On multi-subjectivity in linguistic summarization of relational databases

• Autorzy: Niewiadomski A. , Superson I.
• Języki publikacji: EN

Abstrakty

EN

We focus on one of the most powerful computing methods for natural-language-driven representation of data, i.e. on Yager’s concept of a linguistic summary of a relational database (1982). In particular, we introduce an original extension of that concept: new forms of linguistic summaries. The new forms are named Multi-Subject linguistic summaries, because they are constructed to handle more than one set of subjects, represented by related sets of records/objects collected in a database, like ”cars, bicycles and motorbikes” (within vehicles), ”male and female” (within people), e.g. More boys than girls play football well. Thanks to that, the generated linguistic summaries – quasi-natural language sentences – are more interesting and human-oriented. Moreover, they can be applied together with the classic forms od summaries, to enrich naturality of comments/descriptions generated. Apart from traditional interpretions linguistic summaries in terms of fuzzy logic, we also introduce some higher-order fuzzy logic methods, to extend possibilities of representing too complex or too ill-defined linguistic terms used in generated messages. The new methods are applied to a computer system that generates natural language description of numeric data, that makes them possible to be clearly presented to an end-user.

Słowa kluczowe

EN

Multi-Subjectivity in relational databases; linguistic summaries of databases; Multi-Subject linguistic; summaries; fuzzy sets

• Wydawca: Komisja Informatyki Polskiej Akademii Nauk, Oddział w Gdańsku
• Czasopismo: Journal of Theoretical and Applied Computer Science
• Rocznik: 2014
• Tom: Vol. 8, nr 1
• Strony: 15--34
• Opis fizyczny: Bibliogr. 22 poz., rys., tab.

Twórcy

autor

Niewiadomski A.

• Institute of Information Technology, Lodz University of Technology

autor

Superson I.

• Institute of Information Technology, Lodz University of Technology

Bibliografia

• [1] Yager, R. R.: A new approach to the summarization of data. Information Sciences, 28, pp. 69–86, 1982.
• [2] Zadeh, L. A.: A computational approach to fuzzy quantifiers in natural languages. Computers and Maths with Applications, 9, pp. 149–184, 1983.
• [3] Yager, R. R., Ford, M., Canas, A. J.: An Approach To The Linguistic Summarization of Data. In: Proceedings of 3rd International Conference, Information Processing and Management of Uncertainty in Knowledge-Based System, Paris, France, pp. 456–468. 1990.
• [4] George, R., Srikanth, R.: Data Summarization Using Genetic Algorithms and Fuzzy Logic. In: Herrera, F., Verdegay, J. L. (eds.), Genetic Algorithms and Soft Computing, pp. 599–611. Physica–Verlag, Heidelberg, 1996.
• [5] Kacprzyk, J., Yager, R. R.: Linguistic summaries of data using fuzzy logic. International Journal of General Systems, 30, pp. 133–154, 2001.
• [6] Kacprzyk, J., Yager, R. R., Zadrożny, S.: A fuzzy logic based approach to linguistic summaries of databases. International Journal of Applied Mathematics and Computer Sciences, 10, pp. 813–834, 2000.
• [7] Yager, R. R.: On ordered weighted averaging operators in multicriteria decision making. IEEE Transactions on Systems, Man, and Cybernetics, 18, pp. 183–190, 1988.
• [8] Niewiadomski, A.: Six new informativeness indices of data linguistic summaries. In: Szczepaniak, P. S., Wgrzyn Wolska, K. (eds.), Advances in Intelligent Web Mastering, pp. 254–259.Springer-Verlag, 2007.
• [9] Niewiadomski, A.: News Generating via Fuzzy Summarization of Databases. Lecture Notes in Computer Science, 3831, pp. 419–429, 2006.
• [10] Zadrożny, S.: Imprecise queries and linguistic summaries of databases. Academic Publishing House EXIT, Warsaw, 2006. (in Polish).
• [11] Bosc, P., Pivert, O.: Fuzzy querying in conventional databases. In: Zadeh, L. A., Kacprzyk, J. (eds.), Fuzzy Logic for the Management of Uncertainty, pp. 645–671. Wiley, New York, 1992.
• [12] Raschia, G., Mouaddib, N.: SAINTETIQ: a fuzzy set-based approach to database summarization. Fuzzy Sets and Systems, 129, pp. 137–162, 2002.
• [13] Rasmussen, D., Yager, R. R.: A fuzzy SQL summary language for data discovery. In: Dubois, D., Prade, H., Yager, R. R. (eds.), Fuzzy Information Engineering: A Guided Tour of Application’s, pp. 253–264. Wiley, New York, 1997.
• [14] Srikanth, R., Agrawal, R.: Mining quantitative association rules in large relational databases. In: The 1996 ACM SIGMOD International Conference on Management of Data, pp. 1–12. 1996.
• [15] Zadrozny, S., Nowacka, K.: Fuzzy information retrieval model revisited. Fuzzy Sets and Systems, 160(15), pp. 2173–2191, 2009.
• [16] Codd, E. F.: A Relational Model of Data for Large Shared Data Banks. Communications of the ACM, 13(6), pp. 377–387, 1970.
• [17] Niewiadomski, A.: A Type-2 Fuzzy Approach to Linguistic Summarization of Data. IEEE Transactions on Fuzzy Systems, 16(1), pp. 198–212, 2008.
• [18] Niewiadomski, A.: On Finity, Countability, Cardinalities, And Cylindric Extensions of Type-2 Fuzzy Sets in Linguistic Summarization of Databases. IEEE Transactions on Fuzzy Systems, 18(3), pp. 532–545, 2010.
• [19] Niewiadomski, A.: Methods for the Linguistic Summarization of Data: Applications of Fuzzy Sets and Their Extensions. Academic Publishing House EXIT, 2008.
• [20] Wu, D., Mendel, J. M.: Linguistic summarization using IFTHEN rules and interval type-2 fuzzy sets. IEEE Transactions on Fuzzy Systems, 19(1), pp. 136–151, 2011.
• [21] Pilarski, D.: Linguistic summarization of databases with quantirius: a reduction algorithm for generated summaries. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 18, p. 305, 2010.
• [22] A. Wilbik, J. M. K.: A distance metric for a space of linguistic summaries. Fuzzy Sets and Systems, 208, pp. 79–94, 2012.