Hostname: page-component-5b777bbd6c-5mwv9 Total loading time: 0 Render date: 2025-06-14T09:44:12.425Z Has data issue: false hasContentIssue false
  • English
  • Français

Building Rules on Top of Ontologies for the Semantic Web with Inductive Logic Programming

Published online by Cambridge University Press:  01 May 2008

FRANCESCA A. LISI
FRANCESCA A. LISI*
Affiliation:
Dipartimento di Informatica, Università degli Studi di Bari, Via Orabona 4, 70125 Bari, Italy (e-mail: lisi@di.uniba.it)
Get access Rights & Permissions [Opens in a new window]

Abstract

Building rules on top of ontologies is the ultimate goal of the logical layer of the Semantic Web. To this aim, an ad-hoc markup language for this layer is currently under discussion. It is intended to follow the tradition of hybrid knowledge representation and reasoning systems, such as -log that integrates the description logic and the function-free Horn clausal language Datalog. In this paper, we consider the problem of automating the acquisition of these rules for the Semantic Web. We propose a general framework for rule induction that adopts the methodological apparatus of Inductive Logic Programming and relies on the expressive and deductive power of -log. The framework is valid whatever the scope of induction (description versus prediction) is. Yet, for illustrative purposes, we also discuss an instantiation of the framework which aims at description and turns out to be useful in Ontology Refinement.

Keywords

Inductive logic programminghybrid knowledge representation and reasoning systemsontologiessemantic web
Type
Regular Papers
Information
Theory and Practice of Logic Programming , Volume 8 , Issue 3 , May 2008 , pp. 271 - 300
Copyright
Copyright © Cambridge University Press 2008

