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.
['Ki-Joo Hong', 'Han-Joon Kim']
A semantic search technique with Wikipedia-based text representation model