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A Logic-based Computational Method for the Automated Induction of Fuzzy Ontology Axioms

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Języki publikacji
EN
Abstrakty
EN
Fuzzy Description Logics (DLs) are logics that allow to deal with structured vague knowledge. Although a relatively important amount of work has been carried out in the last years concerning the use of fuzzy DLs as ontology languages, the problem of automatically managing the evolution of fuzzy ontologies has received very little attention so far. We describe here a logic-based computational method for the automated induction of fuzzy ontology axioms which follows the machine learning approach of Inductive Logic Programming. The potential usefulness of the method is illustrated by means of an example taken from the tourism application domain.
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Rocznik
Strony
503--519
Opis fizyczny
Bibliogr. 30 poz., wykr.
Twórcy
autor
  • Dipartimento di Informatica, Università degli Studi di Bari 'Aldo Moro', Italy
autor
  • ISTI - CNR, Pisa, Italy
Bibliografia
  • [1] Baader, F., Calvanese, D., McGuinness, D., Nardi, D., Patel-Schneider, P., Eds.: The Description Logic Handbook: Theory, Implementation and Applications (2nd ed.), Cambridge University Press, 2007.
  • [2] Borgida, A.: On the Relative Expressiveness of Description Logics and Predicate Logics, Artificial Intelligence, 82(1-2), 1996, 353-367.
  • [3] Calvanese, D., De Giacomo, G., Lembo, D., Lenzerini, M., Poggi, A., Rosati, R., Ruzzi, M.: Data Integration through DL-LiteA Ontologies, Semantics in Data and Knowledge Bases, Third International Workshop, SDKB 2008, Nantes, France, March 29, 2008, Revised Selected Papers (K.-D. Schewe, B. Thalheim, Eds.), number 4925 in Lecture Notes in Computer Science, Springer Verlag, 2008.
  • [4] Calvanese, D., De Giacomo, G., Lembo, D., Lenzerini, M., Rosati, R.: Data Complexity of Query Answering in Description Logics., Proc. of the 2005 Int. Workshop on Description Logics (I. Horrocks, U. Sattler, F. Wolter, Eds.), 147, CEUR-WS.org, 2005.
  • [5] Calvanese, D., Lenzerini, M., Rosati, R., Vetere, G.: DL-Lite: Practical Reasoning for Rich DLs, Proc. of the 2004 Int. Workshop on Description Logics (V. Haarslev, R. Moller, Eds.), 104, CEUR-WS.org, 2004.
  • [6] Cerami, M., Straccia, U.: On the Undecidability of Fuzzy Description Logics with GCIs with Lukasiewicz t-norm, Technical report, Computing Research Repository, 2011.
  • [7] De Raedt, L., Thon, I.: Probabilistic Rule Learning, Inductive Logic Programming - 20th International Conference, ILP2010, Florence, Italy, June 27-30, 2010. Revised Papers (P. Frasconi, F. A. Lisi, Eds.), 6489, Springer, 2011, ISBN 978-3-642-21294-9.
  • [8] Drobics, M., Bodenhofer, U., Klement, E.-P.: FS-FOIL: an inductive learning method for extracting interpretable fuzzy descriptions, Int. J. Approximate Reasoning, 32(2-3), 2003, 131-152.
  • [9] Dubois, D., Prade, H.: Possibility Theory, Probability Theory and Multiple-Valued Logics: A Clarification, Annals of Mathematics and Artificial Intelligence, 32(1-4), 2001, 35-66, ISSN 1012-2443.
  • [10] Fanizzi, N., d’Amato, C., Esposito, F.: DL-FOIL Concept Learning in Description Logics, Inductive Logic Programming (F. Zelezny, N. Lavrac, Eds.), 5194, Springer, 2008.
  • [11] Hajek, P.: Metamathematics of Fuzzy Logic, Kluwer, 1998.
  • [12] Hellmann, S., Lehmann, J., Auer, S.: Learning of OWL Class Descriptions on Very Large Knowledge Bases, International Journal on Semantic Web and Information Systems, 5(2), 2009, 25-48.
  • [13] Horvath, T., Vojtas, P.: Induction of Fuzzy and Annotated Logic Programs, Inductive Logic Programming, 16th International Conference, ILP 2006, Santiago de Compostela, Spain, August 24-27, 2006, Revised Selected Papers (S. Muggleton, R. P. Otero, A. Tamaddoni-Nezhad, Eds.), 4455, Springer, 2007.
  • [14] Iglesias, J., Lehmann, J.: Towards Integrating Fuzzy Logic Capabilities into an Ontology-based Inductive Logic Programming Framework, Proc. of the 11th Int. Conf. on Intelligent Systems Design and Applications, IEEE Press, 2011.
  • [15] Klir, G. J., Yuan, B.: Fuzzy sets and fuzzy logic: theory and applications, Prentice-Hall, Inc., Upper Saddle River, NJ, USA, 1995, ISBN 0-13-101171-5.
  • [16] Konstantopoulos, S., Charalambidis, A.: Formulating description logic learning as an Inductive Logic Programming task, Proc. of the 19th IEEE Int. Conf. on Fuzzy Systems, IEEE Press, 2010.
  • [17] Lisi, F. A., Straccia, U.: An Inductive Logic Programming Approach to Learning Inclusion Axioms in Fuzzy Description Logics, Proc. of the 26th Italian Conference on Computational Logic (F. Fioravanti, Ed.), 810, CEUR-WS.org, 2011.
  • [18] Lisi, F. A., Straccia, U.: Towards Learning Fuzzy DL Inclusion Axioms, Fuzzy Logic and Applications - 9th International Workshop, WILF 2011, Trani, Italy, August 29-31,2011. Proceedings (A. M. Fanelli, W. Pedrycz, A. Petrosino, Eds.), 6857, Springer, 2011, ISBN 978-3-642-23712-6.
  • [19] Lukasiewicz, T., Straccia, U.: Managing Uncertainty and Vagueness in Description Logics for the Semantic Web, Journal of Web Semantics, 6, 2008, 291-308.
  • [20] Motik, B., Rosati, R.: A Faithful Integration of Description Logics with Logic Programming, IJCAI2007, Proc. of the 20th Int. Joint Conf. on Artificial Intelligence (M. Veloso, Ed.), 2007.
  • [21] Nienhuys-Cheng, S., de Wolf, R.: Foundations of Inductive Logic Programming, vol. 1228 of Lecture Notes in Artificial Intelligence, Springer, 1997.
  • [22] Ochoa Luna, J. E., Gagliardi Cozman, F.: An Algorithm for Learning with Probabilistic Description Logics, Proceedings of the Fifth International Workshop on Uncertainty Reasoning for the Semantic Web (URSW 2009), collocated with the 8th International Semantic Web Conference (ISWC-2009), Washington DC, USA, October 26, 2009 (F. Bobillo, P. C. G. da Costa, C. d’Amato, N. Fanizzi, K. B. Laskey, K. J. Laskey, T. Lukasiewicz, T. Martin, M. Nickles, M. Pool, P. Smrz, Eds.), 527, CEUR-WS.org, 2009.
  • [23] Poggi, A., Lembo, D., Calvanese, D., De Giacomo, G., Lenzerini, M., Rosati, R.: Linking Data to Ontologies, Journal of Data Semantics, 10, 2008, 133-173.
  • [24] Quinlan, J.: Learning Logical Definitions from Relations, Machine Learning, 5, 1990, 239-266.
  • [25] Reiter, R.: Equality and Domain Closure in First Order Databases, Journal of ACM, 27, 1980, 235-249.
  • [26] Serrurier, M., Prade, H.: Improving Expressivity of Inductive Logic Programming by Learning Different Kinds of Fuzzy Rules, Soft Computing, 11(5), 2007, 459-466.
  • [27] Shibata, D., Inuzuka, N., Kato, S., Matsui, T., Itoh, H.: An Induction Algorithm Based on Fuzzy Logic Programming, Methodologies for Knowledge Discovery and Data Mining, Third Pacific-Asia Conference, PAKDD-99, Beijing, China, April 26-28,1999, Proceedings (N. Zhong, L. Zhou, Eds.), 1574, Springer, 1999.
  • [28] Straccia, U.: Reasoning within Fuzzy Description Logics, Journal of Artificial Intelligence Research, 14, 2001, 137-166.
  • [29] Straccia, U.: SoftFacts: A Top-k Retrieval Engine for Ontology Mediated Access to Relational Databases, Proc. of the 2010 IEEE Int. Conf. on Systems, Man and Cybernetics, IEEE Press, 2010.
  • [30] Straccia, U.: Top-k Retrieval for Ontology Mediated Access to Relational Databases, Information Sciences, 198, 2012, 1-23.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-12aec2ec-2700-472e-967a-5c64dddf7e0d
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