Semantic Query Expansion Combining Association Rules with Ontologies and Information Retrieval Techniques

  • Min Song
  • Il-Yeol Song
  • Xiaohua Hu
  • Robert Allen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3589)


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.


Association Rule Noun Phrase Relevance Feedback Retrieval Performance Query Expansion 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Agrawal, R., Shafer, J.C.: Parallel mining of association rules. IEEE Transactions on Knowledge and Data Engineering 8(6), 962–969 (1996)CrossRefGoogle Scholar
  2. 2.
    Cohen, W.W., Singer, Y.: Simple, Fast, and Effective Rule Learner. In: Proceedings of the Sixteenth National Conference on Artificial Intelligence and Eleventh Conference on Innovative Applications of Artificial Intelligence, July 18-22, pp. 335–342 (1999)Google Scholar
  3. 3.
    French, J.C., Powell, A.L., Gey, F., Perelman, N.: Exploiting a Controlled Vocabulary to Improve Collection Selection and Retrieval Effectiveness. In: 10th International Conference on Information and Knowledge Management (2001)Google Scholar
  4. 4.
    Gonzalo, J., Verdejo, F., Chugur, I., Cigarran, J.: Indexing with WordNet synsets can improve text retrieval. In: Proceedings of the COLING/ACL Workshop on Usage of WordNet in Natural Language Processing systems, Montreal (1998)Google Scholar
  5. 5.
    Lam-Adesina, A.M., Jones, G.J.F.: Applying Summarization Techniques for Term Selection in Relevance Feedback. In: Proceedings of the 24th annual international ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1–9 (2001)Google Scholar
  6. 6.
    Liu, S., Liu, F., Yu, C., Meng, W.: An Effective Approach to Document Retrieval via Utilizing WordNet and Recognizing Phrases. In: Proceedings of the 27th annual international Conference on Research and development in Information Retrieval, pp. 266–272 (2004)Google Scholar
  7. 7.
    Latiri, C.C., Yahia, S.B., Chevallet, J.P., Jaoua, A.: Query expansion using fuzzy association rules between terms. In: JIM 2003, France, September 3-6 (2003)Google Scholar
  8. 8.
    Mihalcea, R., Moldovan, D.: Semantic Indexing Using WordNet Senses. In: ACL Workshop on IR & NLP (2000)Google Scholar
  9. 9.
    Mitra, C.U., Singhal, A., Buckely, C.: Improving Automatic Query Expansion. In: Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 206–214 (1998)Google Scholar
  10. 10.
    Salton, G., Buckley, C., Fox, E.A.: Automatic query formulations in information retrieval. Journal of the American Society for Information Science 34(4), 262–280 (1983)CrossRefGoogle Scholar
  11. 11.
    Sanderson, M.: Word sense disambiguation and information retrieval. In: Proceedings, ACM Special Interest Group on Information retrieval, pp. 142–151 (1994)Google Scholar
  12. 12.
    Voorhees, E.M.: Using WordNet for Text Retrieval. In: Fellbaum, C. (ed.) WordNet, an Electronic Lexical Database, pp. 285–303. MIT Press, Cambridge (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Min Song
    • 1
  • Il-Yeol Song
    • 1
  • Xiaohua Hu
    • 1
  • Robert Allen
    • 1
  1. 1.College of Information Science & TechnologyDrexel UniversityPhiladelphia

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