SWETO Ontology","date":"2010-07-22","year":2010,"type":"article","authors":"Shasha Liu | Asmita Rahman","authors_count":2,"venue":"http://www.cs.uga.edu/%7Eshasha/course/csci8380/8380ReportFinal.pdf","publisher":null,"is_oa":0,"oa_status":"closed","license":null,"citations":0,"is_retracted":0,"url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.167.8990","pdf_url":null,"topic":"Phytochemistry Medicinal Plant Applications","field":"Agricultural and Biological Sciences","keywords":"Semantic search | Ranking (information retrieval) | Semantic Web | Semantic query | Ontology | Social Semantic Web | Search engine | Semantic computing","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"Abstract — Web has become an important part of our day to day lives. Whether we are trying to make an online payment or if we are trying to reserve a trip, we can do that with a click of a mouse. Web has defined the way we live. It has made our lives easier and effortless. Search engines play an important role in the current web’s infrastructure. Similarly, with the advent of semantic web, there would be a need of semantic search engines. Semantic search starts with the existing searchable data in the knowledge base and focuses on the semantic meaning of user query. In order to find the relationship between two entities, a recursive semantic search algorithm is proposed and tested in SWETO ontology. After applying the relationship ranking algorithm, the search and ranking results meet our expectation. A prototype system is implemented as well. Index Terms — Semantic Search, Relationship Ranking 1.","score":0.4729,"junk":0.117,"openalex":"https://openalex.org/W7098883252"},{"id":"W2601553472","doi":null,"title":"Agent Based Semantics Using Relationships","date":"2010-01-01","year":2010,"type":"article","authors":"Krishnaswami “Hari” Srihari | A. Chitra T. Rajan","authors_count":2,"venue":"CiiT international journal of data mining and knowledge engineering","publisher":null,"is_oa":0,"oa_status":"closed","license":null,"citations":0,"is_retracted":0,"url":"http://www.ciitresearch.org/dl/index.php/dmke/article/view/DMKE022010003","pdf_url":null,"topic":"Semantic Web and Ontologies","field":"Computer Science","keywords":"Computer science | Information retrieval | OWL-S | Semantic search | Semantic Web Stack | Upper ontology | Ontology | Ontology Inference Layer | Semantic Web | Social Semantic Web | Ontology-based data integration | Semantic analytics | World Wide Web","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"The semantic web initiative of the world-wide web consortium (w3c) has been active for the last few years and has attracted interest and skepticism in equal measure. Semantic search generally deals with the searching of the keyword along with the concept related to that semantic word. Perhaps the most widely developed space at the moment within the semantic web is in information management, i.e. the organization and discovery of information. This is the primary motivation behind the semantic web’s development, but people are taking a variety of approaches to developing tools to extend the current web into a true semantic web. One of the methods of implementing semantic search is by means of agents like indexing agent, query agent and ontology agent. Index agent downloads files from web and extract textual content, query agent process all queries, ontology agent extracts the descriptive concepts and calculates relation between those concepts. The main problem with these agents is the pages retrieved after searching are not relevant. The other method of implementing semantic search is by combining semantic search and ontology learning. The need for domain ontology for information retrieval to improve answer queries, However ontology in information retrieval systems requires regular updates and relationship between new concepts. Semantic search and ontology collectively improves the value of indexing documents and enriching ontology. It is done by multi layer like domain ontology,topic ontology, and natural language ontology. The main constraint here is this system is based on little domain ontology that cannot be expanded. Semantic search is also implemented by improving results with light weight semantic search. Here search queries and light weight semantics are combined together to improve search results. It is done by basic and concept scoring computation. Here indexing is done allocating resources and results are obtained by means of search engines. The main problems in this research are the inverted resources and metadata are not allocated properly for indexing. The problems with the previous implementations are time complexity, accuracy in retrieving pages, domain ontology,and resource allocation for indexing. The semantic pages are searched in the query repository based upon the algorithm for query retrieval. Then an algorithm for ontology based search is implemented. The ontology is obtained based on multi level ontology structure. Then a retrieval algorithm is designed to implement the semantic search.","score":0.4544,"junk":0.055,"openalex":"https://openalex.org/W2601553472"},{"id":"W2100029298","doi":"10.1109/icsccn.2011.6024595","title":"Improving the