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Baader, F., Calvanese, D., McGuinness, D., Nardi, D., and Patel-Schneider, P., Eds. 2003. The Description Logic Handbook: Theory, Implementation and Applications. Cambridge University Press, Cambridge.Google Scholar
Berners-Lee, T., Hendler, J., and Lassila, O. 2001. The Semantic Web. Scientific American May.Google Scholar
Bisson, G., Nedellec, C., and Cañamero, D. 2000. Designing clustering methods for ontology building—the Mo'K workbench. In ECAI Workshop on Ontology Learning, Staab, S., Maedche, A., Nedellec, C., and Wiemer-Hastings, P., Eds. CEUR Workshop Proceedings, vol. 31. CEUR-WS.org.Google Scholar
Blockeel, H., De Raedt, L., Jacobs, N., and Demoen, B. 1999. Scaling up inductive logic programming by learning from interpretations. Data Mining and Knowledge Discovery 3, 59–93.Google Scholar
Borgida, A. 1996. On the relative expressiveness of description logics and predicate logics. Artificial Intelligence 82, 1–2, 353–367.Google Scholar
Buntine, W. 1988. Generalized subsumption and its application to induction and redundancy. Artificial Intelligence 36, 2, 149–176.Google Scholar
Ceri, S., Gottlob, G., and Tanca, L. 1990. Logic Programming and Databases. Springer, New York.Google Scholar
De Raedt, L. 1997. Logical settings for concept-learning. Artificial Intelligence 95, 1, 187–201.Google Scholar
De Raedt, L. and Dehaspe, L. 1997. Clausal discovery. Machine Learning 26, 2–3, 99–146.Google Scholar
De Raedt, L. and Džeroski, S. 1994. First order jk-clausal theories are PAC-learnable. Artificial Intelligence 70, 375–392.Google Scholar
Dellschaft, K. and Staab, S. 2006. On how to perform a gold standard based evaluation of ontology learning. In The Semantic Web, Cruz, I., Decker, S., Allemang, D., Preist, C., Schwabe, D., Mika, P., Uschold, M., and Aroyo, L., Eds. Lecture Notes in Computer Science, vol. 4273. Springer, Berlin/Heidelberg, 228–241.Google Scholar
Donini, F., Lenzerini, M., Nardi, D., and Schaerf, A. 1998. -log: Integrating datalog and description logics. Journal of Intelligent Information Systems 10, 3, 227–252.Google Scholar
Fisher, D. 1987. Knowledge acquisition via incremental conceptual clustering. Machine Learning 2, 2, 139–172.Google Scholar
Flach, P. and Lavrač, N. 2002. Learning in clausal logic: A perspective on inductive logic programming. In Computational Logic: Logic Programming and Beyond, Kakas, A. and Sadri, F., Eds. Lecture Notes in Computer Science, vol. 2407. Springer, Berlin/Heidelberg, 437–471.Google Scholar
Frazier, M. and Page, C. 1993. Learnability in inductive logic programming. In Proceedings of the 10st National Conference on Artificial Intelligence. The AAAI Press/The MIT Press, 93–98.Google Scholar
Frisch, A. 1991. The substitutional framework for sorted deduction: Fundamental results on hybrid reasoning. Artificial Intelligence 49, 161–198.Google Scholar
Frisch, A. 1999. Sorted downward refinement: Building background knowledge into a refinement operator for inductive logic programming. In Inductive Logic Programming, Džeroski, S. and Flach, P., Eds. Lecture Notes in Artificial Intelligence, vol. 1634. Springer, Berlin/Heidelberg, 104–115.Google Scholar
Frisch, A. and Cohn, A. 1991. Thoughts and afterthoughts on the 1988 workshop on principles of hybrid reasoning. AI Magazine 11, 5, 84–87.Google Scholar
Gennari, J., Langley, P., and Fisher, D. 1989. Models of incremental concept formation. Artificial Intelligence 40, 1–3, 11–61.Google Scholar
Gómez-Pérez, A., Fernández-López, M., and Corcho, O. 2004. Ontological Engineering. Springer, London.Google Scholar
Gruber, T. 1993. A translation approach to portable ontology specifications. Knowledge Acquisition 5, 199–220.Google Scholar
Halkidi, M., Batistakis, Y., and Vazirgiannis, M. 2001. On clustering validation techniques. Journal of Intelligent Information Systems 17, 2–3, 107–145.Google Scholar
Han, J. and Fu, Y. 1999. Mining multiple-level association rules in large databases. IEEE Transactions on Knowledge and Data Engineering 11, 5, 798–804.Google Scholar
Hartigan, J. 2001. Statistical clustering. In International Encyclopedia of the Social and Behavioral Sciences, Smelser, N. and Baltes, P., Eds. Oxford Press, Oxford, 15014–15019.Google Scholar
Horrocks, I., Patel-Schneider, P., and van Harmelen, F. 2003. From and RDF to OWL: The making of a web ontology language. Journal of Web Semantics 1, 1, 7–26.Google Scholar
Horrocks, I., Sattler, U., and Tobies, S. 2000. Practical reasoning for very expressive description logics. Logic Journal of the IGPL 8, 3, 239–263.Google Scholar
Kietz, J. 2003. Learnability of description logic programs. In Inductive Logic Programming, Matwin, S. and Sammut, C., Eds. Lecture Notes in Artificial Intelligence, vol. 2583. Springer, Berlin/Heidelberg, 117–132.Google Scholar
Langley, P. 1987. Machine learning and concept formation. Machine Learning 2, 2, 99–102.Google Scholar
Levy, A. and Rousset, M.-C. 1998. Combining Horn rules and description logics in CARIN. Artificial Intelligence 104, 165–209.Google Scholar
Lisi, F. 2006. Practice of inductive reasoning on the semantic web: A system for semantic web mining. In Principles and Practice of Semantic Web Reasoning, Alferes, J., Bailey, J., May, W. and Schwertel, U., Eds. Lecture Notes in Computer Science, vol. 4187. Springer, Berlin/Heidelberg, 242–256.Google Scholar
Lisi, F. and Esposito, F. 2004. Efficient evaluation of candidate hypotheses in -log. In Inductive Logic Programming, Camacho, R., King, R., and Srinivasan, A., Eds. Lecture Notes in Artificial Intelligence, vol. 3194. Springer, Berlin, 216–233.Google Scholar
