Word sense disambiguation and information retrieval
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Improving automatic query expansion
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
A simple, fast, and effective rule learner
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Applying summarization techniques for term selection in relevance feedback
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Exploiting a controlled vocabulary to improve collection selection and retrieval effectiveness
Proceedings of the tenth international conference on Information and knowledge management
Parallel Mining of Association Rules
IEEE Transactions on Knowledge and Data Engineering
An effective approach to document retrieval via utilizing WordNet and recognizing phrases
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Semantic indexing using WordNet senses
RANLPIR '00 Proceedings of the ACL-2000 workshop on Recent advances in natural language processing and information retrieval: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 11
Semantic spam filtering from personalized ontologies
Journal of Web Engineering
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Query expansion techniques are used to find the desired set of query terms to improve retrieval performance. One of the limitations with the query expansion techniques is that a query is often expanded only by the linguistic features of terms. This paper presents a novel semantic query expansion technique that combines association rules with ontologies and information retrieval techniques. We propose to use the association rule discovery to find good candidate terms to improve the retrieval performance. These candidate terms are automatically derived from collections and added to the original query. Our method is differentiated from others in that 1) it utilizes the semantics as well as linguistic properties of unstructured text corpus and 2) it makes use of contextual properties of important terms discovered by association rules. Experiments conducted on a subset of TREC collections give quite encouraging results. We achieve from 15.49% to 20.98% improvement in term of P@20 with TREC5 ad hoc queries.