precision ratio using semantic based search","date":"2011-07-01","year":2011,"type":"conference-paper","authors":"S. Amudaria | S.P. Sasirekha","authors_count":2,"venue":"2011 International Conference on Signal Processing, Communication, Computing and Networking Technologies","publisher":null,"is_oa":0,"oa_status":"closed","license":null,"citations":0,"is_retracted":0,"url":"https://doi.org/10.1109/icsccn.2011.6024595","pdf_url":null,"topic":"Semantic Web and Ontologies","field":"Computer Science","keywords":"Computer science | Information retrieval | Precision and recall | Ontology | Concept search | Semantic search | Process (computing) | Explicit semantic analysis | Recall | Search engine | Semantic computing | Semantic technology | Web search query | Semantic Web","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"The overall goal of an information retrieval process is to retrieve the information relevant to the given request. The information retrieval techniques commonly used are based on keywords. These techniques use keyword listed to describe the content of information, but one problem with such list is that they do not say anything about the semantic relationships between keywords, nor do they take into account the meaning of words and phrases. To overcome these limitations, from the recent literature it is identified that it is necessary to analyze and determine the semantic features of both the content in document and query. Hence in this paper it is proposed to develop ontology and a comparison is made between the normal search and ontology based semantic search. Various experimental results are carried on, which shows the increase in document retrieval recall and precision rates, thereby demonstrating the effectiveness of the model.","score":0.4386,"junk":0.003,"openalex":"https://openalex.org/W2100029298"},{"id":"W4404104718","doi":"10.55041/ijsrem15118","title":"A Review of Hybrid Search Architectures: Integrating Keyword and Semantic Search to Optimize Query Relevance","date":"2024-11-06","year":2024,"type":"article","authors":"Senior Solutions Architect, Data Science Dojo  Bellevue, WA | Syed Arham Akheel","authors_count":2,"venue":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","publisher":null,"is_oa":1,"oa_status":"bronze","license":null,"citations":0,"is_retracted":0,"url":"http://dx.doi.org/10.55041/ijsrem15118","pdf_url":"https://ijsrem.com/download/a-review-of-hybrid-search-architectures-integrating-keyword-and-semantic-search-to-optimize-query-relevance/?wpdmdl=39674&refresh=672ec84ade5441731119178","topic":"Data Management and Algorithms","field":"Computer Science","keywords":"Relevance (law) | Information retrieval | Computer science | Keyword search | Web search query | Search engine | Semantic search | Query expansion","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"Abstract—The advent of semantic search alongside the tra- ditional robustness of keyword search systems has established an intriguing dichotomy within the landscape of information retrieval. While keyword-based methods excel in terms of speci- ficity, semantic models offer contextual richness and intent aware- ness. However, each paradigm faces challenges in harnessing the strengths of the other. This paper investigates a hybrid search model that integrates both keyword and semantic search techniques, aiming to optimize relevance, interpretability, and user experience in complex information-seeking scenarios. We present an innovative hybrid architecture, conduct a comprehen- sive evaluation of its performance, and discuss its implications for information retrieval in specialized domains such as healthcare and education. Index Terms—Hybrid Search, Keyword Search, Semantic Search, Information Retrieval, Natural Language Processing, Search Architectures","score":0.4505,"junk":0.044,"openalex":"https://openalex.org/W4404104718"},{"id":"W2352483517","doi":null,"title":"Semantic Search System using Ontology-based Inference","date":"2005-01-01","year":2005,"type":"article","authors":"Ha Sang-Bum | Park Yong-Tack","authors_count":2,"venue":"Jeongbo gwahaghoe nonmunji. so'peuteuweeo mich eung'yong","publisher":"Korean Institute of Information Scientists and Engineers (KIISE)","is_oa":0,"oa_status":"closed","license":null,"citations":1,"is_retracted":0,"url":"https://www.kci.go.kr/kciportal/ci/sereArticleSearch/ciSereArtiView.kci?sereArticleSearchBean.artiId=ART001183875","pdf_url":null,"topic":"Semantic Web and Ontologies","field":"Computer Science","keywords":"Computer science | Information retrieval | Ontology | Semantic search | Semantics (computer science) | Inference | Semantic Web | Artificial intelligence | Programming language","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"The semantic web is the web paradigm that represents not general link of documents but semantics and relation of document. In addition it enables software agents to understand semantics of documents. We propose a semantic search based on inference with ontologies, which has the following characteristics. First, our search engine enables retrieval using explicit ontologies to reason though a search keyword is different from that of documents. Second, although the concept of two