Skip to main content
SpringerLink
Log in

Ontology Enhancement through Inductive Decision Trees

  • Conference paper
Uncertainty Reasoning for the Semantic Web II (URSW 2010, URSW 2009, URSW 2008, UniDL 2010)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7123))

  • 872 Accesses

Abstract

The popularity of ontologies for representing the semantics behind many real-world domains has created a growing pool of ontologies on various topics. Different ontologists, experts, and organizations create a great variety of ontologies, often for narrow application domains. Some of the created ontologies frequently overlap with other ontologies in broader domains if they pertain to the Semantic Web. Sometimes, they model similar or matching theories that may be inconsistent. To assist in the reuse of these ontologies, this paper describes a technique for enriching manually created ontologies by supplementing them with inductively derived rules, and reducing the number of inconsistencies. The derived rules are translated from decision trees with probability measures, created by executing a tree based data mining algorithm over the data being modelled. These rules can be used to revise an ontology in order to extend the ontology with definitions grounded in empirical data, and identify possible similarities between complementary ontologies. We demonstrate the application of our technique by presenting an example, and discuss how various data types may be treated to generalize the semantics of an ontology for a broader application domain.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Belleau, F., Nolin, M.-A., Tourigny, N., Rigault, P., Morissette, J.: Bio2rdf: towards a mashup to build bioinformatics knowledge systems. Journal of Biomedical Informatics 41(5), 706–716 (2008)

    Article  Google Scholar 

  2. Bittner, T., Smith, B.: Granular partitions and vagueness. In: FOIS 2001: Proceedings of the International Conference on Formal Ontology in Information Systems, pp. 309–320. ACM, New York (2001)

    Chapter  Google Scholar 

  3. Bouza, A., Reif, G., Bernstein, A., Gall, H.: Semtree: Ontology-based decision tree algorithm for recommender systems. In: 7th International Semantic Web Conference, ISWC 2008, Germany (October 2008)

    Google Scholar 

  4. Brodaric, M., Gahegan, M.: Experiments to examine the situated nature of geoscientific concepts. Spatial Cognition and Computation: An Interdisciplinary Journal 7(1), 61–95 (2007)

    Article  Google Scholar 

  5. Chepelev, L., Klassen, D., Dumontier, M.: Chemical hazard estimation and method comparison with owl-encoded toxicity decision trees. In: OWLED 2011 OWL: Experiences and Directions (June 2011)

    Google Scholar 

  6. Chien, B.-C., Lu, C.-F., Hsu, S.-J.: Handling incomplete quantitative data for supervised learning based on fuzzy entropy. In: 2005 IEEE International Conference on Granular Computing, vol. 1, pp. 135–140 (July 2005)

    Google Scholar 

  7. da Costa, P.C.G., Laskey, K.B., Laskey, K.J.: PR-OWL: A Bayesian Ontology Language for the Semantic Web. In: da Costa, P.C.G., d’Amato, C., Fanizzi, N., Laskey, K.B., Laskey, K.J., Lukasiewicz, T., Nickles, M., Pool, M. (eds.) URSW 2005-2007. LNCS (LNAI), vol. 5327, pp. 88–107. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  8. Ding, Z., Peng, Y., Pan, R.: BayesOWL: Uncertainty Modeling in Semantic Web Ontologies. In: Ma, Z. (ed.) Soft Computing in Ontologies and Semantic Web. STUDFUZZ, vol. 204, pp. 3–29. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  9. Ding, Z., Peng, Y., Pan, R., Yu, Y.: A bayesian methodology towards automatic ontology mapping. In: Proceedings of the AAAI 2005 C&O Workshop on Contexts and Ontologies: Theory, Practice and Applications, pp. 72–79. AAAI Press (July 2005)

    Google Scholar 

  10. Erdur, C., Seylan, I.: The design of a semantic web compatible content language for agent communication. Expert Systems 25(3), 268–294 (2008)

    Article  Google Scholar 

  11. Euzenat, J., Shvaiko, P.: Ontology Matching. Springer-Verlag New York, Secaucus (2007)

    MATH  Google Scholar 

  12. Falconer, S.M., Noy, N.F., Storey, M.A.: Ontology mapping - a user survey. In: Proceedings of the Workshop on Ontology Matching, OM 2007, pp. 113–125. ISWC/ASWC (2007)

    Google Scholar 

  13. Fanizzi, N., d’Amato, C., Esposito, F.: Towards the induction of terminological decision trees. In: Proceedings of the 2010 ACM Symposium on Applied Computing, SAC 2010, pp. 1423–1427. ACM, New York (2010)

    Chapter  Google Scholar 

  14. Fellbaum, C.: Wordnet: An electronic lexical database (1998)

    Google Scholar 

  15. Gajderowicz, B.: Using decision trees for inductively driven semantic integration and ontology matching. Master’s thesis, Ryerson University, 250 Victoria Street, Toronto, Ontario, Canada (2011)

    Google Scholar 

  16. Giugno, R., Lukasiewicz, T.: P-\(\mathcal{SHOQ}({\bf D})\): A Probabilistic Extension of \(\mathcal{SHOQ}({\bf D})\) for Probabilistic Ontologies in the Semantic Web. In: Flesca, S., Greco, S., Leone, N., Ianni, G. (eds.) JELIA 2002. LNCS (LNAI), vol. 2424, pp. 86–97. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  17. Horrocks, I., Kutz, O., Sattler, U.: The even more irresistible sroiq. In: Proc. of the 10th Int. Conf. on Principles of Knowledge Representation and Reasoning, KR 2006, pp. 57–67. AAAI Press (June 2006)

    Google Scholar 

  18. Horrocks, I., Patel-Schneider, P.F., Harmelen, F.V.: From shiq and rdf to owl: The making of a web ontology language. Journal of Web Semantics 1, 7–26 (2003)

    Article  Google Scholar 

  19. Horrocks, I., Sattler, U.: Ontology reasoning in the shoq(d) description logic. In: Proc. of the 17th Int. Joint Conf. on Artificial Intelligence, IJCAI 2001, pp. 199–204. Morgan Kaufmann (2001)

    Google Scholar 

  20. Kieler, B.: Semantic data integration across different scales: Automatic learning generalization rules. In: International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 37 (2008)

    Google Scholar 

  21. Kieler, B., Huang, W., Haunert, J.H., Jiang, J.: Matching river datasets of different scales. In: AGILE Conf. Lecture Notes in Geoinformation and Cartography, pp. 135–154. Springer (2009)

    Google Scholar 

  22. Klinov, P., Mazlack, L.J.: Granulating semantic web ontologies. In: 2006 IEEE International Conference on Granular Computing, pp. 431–434 (2006)

    Google Scholar 

  23. Krotzsch, M., Rudolph, S., Hitzler, P.: Description logic rules. In: Proceeding of the 2008 Conference on ECAI 2008, pp. 80–84. IOS Press, Amsterdam (2008)

    Google Scholar 

  24. Kwok, R.B.H.: Translations of Ripple Down Rules into Logic Formalisms. In: Dieng, R., Corby, O. (eds.) EKAW 2000. LNCS (LNAI), vol. 1937, pp. 366–379. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  25. Pan, J.Z., Horrocks, I.: Reasoning in the shoq(dn) description logic. In: Proc. of the 2002 Int. Workshop on Description Logics, DL 2002 (April 2002)

    Google Scholar 

  26. Sikder, I.U., Munakata, T.: Application of rough set and decision tree for characterization of premonitory factors of low seismic activity. Expert Systems with Applications 36(1), 102–110 (2009)

    Article  Google Scholar 

  27. Staab, S., Studer, R.: Handbook on Ontologies. International Handbooks on Information Systems. Springer (2004)

    Google Scholar 

  28. Straccia, U.: A fuzzy description logic. In: 15th National Conference on Artificial Intelligence, Madison, USA, vol. 4, pp. 594–599 (1998)

    Google Scholar 

  29. Straccia, U.: Reasoning within fuzzy description logics. Journal of Artificial Intelligence Research 14, 137–166 (2001)

    MathSciNet  MATH  Google Scholar 

  30. Straccia, U.: A fuzzy description logic for the semantic web. In: Fuzzy Logic and the Semantic Web, Capturing Intelligence, vol. 4, pp. 167–181. Elsevier (2005)

    Google Scholar 

  31. Witten, I., Frank, E.: Data Mining: Practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann Publishers, San Francisco (2005)

    MATH  Google Scholar 

  32. Yao, Y.Y.: Granular computing: basic issues and possible solutions. In: Proceedings of the 5th Joint Conference on Information Sciences, pp. 186–189 (2000)

    Google Scholar 

  33. Zadeh, L.A.: Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets Syst. 90, 111–127 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  34. Zadeh, L.A.: Fuzzy sets, fuzzy logic, and fuzzy systems. World Scientific Publishing Co., Inc., River Edge (1996)

    MATH  Google Scholar 

  35. Zhang, J., Silvescu, A., Honavar, V.: Ontology-Driven Induction of Decision Trees at Multiple Levels of Abstraction. In: Koenig, S., Holte, R.C. (eds.) SARA 2002. LNCS (LNAI), vol. 2371, pp. 316–323. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Science Department, Ryerson University, 250 Victoria Street, Toronto, Ontario, Canada

    Bart Gajderowicz, Alireza Sadeghian & Mikhail Soutchanski

Authors
  1. Bart Gajderowicz
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alireza Sadeghian
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mikhail Soutchanski
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Computer Science and Systems Engineering, University of Zaragoza, Ada Byron Building, Maria de Luna 1, 50018, Zaragoza, Spain

    Fernando Bobillo

  2. Department of Systems Engineering and Operations Research, George Mason University, Engineering Building, 4400 University Drive, 22030-4444, Fairfax, VA, USA

    Paulo C. G. Costa

  3. Dipatimento di Informatica, Università degli Studi di Bari, Via Orabona 4, 70125, Bari, Italy

    Claudia d’Amato

  4. Dipatimento di Informatica and CILA, Università degli Studi di Bari, Via Orabona 4, 70125, Bari, Italy

    Nicola Fanizzi

  5. Department of Systems Engineering and Operations Research, George Mason University, Engineering Building, 4400 University Drive, 22030-4444, Fairfax, VA, USA

    Kathryn B. Laskey

  6. MIRTE Corporation, M/S H305, 7515 Colshire Drive, 22102-7508, McLean, VA, USA

    Kenneth J. Laskey

  7. Department of Computer Science, University of Oxford, Wolfson Building, Parks Road, OX1 3QD, Oxford, UK

    Thomas Lukasiewicz

  8. Technische Universität München, Abteilung für Informatk, Boltzmannstraße 3, 85748, Garching, Germany

    Matthias Nickles

  9. Goldman Sachs, 30 Hudson Street, 07302-4699, Jersey City, NJ, USA

    Michael Pool

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Gajderowicz, B., Sadeghian, A., Soutchanski, M. (2013). Ontology Enhancement through Inductive Decision Trees. In: Bobillo, F., et al. Uncertainty Reasoning for the Semantic Web II. URSW URSW URSW UniDL 2010 2009 2008 2010. Lecture Notes in Computer Science(), vol 7123. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35975-0_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-35975-0_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-35974-3

  • Online ISBN: 978-3-642-35975-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation