Advertisement

Applied Intelligence

, Volume 26, Issue 2, pp 139–159 | Cite as

An algorithm based on counterfactuals for concept learning in the Semantic Web

  • Luigi Iannone
  • Ignazio Palmisano
  • Nicola Fanizzi
Article

Abstract

In the line of realizing the Semantic-Web by means of mechanized practices, we tackle the problem of building ontologies, assisting the knowledge engineers’ job by means of Machine Learning techniques. In particular, we investigate on solutions for the induction of concept descriptions in a semi-automatic fashion. In particular, we present an algorithm that is able to infer definitions in the \(\mathcal{ALC}\) Description Logic (a sub-language of OWL-DL) from instances made available by domain experts. The effectiveness of the method with respect to past algorithms is also empirically evaluated with an experimentation in the document image understanding domain.

Keywords

Ontology learning Refinement operators Inductive reasoning Machine learning Knowledge management Ontology evolution 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Irina Astrova (2004) Reverse engineering of relational databases to ontologies. In: Christoph Bussler, John Davies, Dieter Fensel, Rudi Studer (eds), ESWS, vol 3053 of lecture notes in computer science, Springer, pp 327–341Google Scholar
  2. 2.
    Baader F, Calvanese D, McGuinness D, Nardi D, Patel-Schneider P (eds) (2003) The description logic handbook. Cambridge University PressGoogle Scholar
  3. 3.
    Baader F, Küsters R (2005) Non-standard inferences in description logics: The story so far. In: Gabbay D, Goncharov SS, Zakharyaschev M (eds), Mathematical problems from applied logic. New logics for the XXIst century, vol 4 of International mathematical series. Kluwer/Plenum Publishers (To appear)Google Scholar
  4. 4.
    Badea L, Nienhuys-Cheng S-H (2000) A refinement operator for description logics. In: Cussens J, Frisch A (eds) Proceedings of the 10th international conference on inductive logic programming, vol 1866 of LNAI, pp 40–59Google Scholar
  5. 5.
    Liviu Badea, Monica Stanciu (1999) Refinement operators can be (weakly) perfect. In: Saso Dzeroski, Peter AF (eds) ILP, vol 1634 of lecture notes in computer science, Springer, pp 21–32Google Scholar
  6. 6.
    Berners-Lee T, Hendler J, Lassila O (2001) The semantic web. Scientific AmericanGoogle Scholar
  7. 7.
    Alex Borgida, Thomas JW, Haym Hirsh (2005) Towards measuring similarity in description logics. In: Ian Horrocks, Ulrike Sattler, Frank Wolter (eds) Description logics 2005. Proceedings of the 2005 international workshop on description logics (DL-2005), Edinburgh, UK, vol 147. CEUR-WSGoogle Scholar
  8. 8.
    Cohen WW, Hirsh H (1994) The learnability of description logics with equality constraints. Mach Learn 17(2–3):169–199zbMATHGoogle Scholar
  9. 9.
    Cohen WW, Hirsh H (1994) Learning the classic description logic: Theoretical and experimental results. In: Torasso P, Doyle J, Sandewall E (eds) Proceedings of the 4th international conference on the principles of knowledge representation and reasoning, Morgan Kaufmannpp, pp 121–133Google Scholar
  10. 10.
    d’Amato C, Fanizzi N, Esposito F (2005) A semantic similarity measure for expressive description logics. In: Pettorossi A (ed) Proceedings of convegno Italiano di logica computazionale (CILC05), Rome, Italy, http://www.disp.uniroma2.it/CILC2005/downloads/papers/15.dAmato_CILC05.%pdf.Google Scholar
  11. 11.
    d’Amato C, Fanizzi N, Esposito F (2006) A dissimilarity measure for ALC concept descriptions. In: Proceedings of the 21st annual ACM symposium of applied computing, SAC2006, Dijon, FranceGoogle Scholar
  12. 12.
    Dietterich TG, London RL, Clarkson K, Dromey G (1982) The Handbook of artificial intelligence, vol III, Chapter XIV— Learning and inductive inference, William Kaufmann, Los Altos, CA, pp 323–512Google Scholar
  13. 13.
    Donini FM, Lenzerini M, Nardi D, Nutt W (1998) An epistemic operator for description logics. Artif Intell 100(1–2):225–274zbMATHCrossRefMathSciNetGoogle Scholar
  14. 14.
    Floriana Esposito, Nicola Fanizzi, Luigi Iannone, Ignazio Palmisano, Semeraro G (2004) Induction and revision of terminologies. In: Lorenza Saitta (ed) Proceedings of the 16th European Conference on Artificial Intelligence (ECAI), IOS Press, pp 1007–1008Google Scholar
  15. 15.
    Floriana Esposito, Nicola Fanizzi, Luigi Iannone, Ignazio Palmisano, Giovanni Semeraro (2005) A counterfactual-based learning algorithm for description logic. In: Proceedings of the Italian conference on artificial intelligence, AI*IA2005, pp 406–417Google Scholar
  16. 16.
    Floriana Esposito, Donato Malerba, Francesca A. Lisi (2000) Machine learning for intelligent processing of printed documents. J Intell Inf Syst 14(2–3):175–198Google Scholar
  17. 17.
    Stefano Ferilli, Nicola Di Mauro, Teresa Maria Altomare Basile, Floriana Esposito (2003) Incremental induction of rules for document image understanding. In: Cappelli A, Turini F (eds) Proceedings of the conference of the Italian association for artificial intelligence, AI*IA, vol 2829 of Lecture notes in computer science, pp 176–188Google Scholar
  18. 18.
    Gerhard Friedrich, Kostyantyn Shchekotykhin (2005) A general diagnosis method for ontologies. In: Gil Y, Motta V, Benjamins E, Mark A Musen (eds) Proceedings of the 4th international semantic web conference, ISWC2005, no 3279 in LNCS, Galway, Ireland, pp 232–246Google Scholar
  19. 19.
    Thomas R Gruber (1993) A Translation Approach to Portable Ontology Specifications. Academic PressGoogle Scholar
  20. 20.
    Peter Haase, Frank van Harmelen, Zhisheng Huang, Heiner Stuckenschmidt, York Sure (2005) A framework for handling inconsistency in changing ontologies. In: Gil Y, Motta V, Benjamins E, Mark A Musen (eds) Proceedings of the 4th international semantic web conference, ISWC2005, no 3279 in LNCS, Galway, Ireland, pp 353–367Google Scholar
  21. 21.
    Ian Horrocks, Peter F Patel-Schneider, Frank van Harmelen (2003) From SHIQ and RDF to OWL: The making of a web ontology language. J Web Semantics 1(1):7–26Google Scholar
  22. 22.
    Luigi Iannone, Ignazio Palmisano (2005) An algorithm based on counterfactuals for concept learning in the semantic web. In: Moonis Ali, Floriana Esposito (eds) Innovations in applied artificial intelligence, 18th international conference on industrial and engineering applications of artificial intelligence and expert systems, IEA/AIE 2005, vol 3533 of Lecture notes in computer science, pp 370–379Google Scholar
  23. 23.
    Kietz J-U (2002) Learnability of description logic programs. In: Matwin S, Sammut C (eds) Proceedings of the 12th international conference on inductive logic programming, vol 2583 of LNAI, Sydney, pp 117–132Google Scholar
  24. 24.
    Alexander Maedche, Steffen Staab (2001) Ontology learning for the semantic web. IEEE Intell Syst 16(2):March/AprilGoogle Scholar
  25. 25.
    Michalski RS, Carbonell JG, Mitchell TM (eds) (1984) Machine learning: An artificial intelligence approach, Chapter a theory and methodology of inductive learning, Berlin, Heidelberg pp 83–130Google Scholar
  26. 26.
    Tom Mitchell (1982) Generalization as search. Artif Intell, Pages 203–226Google Scholar
  27. 27.
    RDF-Schema (2003) RDF Vocabulary Description Language 1.0: RDF Schema. Available at: http://www.w3c.org/TR/rdf-schemaGoogle Scholar
  28. 28.
    Rouveirol C, Ventos V (2000) Towards learning in CARIN-ALN. In: Cussens J, Frisch A (eds) Proceedings of the 10th international conference on inductive logic programming, vol 1866 of LNAI, pp 191–208Google Scholar
  29. 29.
    Schmidt-Schauß M, Smolka G (1991) Attributive concept descriptions with complements. Artif Intell 48(1):1–26zbMATHCrossRefGoogle Scholar
  30. 30.
    Teege G (1994) A subtraction operation for description logics. In: Torasso P, Doyle J, Sandewall E (eds) Proceedings of the 4th international conference on principles of knowledge representation and reasoning, Morgan Kaufmann, pp 540–550Google Scholar
  31. 31.
    Leslie G Valiant (1984) A theory of the learnable. Commun ACM, 27(11):1134–1142Google Scholar
  32. 32.
    Patrick RJ van der Laag, Shan-Hwei Nienhuys-Cheng (1994) Existence and nonexistence of complete refinement operators. In: Francesco Bergadano, Luc De Raedt (eds) ECML, vol 784 of Lecture notes in computer science, pp 307–322Google Scholar
  33. 33.
    Vere SA (1980) Multilevel counterfactuals for generalizations of relational concepts and productions. Artif Intell 14:139–164zbMATHCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2006

Authors and Affiliations

  • Luigi Iannone
    • 1
  • Ignazio Palmisano
    • 1
  • Nicola Fanizzi
    • 1
  1. 1.Dipartimento di InformaticaUniversità degli Studi di BariBariItaly

Personalised recommendations