ontologies does not match exactly, can be found out similar results from a rule based translator and ontological reasoning. Third, our approach enables search engine to increase accuracy and precision by using explicit ontologies to reason about meanings of documents rather than guessing meanings of documents just by keyword. Fourth, domain ontology enables users to use more detailed queries based on ontology-based automated query generator that has search area and accuracy similar to NLP. Fifth, it enables agents to do automated search not only documents with keyword but also user-preferable information and knowledge from ontologies. It can perform search more accurately than current retrieval systems which use query to databases or keyword matching. We demonstrate our system, which use ontologies and inference based on explicit ontologies, can perform better than keyword matching approach .","score":0.4459,"junk":0.031,"openalex":"https://openalex.org/W2352483517"},{"id":"W2894831396","doi":"10.14569/ijacsa.2018.090926","title":"Profile-Based Semantic Method using Heuristics for Web Search Personalization","date":"2018-01-01","year":2018,"type":"article","authors":"Hikmat A. M. Abdeljaber","authors_count":1,"venue":"International Journal of Advanced Computer Science and Applications","publisher":"Science and Information Organization","is_oa":1,"oa_status":"diamond","license":"cc-by","citations":1,"is_retracted":0,"url":"https://doi.org/10.14569/ijacsa.2018.090926","pdf_url":"http://thesai.org/Downloads/Volume9No9/Paper_26-Profile_based_Semantic_Method.pdf","topic":"Web Data Mining and Analysis","field":"Computer Science","keywords":"Computer science | Information retrieval | Personalization | Semantic search | Heuristics | Ontology | Profiling (computer programming) | Web search query | User profile | World Wide Web | Search engine | Semantic Web","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"User profiles play a critical role in personalizing user search. It assists search systems in retrieving relevant information that is searched on the web considering the user needs. Researchers presented a vast number of profile-based approaches that aims to improve the effectiveness of information retrieval. However, these approaches are syntactic-based which fail to achieve the user satisfaction. By the means that the search results do not meet user preferences, due to the fact that the search is keyword-based rather than semantic-based. Exploiting user profiles with the application of semantic web technology into personalization might produce a step forward in future retrieval systems. By adopting profiling approach and using ontology base characteristics, a semantic-based method using heuristics and KNN algorithm is proposed. It engages searching ontology base domains horizontally and vertically to discover and extract the closest concept to the meaning of the query keyword. The extracted concept is used to expand the user query to personalize the search result and present the customized information for individuals.","score":0.4376,"junk":0.004,"openalex":"https://openalex.org/W2894831396"},{"id":"W7095268965","doi":null,"title":"Chungnam, Republic of Korea,","date":"2012-03-19","year":2012,"type":"article","authors":"Jihyun Lee | Jun-ki Min | Chin-wan Chung","authors_count":3,"venue":"http://www2009.org/proceedings/pdf/p1057.pdf","publisher":null,"is_oa":0,"oa_status":"closed","license":null,"citations":0,"is_retracted":0,"url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.215.7615","pdf_url":null,"topic":"Political Science Research and Education","field":"Social Sciences","keywords":"Semantic Web | Ranking (information retrieval) | Order (exchange) | Measure (data warehouse) | Semantic search | Social Semantic Web | Semantics (computer science)","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"In this paper, we present a semantic search technique considering the type of desired Web resources and the semantic relationships between the resources and the query keywords in the ontology. In order to effectively retrieve the most relevant top-k resources, we propose a novel ranking model. To do this, we devise a measure to determine the weight of the semantic relationship. In addition, we consider the number of meaningful semantic relationships between","score":0.4458,"junk":0.031,"openalex":"https://openalex.org/W7095268965"},{"id":"W2624902666","doi":null,"title":"Smart Image Search System Using Personalized Semantic Search Method","date":"2017-01-01","year":2017,"type":"article","authors":"Fangyu Zhang","authors_count":1,"venue":"Open PRAIRIE (South Dakota State University)","publisher":"South Dakota State University","is_oa":1,"oa_status":"green","license":null,"citations":0,"is_retracted":0,"url":"https://openprairie.sdstate.edu/etd/1167","pdf_url":"https://openprairie.sdstate.edu/cgi/viewcontent.cgi?article=2168&context=etd","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","keywords":"Computer science | Semantic search | Information retrieval | Image (mathematics) | Artificial intelligence | Computer vision | Search engine","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"Due to the emerge in huge numbers of information on the internet