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
Download to read the full chapter text
Chapter PDF
References
H. Lee, S. Lin, and C. Huang, Interactive Query Expansion Based on Fuzzy Association Thesaurus for Web Information Retrieval, in Proc. of the 10th IEEE International Conference on Fuzzy System (IEEE Press: Melbourne, Australia, 2001), pp.724–727.
J. Gonzalo, F. Verdejo, and I. Chugur, Using Eurowordnet in a Concept-Based Approach to Cross-Language Text Retrieval, Applied Artificial Intelligence. Volume 13, Number 7, pp.647–678, (1999).
E.M. Voorhees, Using WordNet to Disambiguate Word Senses for Text Retrieval, in Proc. of the Sixteenth Annual Intl. ACM SIGIR Conf. on Research and Development in Information Retrieval (ACM Press: New York, NY, USA, 1993), pp.171 -180.
R. Mandala, T. Tokunaga, and H. Tanaka, Combining multiple evidence from different types of thesaurus for query expansion, in Proc. of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (ACM Press: Berkeley, CA, USA, 1999), pp.191-197.
G.A. Miller, R. Beckwith, C. Fellbaum, D. Gross, and K. Miller, Introduction to WordNet: an on-line lexical database, International Journal of Lexicography. Volume 3, Number 4, pp.235-244.
M.E. Maron and J.L. Kuhns, On Relevance, Probabilistic Indexing and Information Retrieval, Journal of the Association for Computer Machinery. Volume 7, Number 3, pp.216–244,(1960).
J.J. Rocchio, Relevance feedback in information retrieval , in The SMART Retrieval System ,eds. G. Salton (Prentice-Hall, Inc.: Englewood Cliffs, NJ, 1971), pp.313–323.
G. Salton and C. Buckley, Improving retrieval performance by relevance feedback, Journal of the American Society for Information Science. Volume 41, Number 4, pp.288–297, (1990).
L. Song, Y. Cheng, and Q. Shan, Relevance Feedback for Information Retrieval System, Journal of the China Society for Scientific and Technical Information. Volume 24, Number 1, pp.34–41, (2005).
P. Paggio, B.S. Pedersen, and D. Haltrup, Applying Language Technology to Ontology Based Querying: the Ontoquery Project, Applied Artificial Intelligence. Volume 17, Number 8 &9, pp.817–833, (2003).
B.Y. Ricardo and R.N. Berthier, Retrieval Performance Evaluation, in Modem Information Retrieval ,eds. X. Sui (China Machine Press: Beijing, 2005), pp.51–57.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2007 IFIP International Federation for Information Processing
About this paper
Cite this paper
Wu, F., Wu, G., Fu, X. (2007). Design and Implementation of Ontology-Based Query Expansion for Information Retrieval. In: Xu, L.D., Tjoa, A.M., Chaudhry, S.S. (eds) Research and Practical Issues of Enterprise Information Systems II. IFIP — The International Federation for Information Processing, vol 254. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-75902-9_30
Download citation
DOI: https://doi.org/10.1007/978-0-387-75902-9_30
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4757-0563-8
Online ISBN: 978-0-387-75902-9
eBook Packages: Computer ScienceComputer Science (R0)