Lisi, F. and Esposito, F. 2006. Two orthogonal biases for choosing the intensions of emerging concepts in ontology refinement. In ECAI 2006. Proceedings of the 17th European Conference on Artificial Intelligence, Brewka, G., Coradeschi, S., Perini, A., and Traverso, P., Eds. IOS Press, Amsterdam, 765–766.Google Scholar
Lisi, F. and Esposito, F. 2007. On the missing link between frequent pattern discovery and concept formation. In Inductive Logic Programming, Muggleton, S., Otero, R., and Tamaddoni-Nezhad, A., Eds. Lecture Notes in Artificial Intelligence, vol. 4455. Springer, Berlin/Heidelberg, 305–319.Google Scholar
Lisi, F. and Malerba, D. 2003a. Bridging the gap between horn clausal logic and description logics in inductive learning. In AI*IA 2003: Advances in Artificial Intelligence, Cappelli, A. and Turini, F., Eds. Lecture Notes in Artificial Intelligence, vol. 2829. Springer, Berlin/Heidelberg, 49–60.Google Scholar
Lisi, F. and Malerba, D. 2003b. Ideal refinement of descriptions in -log. In Inductive Logic Programming, Horvath, T. and Yamamoto, A., Eds. Lecture Notes in Artificial Intelligence, vol. 2835. Springer, Berlin/Heidelberg, 215–232.Google Scholar
Lisi, F. and Malerba, D. 2004. Inducing multi-level association rules from multiple relations. Machine Learning 55, 175–210.Google Scholar
Maedche, A. and Staab, S. 2000. Discovering conceptual relations from text. In Proceedings of the 14th European Conference on Artificial Intelligence, Horn, W., Ed. IOS Press, Amsterdam, 321–325.Google Scholar
Maedche, A. and Staab, S. 2004. Ontology learning. In Handbook on Ontologies, Staab, S. and Studer, R., Eds. Springer, Berlin.Google Scholar
Maedche, A. and Zacharias, V. 2002. Clustering ontology-based metadata in the semantic web. In Principles of Data Mining and Knowledge Discovery, Elomaa, T., Mannila, H., and Toivonen, H., Eds. Lecture Notes in Computer Science, vol. 2431. Springer, Berlin/Heidelberg, 348–360.Google Scholar
Mannila, H. and Toivonen, H. 1997. Levelwise search and borders of theories in knowledge discovery. Data Mining and Knowledge Discovery 1, 3, 241–258.Google Scholar
Michalski, R. and Stepp, R. 1983. Learning from observation: Conceptual clustering. In Machine Learning: An Artificial Intelligence Approach, Michalski, R., Carbonell, J., and Mitchell, T., Eds. Vol. I. Morgan Kaufmann, San Mateo, CA.Google Scholar
Mitchell, T. 1982. Generalization as search. Artificial Intelligence 18, 203–226.Google Scholar
Motik, B., Sattler, U., and Studer, R. 2004. Query answering for OWL-DL with rules. In The Semantic Web, McIlraith, S., Plexousakis, D., and van Harmelen, F., Eds. Lecture Notes in Computer Science, vol. 3298. Springer, Berlin/Heidelberg, 549–563.Google Scholar
Nédellec, C., Rouveirol, C., Adé, H., Bergadano, F., and Tausend, B. 1996. Declarative bias in ILP. In Advances in Inductive Logic Programming, Raedt, L. D., Ed. IOS Press, Amsterdam, 82–103.Google Scholar
Nienhuys-Cheng, S. and de Wolf, R. 1997. Foundations of Inductive Logic Programming. Lecture Notes in Artificial Intelligence, vol. 1228. Springer, New York.Google Scholar
Reiter, R. 1980. Equality and domain closure in first order databases. Journal of ACM 27, 235–249.Google Scholar
Rosati, R. 2005. On the decidability and complexity of integrating ontologies and rules. Journal of Web Semantics 3, 1, 61–73.Google Scholar
Rouveirol, C. and Ventos, V. 2000. Towards Learning in CARIN-. In Inductive Logic Programming, Cussens, J. and Frisch, A., Eds. Lecture Notes in Artificial Intelligence, vol. 1866. Springer, Berlin/Heidelberg, 191–208.Google Scholar
Schmidt-Schauss, M. and Smolka, G. 1991. Attributive concept descriptions with complements. Artificial Intelligence 48, 1, 1–26.Google Scholar
Semeraro, G., Esposito, F., Malerba, D., Fanizzi, N., and Ferilli, S. 1998. A logic framework for the incremental inductive synthesis of Datalog theories. In Proceedings of 7th International Workshop on Logic Program Synthesis and Transformation, Fuchs, N., Ed. Lecture Notes in Computer Science, vol. 1463. Springer, Berlin, 300–321.Google Scholar
Staab, S. and Studer, R., Eds. 2004. Handbook on Ontologies. International Handbooks on Information Systems. Springer, Berlin.Google Scholar
Stumme, G. 2004. Iceberg query lattices for Datalog. In Conceptual Structures at Work, Wolff, K., Pfeiffer, H., and Delugach, H., Eds. Lecture Notes in Artificial Intelligence, vol. 3127. Springer, Berlin/Heidelberg, 109–125.Google Scholar
Utgoff, P. and Mitchell, T. 1982. Acquisition of appropriate bias for inductive concept learning. In Proceedings of the 2nd National Conference on Artificial Intelligence. Morgan Kaufmann, Los Altos, CA, 414–418.Google Scholar
Vrain, C. 1996. Hierarchical conceptual clustering in a first order representation. In Foundations of Intelligent Systems, Ras, Z. and Michalewicz, M., Eds. Lecture Notes in Computer Science, vol. 1079. Springer, Berlin, 643–652.Google Scholar
Xiong, H., Steinbach, M., Ruslim, A., and Kumar, V. 2005. Characterizing pattern-based clustering. Technical Report TR 05-015, Dept. of Computer Science and Engineering, University of Minnesota, Minneapolis, USA.Google Scholar
Zimmermann, A. and Raedt, L. D. 2004. Cluster-grouping: From subgroup discovery to clustering. In Machine Learning: ECML 2004, Boulicaut, J.-F., Esposito, F., Giannotti, F., and Pedreschi, D., Eds. Lecture Notes in Artificial Intelligence, vol. 3201. Springer, Berlin/Heidelberg, 575–577.Google Scholar