Advertisement

Grouping Results of Queries to Ontological Knowledge Bases by Conceptual Clustering

  • Agnieszka Ławrynowicz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5796)

Abstract

The paper proposes the framework for clustering results of queries submitted over Semantic Web data. As an instantiation of a framework, an approach is proposed for clustering the results of conjunctive queries submitted to knowledge bases represented in Web Ontology Language (OWL). As components of the approach, a method for the construction of a set of semantic features, as well as a novel method for feature selection are presented. The proposed approach is implemented, and its feasibility is tested empirically.

Keywords

Description Logic Conjunctive Query SPARQL Query Conceptual Cluster Atomic Concept 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Baader, F., Calvanese, D., McGuinness, D., Nardi, D. (eds.): The description logic handbook: Theory, implementation and applications. Cambridge University Press, Cambridge (2003)zbMATHGoogle Scholar
  2. 2.
    Berners-Lee, T., Hendler, J., Lassila, O.: The Semantic Web. Scientific American 284(5), 34–43 (2001)CrossRefGoogle Scholar
  3. 3.
    Clerkin, P., Cunningham, P., Hayes, C.: Ontology discovery for the Semantic Web using hierarchical clustering. In: Proc. of Semantic Web Mining Workshop (2001)Google Scholar
  4. 4.
    Gluck, M.A., Corter, J.E.: Information, uncertainty, and the utility of categories. In: Proc. of the Seventh Annual Conference on Artificial Intelligence (1985)Google Scholar
  5. 5.
    Fanizzi, N., d’Amato, C., Esposito, F.: Randomized metric induction and evolutionary conceptual clustering for semantic knowledge bases. In: Proc. of the ACM International Conference on Knowledge Management, CIKM 2007 (2007)Google Scholar
  6. 6.
    Fanizzi, N., d’Amato, C., Esposito, F.: Induction of Optimal Semi-distances for Individuals based on Feature Sets. In: Proc. of the DL 2007, Workshop (2007)Google Scholar
  7. 7.
    Fanizzi, N., d’Amato, C., Esposito, F.: Evolutionary conceptual clustering based on induced pseudo-metrics. Int. J. Semantic Web Inf. Syst. 4(3), 44–67 (2008)CrossRefGoogle Scholar
  8. 8.
    Fanizzi, N., d’Amato, C., Esposito, F.: Conceptual clustering and its application to concept drift and novelty detection. In: Bechhofer, S., Hauswirth, M., Hoffmann, J., Koubarakis, M. (eds.) ESWC 2008. LNCS, vol. 5021, pp. 318–332. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  9. 9.
    Fisher, D.H.: Knowledge acquisition via incremental conceptual clustering. Machine Learning 2, 139–172 (1987)Google Scholar
  10. 10.
    Grimnes, G.A., Edwards, P., Preece, A.D.: Instance Based Clustering of Semantic Web Resources. In: Bechhofer, S., Hauswirth, M., Hoffmann, J., Koubarakis, M. (eds.) ESWC 2008. LNCS, vol. 5021, pp. 303–317. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  11. 11.
    Jozefowska, J., Lawrynowicz, A., Lukaszewski, T.: Frequent pattern discovery in owl dlp knowledge bases. In: Staab, S., Svátek, V. (eds.) EKAW 2006. LNCS (LNAI), vol. 4248, pp. 287–302. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  12. 12.
    Jozefowska, J., Lawrynowicz, A., Lukaszewski, T.: On reducing redundancy in mining relational association rules from the semantic web. In: Calvanese, D., Lausen, G. (eds.) RR 2008. LNCS, vol. 5341, pp. 205–213. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  13. 13.
    Li, C., Wang, M., Lim, L., Wang, H.: Supporting ranking and clustering as generalized order-by and group-by. In: Proc. of SIGMOD 2007, pp. 127–138 (2007)Google Scholar
  14. 14.
    Osiński, S., Weiss, D.: Carrot2: Design of a Flexible and Efficient Web Information Retrieval Framework. In: Szczepaniak, P.S., Kacprzyk, J., Niewiadomski, A. (eds.) AWIC 2005. LNCS (LNAI), vol. 3528, pp. 439–444. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  15. 15.
    Osinski, S., Weiss, D.: A Concept-Driven Algorithm for Clustering Search Results. IEEE Intelligent Systems 20, 48–54 (2005)CrossRefGoogle Scholar
  16. 16.
    Rousseeuw, P.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53–65 (1987)CrossRefzbMATHGoogle Scholar
  17. 17.
    Schickel-Zuber, V., Faltings, B.: Using Hierarchical Clustering for Learning the Ontologies used in Recommendation Systems. In: Proc. of ACM SIGKDD 2007, pp. 599–608 (2007)Google Scholar
  18. 18.
    Zhang, C., Huang, Y.: Cluster By: a new sql extension for spatial data aggregation. In: Proc. of 15th ACM International Symposium on Geographic Information Systems, ACM-GIS 2007, vol. 53 (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Agnieszka Ławrynowicz
    • 1
  1. 1.Institute of Computing SciencePoznan University of TechnologyPoznanPoland

Personalised recommendations