Skip to main content

Frequent Pattern Discovery from OWL DLP Knowledge Bases

  • Conference paper
Managing Knowledge in a World of Networks (EKAW 2006)

Abstract

The Semantic Web technology should enable publishing of numerous resources of scientific and other, highly formalized data on the Web. The application of mining these huge, networked Web repositories seems interesting and challenging. In this paper we present and discuss an inductive reasoning procedure for mining frequent patterns from the knowledge bases represented in OWL DLP. OWL DLP, also known as Description Logic Programs, lies at the intersection of the expressivity of OWL DL and Logic Programming. Our method is based on a special trie data structure inspired by similar, efficient structures used in classical and relational data mining settings. Conjunctive queries to OWL DLP knowledge bases are the language of frequent patterns.

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

Access this chapter

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. Agrawal, R., Mannila, H., Srikant, R., Toivonen, H., Verkamo, A.I.: Fast discovery of association rules. In: Advances in Knowledge Discovery and Data Mining, pp. 307–328. AAAI Press, Menlo Park (1996)

    Google Scholar 

  2. Berners-Lee, T., Hendler, J., Lassila, O.: The Semantic Web. Scientific American 284(5), 34–43 (2001)

    Article  Google Scholar 

  3. Blockeel, H., Dehaspe, L., Demoen, B., Janssens, G., Ramon, J., Vandecasteele, H.: Improving the efficiency of Inductive Logic Programming through the use of query packs. Journal of Artificial Intelligence Research 16, 135–166 (2002)

    Google Scholar 

  4. Dehaspe, L., Toivonen, H.: Discovery of frequent Datalog patterns. Data Mining and Knowledge Discovery 3(1), 7–36 (1999)

    Article  Google Scholar 

  5. Donini, F., Lenzerini, M., Nardi, D., Schaerf, A.: AL-log: Integrating datalog and description logics. Journal of Intelligent Information Systems 10(3), 227–252 (1998)

    Article  Google Scholar 

  6. Dzeroski, S., Lavrac, N. (eds.): Relational data mining. Springer, Heidelberg (2001)

    MATH  Google Scholar 

  7. Grosof, B.N., Horrocks, I., Volz, R., Decker, S.: Description Logic Programs: Combining Logic Programs with Description Logic. In: Proc. of the Twelfth Int’l World Wide Web Conf (WWW 2003), pp. 48–57. ACM Press, New York (2003)

    Chapter  Google Scholar 

  8. Hitzler, P., Studer, R., Sure, Y.: Description Logic Programs: A Practical Choice For the Modelling of Ontologies. In: Proc. of the 1st Workshop on Formal Ontologies meet Meet Industry, FOMI 2005, Verona, Italy (2005)

    Google Scholar 

  9. Józefowska, J., Ławrynowicz, A., Łukaszewski, T.: Towards discovery of frequent patterns in description logics with rules. In: Adi, A., Stoutenburg, S., Tabet, S. (eds.) RuleML 2005. LNCS, vol. 3791, pp. 84–97. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  10. Józefowska, J., Ławrynowicz, A., Łukaszewski, T.: Faster frequent pattern mining from the Semantic Web. In: Intelligent Information Processing and Web Mining Conference, IIS:IIPWM 2006. Advances in Soft Computing, pp. 121–130. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  11. Lisi, F.A., Malerba, D.: Inducing Multi-Level Association Rules from Multiple Relation. Machine Learning Journal 55, 175–210 (2004)

    Article  MATH  Google Scholar 

  12. Mannila, H., Toivonen, H.: Levelwise search and borders of theories in knowledge discovery. Data Mining and Knowledge Discovery 1(3), 241–258 (1997)

    Article  Google Scholar 

  13. Motik, B., Sattler, U., Studer, R.: Query Answering for OWL-DL with Rules. In: McIlraith, S.A., Plexousakis, D., van Harmelen, F. (eds.) ISWC 2004. LNCS, vol. 3298, pp. 549–563. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  14. Motik, B., Sattler, U.: Practical DL Reasoning over Large ABoxes with KAON2 (Submitted for publication)

    Google Scholar 

  15. Nienhuys-Cheng, S.-H., de Wolf, R.: Foundations of Inductive Logic Programming. LNCS (LNAI), vol. 1228. Springer, Heidelberg (1997)

    Google Scholar 

  16. Nijssen, S., Kok, J.N.: Faster Association Rules for Multiple Relations. In: Proceedings of the IJCAI 2001, pp. 891–897 (2001)

    Google Scholar 

  17. Nijssen, S., Kok, J.N.: Efficient frequent query discovery in FARMER. In: Lavrač, N., Gamberger, D., Todorovski, L., Blockeel, H. (eds.) PKDD 2003. LNCS (LNAI), vol. 2838, pp. 350–362. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Józefowska, J., Ławrynowicz, A., Łukaszewski, T. (2006). Frequent Pattern Discovery from OWL DLP Knowledge Bases. In: Staab, S., Svátek, V. (eds) Managing Knowledge in a World of Networks. EKAW 2006. Lecture Notes in Computer Science(), vol 4248. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11891451_26

Download citation

  • DOI: https://doi.org/10.1007/11891451_26

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-46363-4

  • Online ISBN: 978-3-540-46365-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics