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Keywords:
query language; heterogeneously structured document
query language; heterogeneously structured document
Summary:
In this paper we present a fuzzy model for representing documents having a hierarchical structure and possibly containing multimedia information. We consider an archive containing documents with distinct (heterogeneous) logical structures. We also propose a flexible query language for expressing soft selection conditions on the structured documents. The documents’ content is organized into thematic (topical) sections where the index terms play a distinct role. The proposed document representation is adaptive to the user, who can indicate the preferred sections of documents, i. e. those which they estimate to bear the most interesting information, and can linguistically quantify the number of sections which determine the global potential interest of the documents. Linguistic quantifiers in the query specify the approximate number of the sections in which the query terms should appear.
References:
[1] Bookstein A.: Fuzzy requests: an approach to weighted Boolean searches. J. Amer. Soc. Inform. Science 31 (1980), 240–247 DOI 10.1002/asi.4630310403
[2] Bordogna G., Pasi G.: A fuzzy linguistic approach generalizing Boolean IR: 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
[3] Bordogna G., Pasi G.: Controlling 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 | Zbl 0870.68058
[4] Bordogna G., Pasi G.: Linguistic aggregation operators of selection criteria in fuzzy information retrieval. Internat. J. Intelligent Systems 10 (1995), 233–248 DOI 10.1002/int.4550100205
[5] Chiaramella Y., Kheirbek A.: An integrated model for hypermedia and information retrieval. In: Information Retrieval and Hypertext (M. Agosti and A. Smeaton, eds.), 1996, pp. 136–176
[6] H. D. A. Buell D., Kraft: Threshold values and Boolean retrieval systems. Inform. Process. Management 17 (1981), 127–136 DOI 10.1016/S0306-4573(81)80004-0 | Zbl 0456.68134
[7] al V. Christophides et: From structured documents to novel query facilities. In: Proc. ACM SIGMOD Internat. Conf. on Management of Data. ACM Press, Minneapolis 1994
[8] Florescu D., Manolescu I., Kossmann D.: Storing and querying XML data using an RDBMS. IEEE Data Engineering Bulletin 22 (1999), 3, 27–34
[9] Kim H., Cho S.: Structured storage and retrieval of SGML documents using GROVE. Inform. Process. Management 36 (2000), 643–657 DOI 10.1016/S0306-4573(99)00075-8
[10] Krovetz R., Croft W. B.: Lexical ambiguity and information retrieval. ACM Trans. Information System 10 (1992), 2, 115–141 DOI 10.1145/146802.146810
[11] Klir G. J., Folger T. A.: Fuzzy Sets, Uncertainty and Information. Prentice Hall PTR Englewood Cliffs, 1998 MR 0930102 | Zbl 0675.94025
[12] Kraft D. H., Bordogna G., Pasi G.: An extended fuzzy linguistic approach to generalize Boolean information retrieval. J. Inform. Sciences Appl. 2 (1995), 3, 119–134 MR 1327861
[13] Lalmas M., Ruthven I.: Representing and retrieving structured documents using the Dempster–Shafer theory of evidence: Modelling and Evaluation. J. Documentation 54 (1998), 5, 529–565 DOI 10.1108/EUM0000000007180
[14] Macleod I.: Storage and retrieval of structured documents. Inform. Process. Management 26 (1990), 2, 197–208 DOI 10.1016/0306-4573(90)90025-W
[15] Molinari A., Pasi G.: A fuzzy representation of HTML documents for information retrieval systems: In: Proc. IEEE Internat. Conf. on Fuzzy Systems, New Orleans 1996
[16] Negoita C. V.: On the notion of relevance in information retrieval. Kybernetes 2 (1973), 3, 161–165 DOI 10.1108/eb005334 | Zbl 0278.68087
[17] Paice C. D.: Soft evaluation of Boolean search queries in information retrieval systems. Information Technology: Research Development Applications 3 (1984), 1, 33–41
[18] Papakonstantinou Y., Widom J., Molina H. G.: Object exchange and heterogeneous information sources. In: Proc. IEEE Internat. Conf. on Engineering, Birmingham 1996
[19] Paradis F., Berrut C.: Experiments with theme extraction in explanatory texts. In: Proc. II Internat. Conf. on Conceptions of Library and Information (CoLIB 2), Copenhagen 1996, pp. 13–16, 433–446
[20] Perez–Carballo J., Strzalkowski T.: Natural language information retrieval: Progress report. Inform. Process. Management 36 (2000), 155–178 DOI 10.1016/S0306-4573(99)00049-7
[21] al A. Rao et: Query Processing in TREC-6. Inform. Process. Management 36 (2000), 179–186 DOI 10.1016/S0306-4573(99)00050-3
[22] Sager N.: Natural Language Information Processing. Addison Wesley, 1981
[23] Salton G., Fox E., Wu H.: Extended Boolean information retrieval. Comm. ACM 26 (1983), 12, 1022–1036 DOI 10.1145/182.358466 | MR 0784124 | Zbl 0519.68089
[24] Salton G., McGill M. J.: Introduction to modern information retrieval. McGraw–Hill Internat. Book Co., 1984 Zbl 0523.68084
[25] Jones K. A. Sparck: Automatic Keyword Classification for Information Retrieval. Butterworths, London 1971
[26] Jones K. A. Sparck: A statistical interpretation of term specificity and its application in retrieval. J. Documentation 28 (1972), 1, 11–20 DOI 10.1108/eb026526
[27] Rijsbergen C. J. van: Information Retrieval. Butterworths, London 1979
[28] Yager R. R.: On ordered weighted averaging aggregation operators in multi criteria decision making. IEEE Trans. Systems Man Cybernet. 18 (1988), 1, 183–190 DOI 10.1109/21.87068 | MR 0931863
[29] Yager R. R, (eds.) J. Kacprzyk: The Ordered Weighted Averaging Operators: Theory and Applications. Kluwer, Dordrecht 1997
[30] Zadeh L. A.: Fuzzy sets. Inform. and Control 8 (1965), 338–353 DOI 10.1016/S0019-9958(65)90241-X | MR 0219427 | Zbl 0139.24606
[31] Zadeh L. A.: A computational approach to fuzzy quantifiers in natural languages. Comput. Math. Appl. 9 (1983), 149–184 DOI 10.1016/0898-1221(83)90013-5 | MR 0719073 | Zbl 0517.94028
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