Abstract
In this paper we introduce another approach to information retrieval based on rough set theory, but instead of equivalence relations we adopt tolerance relations. We define a tolerance space by employing the co-occurrence of terms in the collection of documents and an algorithm for matching the user query. An illustrative example is provided that shows the application potential of the approach.
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Belkin, N.J., Croft, W.B.: Retrieval techniques. Annual Review of Information Science and Technology 22 (1989) 109–145
Bookstein, A.: Probability and fuzzy-set applications to information retrieval. Annual review of information science and technology 20 (1985) 117–151
Chen, H.: Machine learning for information retrieval: Neural Networks, Symbolic Learning, and Genetic Algorithms. Journal of the American Society for Information Science 46/3 (1995) 194–216
Frakes, W.B.: Introduction to information storage and retrieval systems. In: W.B. Frakes and R. Baeza-Yates (eds.): Information Retrieval: Data Structures 00000 Algorithms, Prentice Hall (1992) 1–27
Ho, T.B., Funakoshi, K.: Information retrieval using rough sets. Journal of Japanese Society for Artificial Intelligenc (submitted)
Fox, E., et al.: Extended boolean models. In: W.B. Frakes, R. Baeza-Yates (eds.), Information Retrieval: Data Structures Si Algorithms, Prentice Hall, (1992) 393–418
Harman, D. et al.: Inverted files. In: W.B. Frakes, R. Baeza-Yates (eds.), Information Retrieval: Data Structures Sc Algorithms, Prentice Hall (1992) 28–43
Kantor, P.B.: Information retrieval techniques. Annual Review of Information Science and Technology 29 (1994) 53–90
Nieminen, J.: Rough tolerance equality and tolerance black boxes. Fundamenta informaticae 11 (1988) 289–296
In Pawlak, Z.: Rough sets: Theoretical aspects of reasoning about data. Kluwer Academic Publishers, Dordrecht (1991)
Raghavan, V.V., Sharma, R.S.: A framework and a prototype for intelligent organisation of information. The Canadian Journal of Information Science 11 (1986) 88–101
Skowron, A., Stepaniuk, J.: Generalized approximation spaces. In: T.Y. Lin (ed.): Proceedings of the Third International Workshop on Rough Sets and Soft Computing (RSSC’94), San Jose State University, San Jose, California, USA, November 10–12 (1994) 156–163
Srinivasan, P.: Intelligent information retrieval using rough set approximations. Information Processing Sc Management 25/4 (1989) 347–361
Srinivasan, P.: The importance of rough approximations for information retrieval. International Journal of Man-Machine Studies 34/5 (1991) 657–671
Wartik, S.: Boolean operations. In: Frakes, W.B. and Baeza-Yates, R. (eds.),: Information Retrieval: Data Structures 00000 Algorithms, Prentice Hall (1992) 264–292
Yao, Y.Y., Li, X., Lin, T.Y. and Liu, Q.: Representation and classification of rough set models. In: T.Y. Lin (ed.): Proceedings of the Third International Workshop on Rough Sets and Soft Computing (RSSC’94), San Jose State University, San Jose, California, USA, November 10–12 (1994) 630–637
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© 1998 Springer-Verlag Berlin Heidelberg
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Funakoshi, K., Ho, T.B. (1998). A Rough Set Approach to Information Retrieval. In: Polkowski, L., Skowron, A. (eds) Rough Sets in Knowledge Discovery 2. Studies in Fuzziness and Soft Computing, vol 19. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1883-3_9
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DOI: https://doi.org/10.1007/978-3-7908-1883-3_9
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-7908-2459-9
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