Advertisement

A Clustering-Based Approach to Ontology Alignment

  • Songyun Duan
  • Achille Fokoue
  • Kavitha Srinivas
  • Brian Byrne
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7031)

Abstract

Ontology alignment is an important problem for the linked data web, as more and more ontologies and ontology instances get published for specific domains such as government and healthcare. A number of (semi-)automated alignment systems have been proposed in recent years. Most combine a set of similarity functions on lexical, semantic and structural features to align ontologies. Although these functions work well in many cases of ontology alignments, they fail to capture alignments when terms or structure varies vastly across ontologies. In this case, one is forced to rely on manual alignment. In this paper, we study whether it is feasible to re-use such expert provided ontology alignments for new alignment tasks. We focus in particular on many-to-one alignments, where the opportunity for re-use is feasible if alignments are stable. Specifically, we define the notion of a cluster as being made of multiple entities in the source ontology \(\mathcal{S}\) that are mapped to the same entity in the target ontology \(\mathcal{T}\). We test the stability hypothesis that the formed clusters of source ontology are stable across alignments to different target ontologies. If this hypothesis is valid, the clusters of an ontology \(\mathcal{S}\), built from an existing alignment with an ontology \(\mathcal{T}\), can be effectively exploited to align \(\mathcal{S}\) with a new ontology \(\mathcal{T}'\). Evaluation on both manual and automated high-quality alignments show remarkable stability of clusters across ontology alignments in the financial domain and the healthcare and life sciences domain. Experimental evaluation also demonstrates the effectiveness of utilizing the stability of clusters in improving the alignment process in terms of precision and recall.

Keywords

Edit Distance Jaccard Similarity Average Cluster Size Cluster Information Ontology Alignment 
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.

References

  1. 1.
    Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, Heidelberg (2007)zbMATHGoogle Scholar
  2. 2.
    Choi, N., Song, I.-Y., Han, H.: A survey on ontology mapping. SIGMOD Rec. (2006)Google Scholar
  3. 3.
    Cruz, I.F., Antonelli, F.P., Stroe, C.: Agreementmaker: efficient matching for large real-world schemas and ontologies. In: Proc. VLDB Endow., vol. 2, pp. 1586–1589 (August 2009)Google Scholar
  4. 4.
    Doan, A., Madhavan, J., Domingos, P., Halevy, A.: Ontology matching: A machine learning approach. In: Handbook on Ontologies in Information Systems. Springer, Heidelberg (2003)Google Scholar
  5. 5.
    Duan, S., Fokoue, A., Srinivas, K.: One size does not fit all: Customizing ontology alignment using user feedback. 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. 177–192. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  6. 6.
    Eckert, K., Meilicke, C., Stuckenschmidt, H.: Improving Ontology Matching Using Meta-Level Learning. In: Aroyo, L., Traverso, P., Ciravegna, F., Cimiano, P., Heath, T., Hyvönen, E., Mizoguchi, R., Oren, E., Sabou, M., Simperl, E. (eds.) ESWC 2009. LNCS, vol. 5554, pp. 158–172. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  7. 7.
    Ehrig, M., Staab, S., Sure, Y.: Bootstrapping Ontology Alignment Methods with APFEL. In: Gil, Y., Motta, E., Benjamins, V.R., Musen, M.A. (eds.) ISWC 2005. LNCS, vol. 3729, pp. 186–200. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  8. 8.
    Jean-Mary, Y.R., et al. : ASMOV: Results for OAEI 2009. In: OM (2009)Google Scholar
  9. 9.
    Ghazvinian, A., Noy, N.F., Jonquet, C., Shah, N., Musen, M.A.: What Four Million Mappings Can Tell You About Two Hundred Ontologies. In: Bernstein, A., Karger, D.R., Heath, T., Feigenbaum, L., Maynard, D., Motta, E., Thirunarayan, K. (eds.) ISWC 2009. LNCS, vol. 5823, pp. 229–242. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  10. 10.
    Gross, A., Hartung, M., Kirsten, T., Rahm, E.: Mapping composition for matching large life science ontologies. In: International Conference on Biomedical Ontology (2011)Google Scholar
  11. 11.
    Hanif, M.S., Aono, M.: Anchor-Flood: Results for OAEI 2009. In: OM (2009)Google Scholar
  12. 12.
    Jean-Mary, Y.R., Patrick Shironoshita, E., Kabuka, M.R.: Ontology matching with semantic verification. Web Semantics: Science, Services and Agents on the World Wide Web 7(3), 235–251 (2009)CrossRefGoogle Scholar
  13. 13.
    Li, J., Tang, J., Li, Y., Luo, Q.: RiMOM: A dynamic multistrategy ontology alignment framework. IEEE Trans. Knowl. Data Eng. (2009)Google Scholar
  14. 14.
    Noy, N.F.: Semantic integration: a survey of ontology-based approaches. SIGMOD Rec. (2004)Google Scholar
  15. 15.
    Pesquita, C., Stroe, C., Cruz, I., Couto, F.M.: Blooms on agreementmaker: Results for oaei 2010. In: OM (2010)Google Scholar
  16. 16.
    Wang, P., Xu, B.: Lily: Ontology alignment results for OAEI 2009. In: OM (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Songyun Duan
    • 1
  • Achille Fokoue
    • 1
  • Kavitha Srinivas
    • 1
  • Brian Byrne
    • 2
  1. 1.IBM T.J. Watson Research CenterHawthorneUSA
  2. 2.Information ManagementIBM Software GroupAustinUSA

Personalised recommendations