Abstract
One of the main issues in the field of information retrieval is to bridge the terminological gap existing between the way in which users specify their information needs and the way in which queries are expressed. One of the approaches for this purpose, called Rule Based Information Retrieval by Computer (RUBRIC), involves the use of production rules to capture user query concepts (or topics). In RUBRIC, a set of related production rules is represented as an AND/OR tree. The retrieval output is determined by Boolean evaluation of the AND/OR tree. However, since the Boolean evaluation ignores the termterm association unless it is explicitly represented in the tree, the terminological gap between users’ queries and their information needs can still remain. To solve this problem, we adopt the generalized vector space model (GVSM) in which the term-term association is well established, and extend the RUBRIC model based on GVSM. Experiments have been performed on some variations of the extended RUBRIC model, and the results have also been compared to the original RUBRIC model based on recall-precision.
This work is supported in part by the US Army Research Office, by Grant No. DAAH04-96-1- 0325, under DEPSCoR program of Advanced Research Projects Agency, Department of Defense, and in part by the U.S. Department of Energy, Grant No. DE-FG02-97ER1220, and by the University of Bahrain.
On leave from the Department of Computer Engineering in Ajou University, Korea.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Alsaffar, A. H., Deogun, J. S., Raghavan, V. V., and Sever, H. Concept-based retrieval with minimal term sets. In Z. W. Ras and A. Skowon, editors, Foundations of Intelligent Systems: Eleventh Int’l Symposium, ISMIS’99 proceedings, pp. 114–122, Springer, Warsaw, Poland, Jun, 1999.
Croft, W. B. Approaches to intelligent information retrieval. Information Processing and Management, 1987, Vol. 23, No. 4, pp. 249–254.
Kim, M. and Raghavan, V. V. Adaptive Concept-based Retrieval Using a Neural Network. In Proceedings of ACM SIGIR Workshop on Mathematical/Formal Methods in Information Retrieval, July 28, 2000, Athens, Greece.
Kim, M., Lu, F., and Raghavan, V. V. Automatic Construction of Rule-based Trees for Conceptual Retrieval. In Proceedings of SPIRE2000, September 27–29, 2000, A Coruna, Spain, IEEE Computer Society Press.
Lu, F., Johnsten, T., Raghavan, V. V., and Dennis Traylor, Enhancing Internet Search Engines to Achieve Concept-based Retrieval, In Proceeding of Inforum’99, Oakridge, USA
McCune, B. P., Tong, R. M., Dean, J. S., and Shapiro, D. G. RUBRIC: A System for Rule-Based Information Retrieval, IEEE Transaction on Software Engineering, Vol. SE-11, No. 9, September 1985.
Resnik, P. Using information content to evaluate semantic similarity in a taxonomy. In Proceedings of the 14th International Joint Conference on Artificial Intelligence, pp. 448–453, 1995.
Salton, G. and Lesk, M. E. Computer Evaluation of Indexing and Text Processing. ACM 15, 1 (Jan, 1968), pp. 8–36.
Salton, G. and McGill, M. J. Introduction to Modern Information Retrieval.McGraw Hill, New York, 1983.
Wong, S.K.M., Ziarko, W., and Wong, P. C. N. Generalized Vector Space Model in Information Retrieval. In Proceedings of the 8th Annual International ACMSIGIR Conference, 1985, New York, pp. 18–25.
Wong, S.K.M., Ziarko, W., Raghavan, V., and Wong, P. C. N. On Modeling of Information Retrieval Concepts in Vector Spaces, ACM Transaction on Database System, Vol. 12, No. 2, June 1987. pp. 299–321.
Wong, S.K.M., Ziarko, W., Raghavan, V., and Wong, P. C. N. Extended Boolean Query Processing in the Generalized Vector Space Model, Information Systems Vol. 14, No. 1, pp. 47–63, 1989.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kim, M., Alsaffar, A.H., Deogun, J.S., Raghavan, V.V. (2000). On Modeling of Concept Based Retrieval in Generalized Vector Spaces. In: RaĹ›, Z.W., Ohsuga, S. (eds) Foundations of Intelligent Systems. ISMIS 2000. Lecture Notes in Computer Science(), vol 1932. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-39963-1_48
Download citation
DOI: https://doi.org/10.1007/3-540-39963-1_48
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-41094-2
Online ISBN: 978-3-540-39963-6
eBook Packages: Computer ScienceComputer Science (R0)