Abstract. In Information Retrieval (IR), the user's input query conditions usually are not detailed enough, so the satisfactory query results can not be brought back. Query expansion of IR can help to solve this problem. However, the common query expansion in IR cannot get steady retrieval results. In this paper, we propose and implement query expansion method which combines domain ontology with the frequent of terms. Ontology is used to describe domain knowledge; logic reasoner and the frequency of terms are used to choose fitting expansion words. By this way, higher recall and precise can be gotten as user' query results. Experimental results show that compared with the results of common query expansion, the method described in this paper can get statistically significant improvement in recall and precise combination. Keywords: Search engine, Ontology, Web ontology language (OWL), Knowledge management, Enterprise search In information retrieval (IR), even the best system has a limited recall. Users may miss many important documents which they really need usually. There are two fundamental reasons for this problem. The first one is word mismatch, which means that concepts (or key words) of user queries are often different from the words of the resource documents although these words have similar meanings. Another is that users submit short queries which are not detailed enough for IR, so the bad search performance ensues. Query expansion (QE) can effectively alleviate the problem by adding additional terms which have similar meaning to the original query. In this study, we proposed a new expansion method which is based on domain ontology and frequency of keyword occurrence in resource documents to filter expansion words. It achieves better performance in both precision and recall.
In REF the authors propose and implement query expansion method which combines domain ontology with the frequency of terms.
Design and Implementation of Ontology-Based Query Expansion for Information Retrieval
CONFENIS
Computer Science