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2016 International Conference on Big Data and Smart Computing (BigComp) (2016)
Hong Kong, China
Jan. 18, 2016 to Jan. 20, 2016
ISSN: 2375-9356
ISBN: 978-1-4673-8795-8
pp: 177-182
Ki-Joo Hong , School of Electrical and Computer Engineering, University of Seoul, Korea
Han-Joon Kim , School of Electrical and Computer Engineering, University of Seoul, Korea
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.
INDEX TERMS
Semantics, Tensile stress, Encyclopedias, Ontologies, Mathematical model, Electronic publishing
CITATION

K. Hong and H. Kim, "A semantic search technique with Wikipedia-based text representation model," 2016 International Conference on Big Data and Smart Computing (BigComp)(BIGCOMP), Hong Kong, China, 2016, pp. 177-182.
doi:10.1109/BIGCOMP.2016.7425818
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