nowadays, search technologies are widely used in various fields. Achieving the most relevant search result for the users becomes a big challenge now. While the traditional semantic search technologies seem to achieve the most relevant search result, however, it faces two problems: one is the one-size-fits-all problem, and another is low efficiency. The purpose of this research is to build a Smart Image Search System by using the personalized semantic search method to solve those problems. The personalized semantic search method makes the search system avoids the one-size-fits-all issue, and increase the efficiency. In the Smart Image Search System, the personalized semantic search method provides users three options to search. They are non-option search, general-option search, and private-option search. Each option search has its specific user needs to achieve the most relevant results. Those options are adopted to solve the one-size-fits-all problem. Also, based on the idea of semantic context concept, the personalized semantic method uses two approaches to increase the search efficiency. First, it applies Apache OpenNLP Library to avoid useless words. Second, it considers the searchers’ actions such as click and feedbacks to affect the associated words and associated weight. The Smart Image Search System uses the associated words and associated weight to calculate the relativity for the search results. This approach makes the Smart Image Search System becomes a self-improved system. Smart Image Search System is implemented based on the presented methodology and design. As a result of current research on semantic search technologies, we conclude that the Smart Image Search System can avoid useless words, fix the one-size-fits-all problem, and self-improve its relevancy.","score":0.4419,"junk":0.018,"openalex":"https://openalex.org/W2624902666"},{"id":"W2105731169","doi":"10.1109/fskd.2009.766","title":"Towards the Ontology Organization for Semantic Searching","date":"2009-01-01","year":2009,"type":"conference-paper","authors":"Li-Zhen Li | Kemin Xie | Zhifeng Dong","authors_count":3,"venue":"2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery","publisher":null,"is_oa":0,"oa_status":"closed","license":null,"citations":0,"is_retracted":0,"url":"https://doi.org/10.1109/fskd.2009.766","pdf_url":null,"topic":"Semantic Web and Ontologies","field":"Computer Science","keywords":"Computer science | Semantic search | Information retrieval | Ontology | Semantic Web Stack | OWL-S | Semantic Web | Upper ontology | Semantic analytics | Social Semantic Web | World Wide Web | Task (project management) | Ontology Inference Layer | Ontology-based data integration | Set (abstract data type) | Engineering","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"The amount of knowledge and the number of existing Web pages are increasing endlessly thus making the task of the search engine supplying semantic search can meet the semantically rich requirements demanded by users. Ontology-based search engines provide one solution to this problem. To organize the semantic concepts & action is the basement to set up the semantic search. This paper focuses on the characteristics of ontology structures and organization that will facilitate semantic searches.","score":0.4382,"junk":0.008,"openalex":"https://openalex.org/W2105731169"},{"id":"W2160191307","doi":"10.1109/sami.2008.4469154","title":"Software Means for the Support of Semantic Web","date":"2008-01-01","year":2008,"type":"conference-paper","authors":"Jozef Vrana | Martin Džbor | Kristína Machová","authors_count":3,"venue":"2008 6th International Symposium on Applied Machine Intelligence and Informatics","publisher":null,"is_oa":0,"oa_status":"closed","license":null,"citations":0,"is_retracted":0,"url":"https://doi.org/10.1109/sami.2008.4469154","pdf_url":null,"topic":"Semantic Web and Ontologies","field":"Computer Science","keywords":"Computer science | Semantic Web Stack | Social Semantic Web | World Wide Web | Data Web | Semantic Web | Web modeling | Web page | Web standards | Information retrieval | Semantic analytics | OWL-S | Semantic search | Web search engine | Static web page | Web intelligence | Web navigation","mesh":null,"pmcid":null,"pmc_oa":null,"pmc_license":null,"pmc_type":null,"smr":0,"abstract":"This article discusses the means of software design that gives Web search semantic character. This work focus on system Magpie. Magpie is tool that interprets contents of web pages. Magpie helps user to find relevant information from a chosen area of interest. Common Web search approaches return only web pages that contain search phrases. On the other side, Web with semantic layer improves machine processing of data from the Web source. Semantic layer is dependent on the ontology availability. Ontology helps with assigning meaning to the information on web page and offers user additional functions based on semantic services.","score":0.461,"junk":0.085,"openalex":"https://openalex.org/W2160191307"}],"timing":{"encode_ms":37.6,"candidates":300,"ivf_ms":38.4,"rerank_ms":9.6,"junk_ms":1.1,"details_ms":33.0,"total_ms":120.1},"filters":{},"ranking":{"by":"similarity - junk_penalty * junk","junk_penalty":0.3},"mode":"semantic"}