Instance-Based Ontological Knowledge Acquisition

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7882)


The Linked Open Data (LOD) cloud contains tremendous amounts of interlinked instances, from where we can retrieve abundant knowledge. However, because of the heterogeneous and big ontologies, it is time consuming to learn all the ontologies manually and it is difficult to observe which properties are important for describing instances of a specific class. In order to construct an ontology that can help users easily access to various data sets, we propose a semi-automatic ontology integration framework that can reduce the heterogeneity of ontologies and retrieve frequently used core properties for each class. The framework consists of three main components: graph-based ontology integration, machine-learning-based ontology schema extraction, and an ontology merger. By analyzing the instances of the linked data sets, this framework acquires ontological knowledge and constructs a high-quality integrated ontology, which is easily understandable and effective in knowledge acquisition from various data sets using simple SPARQL queries.


Semantic Web linked data ontology integration knowledge acquisition machine learning 


  1. 1.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proceedings of the Twentieth International Conference on Very Large Data Bases, pp. 487–499 (1994)Google Scholar
  2. 2.
    Bechhofer, S., van Harmelen, F., Hendler, J., Horrocks, I., McGuinness, D.L., Patel-Schneider, P.F., Stein, L.A.: OWL Web Ontology Language Reference. W3C Recommendation (2004),
  3. 3.
    Berners-Lee, T.: Linked Data - Design Issues (2006),
  4. 4.
    Bizer, C., Heath, T., Berners-Lee, T.: Linked data - the story so far. International Journal on Semantic Web and Information Systems 5(3), 1–22 (2009)CrossRefGoogle Scholar
  5. 5.
    Euzenat, J., Shvaiko, P.: Ontology Matching. Springer, Heidelberg (2007)zbMATHGoogle Scholar
  6. 6.
    Fellbaum, C. (ed.): WordNet: An Electronic Lexical Database. MIT Press (1998)Google Scholar
  7. 7.
    Heath, T., Bizer, C.: Linked Data: Evolving the Web into a Global Data Space. Morgan & Claypool (2011)Google Scholar
  8. 8.
    Ichise, R.: An analysis of multiple similarity measures for ontology mapping problem. International Journal of Semantic Computing 4(1), 103–122 (2010)zbMATHCrossRefGoogle Scholar
  9. 9.
    Jain, P., Hitzler, P., Sheth, A.P., Verma, K., Yeh, P.Z.: Ontology alignment for linked open data. In: Patel-Schneider, P.F., Pan, Y., Hitzler, P., Mika, P., Zhang, L., Pan, J.Z., Horrocks, I., Glimm, B. (eds.) ISWC 2010, Part I. LNCS, vol. 6496, pp. 402–417. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  10. 10.
    Kohavi, R.: The power of decision tables. In: Lavrač, N., Wrobel, S. (eds.) ECML 1995. LNCS, vol. 912, pp. 174–189. Springer, Heidelberg (1995)CrossRefGoogle Scholar
  11. 11.
    Le, N.T., Ichise, R., Le, H.B.: Detecting hidden relations in geographic data. In: Proceedings of the 4th International Conference on Advances in Semantic Processing, pp. 61–68 (2010)Google Scholar
  12. 12.
    Manning, C.D., Raghavan, P., Schtze, H.: Introduction to Information Retrieval. Cambridge University Press (2008)Google Scholar
  13. 13.
    Meilicke, C., Völker, J., Stuckenschmidt, H.: Learning disjointness for debugging mappings between lightweight ontologies. In: Gangemi, A., Euzenat, J. (eds.) EKAW 2008. LNCS (LNAI), vol. 5268, pp. 93–108. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  14. 14.
    Parundekar, R., Knoblock, C.A., Ambite, J.L.: Discovering concept coverings in ontologies of linked data sources. In: Cudré-Mauroux, P., et al. (eds.) ISWC 2012, Part I. LNCS, vol. 7649, pp. 427–443. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  15. 15.
    Pedersen, T., Patwardhan, S., Michelizzi, J.: Wordnet: similarity: Measuring the relatedness of concepts. In: Proceedings of the Nineteenth National Conference on Artificial Intelligence, pp. 1024–1025. Association for Computational Linguistics (2004)Google Scholar
  16. 16.
    W3C OWL Working Group: OWL 2 Web Ontology Language Document Overview. W3C Recommendation (2012),
  17. 17.
    Winkler, W.E.: Overview of record linkage and current research directions. Tech. rep., Statistical Research Division U.S. Bureau of the Census (2006)Google Scholar
  18. 18.
    Zhao, L., Ichise, R.: Graph-based ontology analysis in the linked open data. In: Proceedings of the Eighth International Conference on Semantic Systems, pp. 56–63. ACM (2012)Google Scholar
  19. 19.
    Zhao, L., Ichise, R.: Integrating ontologies using ontology learning approach. IEICE Transactions on Information and Systems E96-D(1), 40–50 (2013)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.The Graduate University for Advanced StudiesJapan
  2. 2.National Institute of InformaticsTokyoJapan

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