Previous |  Up |  Next


information retrieval systems; fuzzy queries
The term query is widely used in the database as well as information retrieval communities. Basically, a query against a collection of information items (to be called later, for brevity, an information source) provides a formal description of the items of interest to the user posing this query. A source of information is meant here very generally. It may take the form of an archive of multimedia or textual documents, a database, or a knowledge base. In the three previous examples the information items are documents, records (rows in relational data model) and facts, respectively. In order to manage and access an information source, an appropriate system is defined which makes it possible to store, represent and retrieve information items by means of a formal query language. Information systems that make it possible to manage information items previously mentioned are information retrieval systems, data base management systems and knowledge based systems, respectively. Query languages of these systems usually refer to some features of entities represented by the items stored in an information source, e. g., keywords (index terms) in textual documents (documents archive), attributes (database) or arguments of facts (knowledge base). Thus, basically, a query may be seen as a set of selection conditions that should be met by an information item (its features) to be qualified as relevant with respect to the query. On the other hand, the query processing itself may be seen as consisting mainly of matching a query against the items of the information source. This process may be essentially more complex, as, e. g., in the case of knowledge bases where we deal with a whole chain of matching within the reasoning process. Often, a user faces the problem of how to express her or his information requirements in a formal query language supported by a given information system interface. These formal languages usually require a crisp (precise, unambiguous) specification of a query, while, for human beings, a query is best expressed in terms of a natural language – a very powerful, but ambiguous and imprecise medium. Thus, adding some flexibility to traditional querying systems seems to be a critical issue for enhancing their effectiveness and efficiency. In this paper, we discuss some recent advances and basic issues related to flexible querying based on the application of fuzzy logic. We focus on two areas corresponding to the type of information source under consideration, namely: information retrieval in which we primarily deal with archives of textual documents and database querying. Both areas share the same interest in fuzzy (linguistic) queries and flexible matching against items of information. However, they have also their specific features, and these are pointed out in the next sections. The third area, that of very broadly meant knowledge bases querying is dealt with in the paper by Peter Vojtáš, in this special issue. Specifically, the concept of matching, essential for querying, may be identified to some extent with the unification. In the mentioned paper, the issues related to the fuzzy unification are discussed. The matching of fuzzy concepts, from a slightly different perspective, is also the subject of the paper by Andrejková, in this issue. Another contribution relevant for the flexible querying of knowledge bases is the paper by Ch. Marsala, in this issue. Moreover, beside its application to querying itself, the concept of flexibility is usually extended to the representation of information to be queried. This is particularly evident in the area of information retrieval in which concepts of fuzzy logic fit very well into advanced indexing schemes for text documents. In case of database management systems, fuzzy logic based ideas have led to the development of imprecise/vague data representation models. These issues are also dealt with in the following sections. This paper is structured in two sections dealing with information retrieval and database querying, respectively. The paper is meant to provide a synthetic description of the research area of the papers appearing in this special issue of the Kybernetika. This issue is comprised of extended versions of selected papers presented at the session on fuzzy querying at the FSTA’2000 Conference held in Liptovský Mikuláš (Slovak Republic) in the winter of 2000. We refer to the other papers in this issue indicating their relevance for the topics discussed here.
[1] Bookstein A.: Fuzzy requests: an approach to weighted boolean searches. J. Amer. Soc. Inform. Science 31 (1980), 4, 240–247 DOI 10.1002/asi.4630310403
[2] Bordogna G., Carrara P., Pasi G.: Query term weights as constraints in fuzzy information retrieval. Inform. Process. Management 27 (1991), 1, 15–26 DOI 10.1016/0306-4573(91)90028-K
[3] Bordogna G., Pasi G.: A fuzzy linguistic approach generalizing Boolean information retrieval: a model and its evaluation. J. Amer. Soc. Inform. Science 44 (1993), 2, 70–82 DOI 10.1002/(SICI)1097-4571(199303)44:2<70::AID-ASI2>3.0.CO;2-I
[4] Bordogna G., Pasi G.: Linguistic aggregation operators in fuzzy information retrieval. Internat. J. Intelligent Systems 10 (1995), 2, 233–248 DOI 10.1002/int.4550100205
[5] Bordogna G., Pasi G.: Controlling Information Retrieval through a user adaptive representation of documents. Internat. J. Approx. Reason. 12 (1995), 317–339 DOI 10.1016/0888-613X(94)00036-3 | MR 1327861
[6] Bordogna G., Pasi G.: The Application of Fuzzy Set Theory to Model Information Retrieval. In: Soft Computing in Information Retrieval: Techniques and Applications (F. Crestani and G. Pasi, eds.), Physica–Verlag, Heidelberg 2000
[7] Bordogna G., Pasi G.: Linguistic granules to express importance in an ordinal information retrieval model. In: Proceedings of the Eighth International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems (IPMU’2000), Madrid 2000, pp. 470–476
[8] Bosc P., Duval L., Pivert O.: Value-based and representation-based querying of possibilistic databases. In: Recent Issues on the Management of Fuzziness in Databases (G. Bordogna and G. Pasi, eds.), Physica–Verlag, Heidelberg 2000, pp. 3–28
[9] Bosc P., Pivert O.: Fuzzy querying in conventional databases. In: Fuzzy Logic for the Management of Uncertainty (L. A. Zadeh and J. Kacprzyk, eds.), Wiley, New York 1992, pp. 645–671
[10] Bosc P., Pivert O.: SQLf: a relational database language for fuzzy querying. IEEE Trans. Fuzzy Systems 3 (1995), 1–17 DOI 10.1109/91.366566
[11] Bosc P., Pivert O.: SQLf query functionality on top of a regular relational database management system. In: Knowledge Management in Fuzzy Databases (O. Pons, M. A. Vila and J. Kacprzyk, eds.), Physica Verlag, Heidelberg 2000, pp. 171–190 Zbl 0964.68047
[12] Buckles B. P., Petry F. E.: A fuzzy model for relational databases. Fuzzy Sets and Systems 7 (1982), 213–226 DOI 10.1016/0165-0114(82)90052-5
[13] Buell D. A., Kraft D. H.: A model for a weighted retrieval system. J. Amer. Soc. for Inform. Science 32 (1981), 3, 211–216 DOI 10.1002/asi.4630320307
[14] Crestani F., Lalmas M., Rijsbergen C. J. van, Campbell I.: “Is this document relevant?. .probably”: a survey of probabilistic models in information retrieval. ACM Comput. Surveys 30 (1998), 4, 528–552 DOI 10.1145/299917.299920
[15] (ed.) R. De Caluwe: Fuzzy and Uncertain Object-Oriented Databases: Concepts and Models. Adv. in Fuzzy Systems – Appl. and Theory 13 (1998). World Scientific Pub Co
[16] Dubois D., Prade H.: Tolerant fuzzy pattern matching: an introduction. In: Fuzziness in Database Management Systems (P. Bosc and J. Kacprzyk, eds.), Physica–Verlag (Springer–Verlag) 1995, pp. 42–58
[17] Fuhr N.: Models for retrieval with probabilistic indexing. Inform. Process. Management 25 (1989), 1, 55–72 DOI 10.1016/0306-4573(89)90091-5
[18] Kacprzyk J., Zadrożny S.: Fuzzy querying for Microsoft Access. In: Proceedings of Third IEEE Conference on Fuzzy Systems Orlando 1994, Vol. 1, pp. 167–171
[19] Kacprzyk J., Zadrożny S.: FQUERY for Access: fuzzy querying for a Windows – based DBMS. In: Fuzziness in Database Management Systems (P. Bosc and J. Kacprzyk, eds.), Physica–Verlag, Heidelberg 1995, pp. 415–433
[20] Kacprzyk J., Zadrożny S., Ziółkowski A.: FQUERY III+: a ‘human consistent’ database querying system based on fuzzy logic with linguistic quantifiers. Inform. Systems 6 (1989), 443–453 DOI 10.1016/0306-4379(89)90012-4
[21] Kacprzyk J., Ziółkowski A.: Database queries with fuzzy linguistic quantifiers. IEEE Trans. Systems Man Cybernet. SMC–16 (1986), 474–479 DOI 10.1109/TSMC.1986.4308982
[22] Kraft D. H., Bordogna G., Pasi G.: An extended fuzzy linguistic approach to generalize Boolean information retrieval. J. Inform. Sci. Appl. 2 (1995), 3, 119–134
[23] Kraft D., Bordogna G., Pasi G.: Fuzzy Set Techniques in Information Retrieval. In: Fuzzy Sets in Approximate Reasoning and Information Systems (J. C. Bezdek, D. Dubois and H. Prade, eds.), The Handbooks of Fuzzy Sets Series, Kluwer Academic Publishers, Boston – Dordrecht – London 1999, pp. 469–510 MR 1799009 | Zbl 0949.68527
[24] Motro A.: VAGUE: A user interface to relational database that permits vague queries. ACM Trans. Office Inform. Systems 6 (1988), 3, 187–214 DOI 10.1145/45945.48027
[25] Petry F. E.: Fuzzy Databases. Principles and Applications. Kluwer Academic Publishers, Boston – Dordrecht – London 1996 Zbl 0953.68052
[26] Prade H., Testemale C.: Generalizing database relational algebra for the treatment of incomplete/uncertain information and vague queries. Inform. Sci. 34 (1984), 115–143 DOI 10.1016/0020-0255(84)90020-3 | MR 0769961
[27] Salton G.: Automatic text processing: The transformation, analysis and retrieval of information by computer. Addison Wesley, Reading 1989
[28] Salton G., Buckley C.: Term weighting approaches in automatic text retrieval. Inform. Process. Management 24 (1988), 5, 513–523 DOI 10.1016/0306-4573(88)90021-0
[29] Salton G., McGill M. J.: Introduction to modern information retrieval. McGraw–Hill, New York 1983 Zbl 0523.68084
[30] Shenoi S., Melton A.: Proximity relations in the fuzzy relational database model. Fuzzy Sets and Systems 31 (1989), 285–296 DOI 10.1016/0165-0114(89)90201-7 | MR 1009262 | Zbl 0677.68113
[31] Tahani V.: A conceptual framework for fuzzy query processing: a step toward very intelligent database systems. Inform. Process. Management 13 (1977), 289–303 DOI 10.1016/0306-4573(77)90018-8 | Zbl 0361.68136
[32] Ullman J. D.: Principles of Database Systems. Computer Science Press, Rockville 1982 MR 0669881 | Zbl 0558.68078
[33] Rijsbergen C. J. Van: Information Retrieval. Butterworths & Co., Ltd, London 1979
[34] Yager R. R.: A note on weighted queries in information retrieval systems. J. Amer. Soc. Inform. Sci. 38 (1987), 1, 23–24 DOI 10.1002/(SICI)1097-4571(198701)38:1<23::AID-ASI4>3.0.CO;2-3
[35] Yager R. R.: On ordered weighted averaging aggregation operators in multicriteria decision making. IEEE Trans. Systems Man Cybernet. 18 (1988), 1, 183–190 DOI 10.1109/21.87068 | MR 0931863
[36] Yazici A., Cibiceli D.: An access structure for similarity–based databases. Inform. Sci. 115 (1999), 1–4, 137–163 DOI 10.1016/S0020-0255(98)10079-8
[37] Zadeh L. A.: A computational approach to fuzzy quantifiers in natural languages. Computers and Math. Appl. 9 (1983), 149–184 DOI 10.1016/0898-1221(83)90013-5 | MR 0719073 | Zbl 0517.94028
Partner of
EuDML logo