Adaptive Knowledge Propagation in Web Ontologies

  • Pasquale Minervini
  • Claudia d’Amato
  • Nicola Fanizzi
  • Floriana Esposito
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8876)


The increasing availability of structured machine-processable knowledge in the Web of Data calls for machine learning methods to support standard reasoning based services (such as query-answering and logic inference). Statistical regularities can be efficiently exploited to overcome the limitations of the inherently incomplete knowledge bases distributed across the Web. This paper focuses on the problem of predicting missing class-memberships and property values of individual resources in Web ontologies. We propose a transductive inference method for inferring missing properties about individuals: given a class-membership/property value learning problem, we address the task of identifying relations which are likely to link similar individuals, and efficiently propagating knowledge across such (possibly diverse) relations. Our experimental evaluation demonstrates the effectiveness of the proposed method.


Similarity Graph Label Function Conjunctive Query Link Open Data BRITISH Geological Survey 
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.


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  1. 1.
    Baader, F., Calvanese, D., McGuinness, D.L., Nardi, D., Patel-Schneider, P.F. (eds.): The Description Logic Handbook. Cambridge University Press (2007)Google Scholar
  2. 2.
    Berners-Lee, T., Hendler, J., Lassila, O.: The Semantic Web. Scientific American 284(5), 34–43 (2001)CrossRefGoogle Scholar
  3. 3.
    Bhagat, S., Cormode, G., Muthukrishnan, S.: Node classification in social networks. In: Aggarwal, C.C. (ed.) Social Network Data Analytics, pp. 115–148. Springer (2011)Google Scholar
  4. 4.
    Bizer, C., Lehmann, J., Kobilarov, G., Auer, S., Becker, C., Cyganiak, R., Hellmann, S.: DBpedia - a crystallization point for the Web of Data. J. Web Sem. 7(3), 154–165 (2009)CrossRefGoogle Scholar
  5. 5.
    Chapelle, O., Schölkopf, B., Zien, A. (eds.): Semi-Supervised Learning. MIT Press (2006)Google Scholar
  6. 6.
    Hastie, T., Tibshirani, R., Friedman, J.: The elements of statistical learning: data mining, inference and prediction, 2nd edn. Springer (2008)Google Scholar
  7. 7.
    Heath, T., Bizer, C.: Linked Data: Evolving the Web into a Global Data Space. Synthesis Lectures on the Semantic Web. Morgan & Claypool Publishers (2011)Google Scholar
  8. 8.
    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
  9. 9.
    Kelner, J.A., Orecchia, L., Sidford, A., Zhu, Z.A.: A simple, combinatorial algorithm for solving sdd systems in nearly-linear time. In: Boneh, D., et al. (eds.) Proceedings of STOC 2013, pp. 911–920. ACM (2013)Google Scholar
  10. 10.
    Koller, D., Friedman, N.: Probabilistic Graphical Models: Principles and Techniques. MIT Press (2009)Google Scholar
  11. 11.
    Lin, H.T., Koul, N., Honavar, V.: Learning Relational Bayesian Classifiers from RDF Data. In: Aroyo, L., Welty, C., Alani, H., Taylor, J., Bernstein, A., Kagal, L., Noy, N., Blomqvist, E. (eds.) ISWC 2011, Part I. LNCS, vol. 7031, pp. 389–404. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  12. 12.
    Lösch, U., Bloehdorn, S., Rettinger, A.: Graph kernels for RDF data. In: Simperl, E., Cimiano, P., Polleres, A., Corcho, O., Presutti, V. (eds.) ESWC 2012. LNCS, vol. 7295, pp. 134–148. Springer, Heidelberg (2012)Google Scholar
  13. 13.
    Minervini, P., d’Amato, C., Fanizzi, N., Esposito, F.: Transductive inference for class-membership propagation in web ontologies. In: Cimiano, P., Corcho, O., Presutti, V., Hollink, L., Rudolph, S. (eds.) ESWC 2013. LNCS, vol. 7882, pp. 457–471. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  14. 14.
    Nickel, M., Tresp, V., Kriegel, H.P.: A Three-Way Model for Collective Learning on Multi-Relational Data. In: Getoor, L., et al. (eds.) Proceedings of ICML 2011, pp. 809–816. Omnipress (2011)Google Scholar
  15. 15.
    Rettinger, A., Lösch, U., Tresp, V., d’Amato, C., Fanizzi, N.: Mining the Semantic Web: Statistical learning for next generation knowledge bases. Data Min. Knowl. Discov. 24(3), 613–662 (2012)CrossRefzbMATHMathSciNetGoogle Scholar
  16. 16.
    Rettinger, A., Nickles, M., Tresp, V.: Statistical Relational Learning with Formal Ontologies. In: Buntine, W., Grobelnik, M., Mladenić, D., Shawe-Taylor, J. (eds.) ECML PKDD 2009, Part II. LNCS, vol. 5782, pp. 286–301. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  17. 17.
    Sirin, E., Parsia, B.: SPARQL-DL: SPARQL Query for OWL-DL. In: Golbreich, C., et al. (eds.) OWLED. CEUR Workshop Proceedings, vol. 258. (2007)Google Scholar
  18. 18.
    Spielman, D.A.: Algorithms, Graph Theory, and Linear Equations in Laplacian Matrices. In: Proceedings of ICM 2010, pp. 2698–2722 (2010)Google Scholar
  19. 19.
    Tresp, V., Huang, Y., Bundschus, M., Rettinger, A.: Materializing and querying learned knowledge. In: Proceedings of IRMLeS 2009 (2009)Google Scholar
  20. 20.
    Vapnik, V.N.: Statistical learning theory. Wiley, 1 edn. (September 1998)Google Scholar
  21. 21.
    de Vries, G.K.D.: A Fast Approximation of the Weisfeiler-Lehman Graph Kernel for RDF Data. In: Blockeel, H., Kersting, K., Nijssen, S., Železný, F. (eds.) ECML PKDD 2013, Part I. LNCS, vol. 8188, pp. 606–621. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  22. 22.
    Zhu, X., Ghahramani, Z., Lafferty, J.D.: Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions. In: Fawcett, T., et al. (eds.) Proceedings of ICML 2003, pp. 912–919. AAAI Press (2003)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Pasquale Minervini
    • 1
  • Claudia d’Amato
    • 1
  • Nicola Fanizzi
    • 1
  • Floriana Esposito
    • 1
  1. 1.Department of Computer ScienceUniversity of BariItaly

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