Skip to main content

General Terminology Induction in OWL

  • Conference paper
  • First Online:
Ontology Engineering (OWLED 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9557))

Included in the following conference series:

Abstract

An ontology is a machine-processable representation of knowledge about a domain of interest.

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 EPUB and 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

Notes

  1. 1.

    http://bioportal.bioontology.org/.

  2. 2.

    This is an abridged version of [14].

  3. 3.

    https://github.com/AKSW/DL-Learner.

  4. 4.

    http://protegewiki.stanford.edu/index.php/Protege_Ontology_Library.

  5. 5.

    http://owl.cs.manchester.ac.uk/repository/.

  6. 6.

    https://archive.ics.uci.edu/ml/datasets/Kinship.

  7. 7.

    http://www.cs.man.ac.uk/~sazonauv/tbox_induction/corpus/.

  8. 8.

    The size of the intersection divided by the size of the union.

  9. 9.

    http://www.cs.man.ac.uk/~sazonauv/tbox_induction/results/.

References

  1. Baader, F., Calvanese, D., McGuinness, D., Nardi, D., Patel-Schneider, P.F. (eds.): The Description Logic Handbook: Theory, Implementation, and Applications. Cambridge University Press, Cambridge (2003)

    MATH  Google Scholar 

  2. Baader, F., Ganter, B., Sertkaya, B., Sattler, U.: Completing description logic knowledge bases using formal concept analysis. In: Proceedings of the 20th International Joint Conference on Artifical Intelligence, IJCAI 2007, pp. 230–235. Morgan Kaufmann Publishers Inc., San Francisco (2007)

    Google Scholar 

  3. Baader, F., Sertkaya, B., Turhan, A.Y.: Computing the least common subsumer w.r.t. a background terminology. J. Appl. Logic 5(3), 392–420 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  4. Conklin, D., Witten, I.H.: Complexity-based induction. Mach. Learn. 16(3), 203–225 (1994)

    MATH  Google Scholar 

  5. Fanizzi, N., d’Amato, C., Esposito, F.: DL-FOIL Concept Learning in Description Logics. In: Železný, F., Lavrač, N. (eds.) ILP 2008. LNCS (LNAI), vol. 5194, pp. 107–121. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  6. Ganter, B., Wille, R.: Formal Concept Analysis, vol. 284. Springer, Berlin (1999)

    Book  MATH  Google Scholar 

  7. Grau, B.C., Horrocks, I., Kazakov, Y., Sattler, U.: Modular reuse of ontologies: theory and practice. J. Artif. Intell. Res. 31, 273–318 (2008)

    MathSciNet  MATH  Google Scholar 

  8. Grau, B.C., Horrocks, I., Motik, B., Parsia, B., Patel-Schneider, P., Sattler, U.: OWL 2: the next step for OWL. Web Semant. 6(4), 309–322 (2008)

    Article  Google Scholar 

  9. Haase, P., Stojanovic, L.: Consistent evolution of OWL ontologies. In: Gómez-Pérez, A., Euzenat, J. (eds.) ESWC 2005. LNCS, vol. 3532, pp. 182–197. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  10. Lehmann, J., Auer, S., Bühmann, L., Tramp, S.: Class expression learning for ontology engineering. Web Semant. 9(1), 71–81 (2011)

    Article  Google Scholar 

  11. Lehmann, J., Hitzler, P.: Concept learning in description logics using refinement operators. Mach. Learn. 78(1–2), 203–250 (2010)

    Article  MathSciNet  Google Scholar 

  12. Lehmann, J., Völker, J. (eds.): Perspectives On Ontology Learning, Studies in the Semantic Web, vol. 18. IOS Press, Amsterdam (2014)

    Google Scholar 

  13. Muggleton, S.: Inductive logic programming. New Gener. Comput. 8(4), 295–318 (1991)

    Article  MATH  Google Scholar 

  14. Sazonau, V., Sattler, U., Brown, G.: General terminology induction in OWL. In: Arenas, M., et al. (eds.) ISWC 2015. LNCS, vol. 9366, pp. 533–550. Springer, Heidelberg (2015)

    Chapter  Google Scholar 

  15. Tsarkov, D., Horrocks, I.: FACT++ description logic reasoner: system description. In: Furbach, U., Shankar, N. (eds.) IJCAR 2006. LNCS (LNAI), vol. 4130, pp. 292–297. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  16. Vitányi, P.M., Li, M.: Minimum description length induction, bayesianism, and kolmogorov complexity. IEEE Trans. Inf. Theor. 46(2), 446–464 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  17. Völker, J., Niepert, M.: Statistical Schema Induction. In: Antoniou, G., Grobelnik, M., Simperl, E., Parsia, B., Plexousakis, D., De Leenheer, P., Pan, J. (eds.) ESWC 2011, Part I. LNCS, vol. 6643, pp. 124–138. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Viachaslau Sazonau .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Sazonau, V., Sattler, U., Brown, G. (2016). General Terminology Induction in OWL. In: Tamma, V., Dragoni, M., Gonçalves, R., Ławrynowicz, A. (eds) Ontology Engineering. OWLED 2015. Lecture Notes in Computer Science(), vol 9557. Springer, Cham. https://doi.org/10.1007/978-3-319-33245-1_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-33245-1_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-33244-4

  • Online ISBN: 978-3-319-33245-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics