Smart Information Retrieval using Query Transformation based on Ontology and Semantic-Association

Abstract

A notable problem with current information retrieval systems is that the input queries cannot express user information needs properly. This imprecise representation of the query hampers the effectiveness of the retrieval system. One method to solve this problem is to transform the original query into a more meaningful form. This paper proposes an ontologybased retrieval system that transforms initial user queries using domain ontologies and applies semantic association during the indexing process. The proposed system performs a semantic matching between an ontologically enhanced query and index to capture query-related terms. To show the performance of the proposed system, it is evaluated using standard parameters like precision, recall, and NDCG. In addition, the authors presented a comparison between the proposed and existing retrieval systems on three test datasets. Experimental results on these datasets indicate that the use of ontology and semantics has significantly increased the retrieval efficiency obtained by baseline. This work highlights the importance of ontology and semantics in information retrieval.