Navigation-Induced Knowledge Engineering by Example

A New Paradigm for Knowledge Engineering by the Masses
  • Sebastian Hellmann
  • Jens Lehmann
  • Jörg Unbehauen
  • Claus Stadler
  • Thanh Nghia Lam
  • Markus Strohmaier
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7774)

Abstract

Knowledge Engineering is a costly, tedious and often time-consuming task, for which light-weight processes are desperately needed. In this paper, we present a new paradigm - Navigation-induced Knowledge Engineering by Example (NKE) - to address this problem by producing structured knowledge as a result of users navigating through an information system. Thereby, NKE aims to reduce the costs associated with knowledge engineering by framing it as navigation. We introduce and define the NKE paradigm and demonstrate it with a proof-of-concept prototype which creates OWL class expressions based on users navigating in a collection of resources. The overall contribution of this paper is twofold: (i) it introduces a novel paradigm for knowledge engineering and (ii) it provides evidence for its technical feasibility.

Keywords

Navigation Knowledge Engineering Paradigm Methodology Ontology Learning Search OWL 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Baader, F., Ganter, B., Sattler, U., Sertkaya, B.: Completing description logic knowledge bases using formal concept analysis. In: IJCAI 2007. AAAI Press (2007)Google Scholar
  2. 2.
    Baader, F., Sertkaya, B., Turhan, A.-Y.: Computing the least common subsumer w.r.t. a background terminology. J. Applied Logic 5(3), 392–420 (2007)MathSciNetMATHCrossRefGoogle Scholar
  3. 3.
    Cimiano, P., Rudolph, S., Hartfiel, H.: Computing intensional answers to questions - an inductive logic programming approach. Journal of Data and Knowledge Engineering, DKE (2009)Google Scholar
  4. 4.
    Cohen, W.W., Hirsh, H.: Learning the CLASSIC description logic: Theoretical and experimental results. In: Doyle, J., Sandewall, E., Torasso, P. (eds.) Proceedings of the 4th International Conference on Principles of Knowledge Representation and Reasoning, pp. 121–133. Morgan Kaufmann (May 1994)Google Scholar
  5. 5.
    Esposito, F., Fanizzi, N., Iannone, L., Palmisano, I., Semeraro, G.: Knowledge-intensive induction of terminologies from metadata. In: McIlraith, S.A., Plexousakis, D., van Harmelen, F. (eds.) ISWC 2004. LNCS, vol. 3298, pp. 441–455. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  6. 6.
    Heim, P., Hellmann, S., Lehmann, J., Lohmann, S., Stegemann, T.: RelFinder: Revealing relationships in RDF knowledge bases. In: Chua, T.-S., Kompatsiaris, Y., Mérialdo, B., Haas, W., Thallinger, G., Bailer, W. (eds.) SAMT 2009. LNCS, vol. 5887, pp. 182–187. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  7. 7.
    Helic, D., Strohmaier, M., Trattner, C., Muhr, M., Lerman, K.: Pragmatic evaluation of folksonomies. In: 20th International World Wide Web Conference (WWW 2011), Hyderabad, India, March 28-April 1, pp. 417–426. ACM (2011)Google Scholar
  8. 8.
    Helic, D., Trattner, C., Strohmaier, M., Andrews, K.: On the navigability of social tagging systems. In: The 2nd IEEE International Conference on Social Computing (SocialCom 2010), Minneapolis, Minnesota, USA, pp. 161–168 (2010)Google Scholar
  9. 9.
    Hellmann, S., Lehmann, J., Auer, S.: Learning of OWL class descriptions on very large knowledge bases. Int. J. Semantic Web Inf. Syst. 5(2), 25–48 (2009)CrossRefGoogle Scholar
  10. 10.
    Hepp, M.: GoodRelations: An ontology for describing products and services offers on the web. In: Gangemi, A., Euzenat, J. (eds.) EKAW 2008. LNCS (LNAI), vol. 5268, pp. 329–346. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  11. 11.
    Iannone, L., Palmisano, I., Fanizzi, N.: An algorithm based on counterfactuals for concept learning in the semantic web. Applied Intelligence 26(2), 139–159 (2007)CrossRefGoogle Scholar
  12. 12.
    Jarrar, M., Meersman, R.: Formal ontology engineering in the DOGMA approach. In: Meersman, R., Tari, Z. (eds.) CoopIS/DOA/ODBASE 2002. LNCS, vol. 2519, pp. 1238–1254. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  13. 13.
    Kim, H.L., Scerri, S., Breslin, J.G., Decker, S., Kim, H.G.: The State of the Art in Tag Ontologies: A Semantic Model for Tagging and Folksonomies. In: Proceedings of the 2008 International Conference on Dublin Core and Metadata Applications, Berlin, Deutschland, pp. 128–137. Dublin Core Metadata Initiative (2008)Google Scholar
  14. 14.
    Koerner, C., Benz, D., Strohmaier, M., Hotho, A., Stumme, G.: Stop thinking, start tagging - tag semantics emerge from collaborative verbosity. In: Proc. of the 19th International World Wide Web Conference (WWW 2010), Raleigh, NC, USA. ACM (April 2010)Google Scholar
  15. 15.
    Lehmann, J.: DL-Learner: learning concepts in description logics. JMLR 2009 (2009)Google Scholar
  16. 16.
    Lehmann, J., Bizer, C., Kobilarov, G., Auer, S., Becker, C., Cyganiak, R., Hellmann, S.: DBpedia - a crystallization point for the web of data. Journal of Web Semantics 7(3), 154–165 (2009)CrossRefGoogle Scholar
  17. 17.
    Lehmann, J., Haase, C.: Ideal downward refinement in the \(\mathcal{EL}\) description logic. In: De Raedt, L. (ed.) ILP 2009. LNCS, vol. 5989, pp. 73–87. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  18. 18.
    Lehmann, J., Hitzler, P.: A refinement operator based learning algorithm for the \(\mathcal{ALC}\) description logic. In: Blockeel, H., Ramon, J., Shavlik, J., Tadepalli, P. (eds.) ILP 2007. LNCS (LNAI), vol. 4894, pp. 147–160. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  19. 19.
    Lehmann, J., Hitzler, P.: Concept learning in description logics using refinement operators. Machine Learning Journal 78(1-2), 203–250 (2010)CrossRefGoogle Scholar
  20. 20.
    Lehmann, J., Knappe, S.: DBpedia navigator. In: Semantic Web Challenge, International Semantic Web Conference 2008 (2008)Google Scholar
  21. 21.
    López, M.M.F.: Overview of Methodologies for Building Ontologies. In: Proceedings of the IJCAI 1999 Workshop on Ontologies and Problem Solving Methods (KRR5), Stockholm, Sweden, August 2 (1999)Google Scholar
  22. 22.
    Pinto, H.S., Martins, J.P.: Ontologies: How can they be built? Knowledge and Information Systems 6(4), 441–464 (2004)CrossRefGoogle Scholar
  23. 23.
    Rudolph, S.: Exploring relational structures via \({\mathcal{F\!LE}}\). In: Wolff, K.E., Pfeiffer, H.D., Delugach, H.S. (eds.) ICCS 2004. LNCS (LNAI), vol. 3127, pp. 196–212. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  24. 24.
    Siorpaes, K., Hepp, M.: OntoGame: Weaving the semantic web by online games. In: Bechhofer, S., Hauswirth, M., Hoffmann, J., Koubarakis, M. (eds.) ESWC 2008. LNCS, vol. 5021, pp. 751–766. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  25. 25.
    Strohmaier, M., Koerner, C., Kern, R.: Why do users tag? Detecting users’ motivation for tagging in social tagging systems. In: International AAAI Conference on Weblogs and Social Media (ICWSM 2010), Menlo Park, CA, USA. AAAI (2010)Google Scholar
  26. 26.
    Studer, R., Benjamins, R., Fensel, D.: Knowledge engineering: Principles and methods. Data & Knowledge Engineering 25(1-2), 161–198 (1998)MATHCrossRefGoogle Scholar
  27. 27.
    Völker, J., Rudolph, S.: Fostering web intelligence by semi-automatic OWL ontology refinement. In: Web Intelligence, pp. 454–460. IEEE (2008)Google Scholar
  28. 28.
    Ziegler, C.-N., Lausen, G., Konstan, J.A.: On exploiting classification taxonomies in recommender systems. AI Commun. 21(2-3), 97–125 (2008)MathSciNetMATHGoogle Scholar
  29. 29.
    Zubiaga, A., Koerner, C., Strohmaier, M.: Tags vs shelves: from social tagging to social classification. In: Proceedings of the 22nd ACM Conference on Hypertext and Hypermedia, pp. 93–102. ACM (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Sebastian Hellmann
    • 1
  • Jens Lehmann
    • 1
  • Jörg Unbehauen
    • 1
  • Claus Stadler
    • 1
  • Thanh Nghia Lam
    • 1
  • Markus Strohmaier
    • 2
  1. 1.Institut für Informatik, AKSWUniversität LeipzigLeipzigGermany
  2. 2.Graz University of Technology and Know-CenterGrazAustria

Personalised recommendations