Integrating Linked Data through RDFS and OWL: Some Lessons Learnt

  • Aidan Hogan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6902)

Abstract

In this paper, we summarise the lessons learnt from the PhD Thesis Exploiting RDFS and OWL for Integrating Heterogeneous, Large-Scale, Linked Data Corpora where we looked at three use-cases for reasoning over Linked Data: (i) translating data between different vocabulary terms; (ii) identifying and repairing noise in the form of inconsistency; and (iii) detecting and processing coreferent identifiers (identifiers which refer to the same thing). We summarise how we overcome the challenges of scalability and robustness faced when reasoning over Linked Data. We validate our methods against an open-domain corpus of 1.1 billion quadruples crawled from 4 million Linked Data documents, discussing the applicability and utility of our reasoning methods in such scenarios.

Keywords

Link Data Open Link Data Vocabulary Term AAAI Spring Symposium Closed Corpus 
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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References

  1. 1.
    Auer, S., Lehmann, J.: Creating knowledge out of interlinked data. Semantic Web 1(1-2), 97–104 (2010)Google Scholar
  2. 2.
    Bishop, B., Kiryakov, A., Ognyanoff, D., Peikov, I., Tashev, Z., Velkov, R.: OWLIM: A family of scalable semantic repositories. In: Sem. Web J. (to appear, 2011)Google Scholar
  3. 3.
    Bizer, C., Heath, T., Berners-Lee, T.: Linked Data - The Story So Far. Int. J. Semantic Web Inf. Syst. 5(3), 1–22 (2009)CrossRefGoogle Scholar
  4. 4.
    Cheng, G., Ge, W., Wu, H., Qu, Y.: Searching Semantic Web Objects Based on Class Hierarchies. In: Proceedings of Linked Data on the Web Workshop (2008)Google Scholar
  5. 5.
    Delbru, R., Polleres, A., Tummarello, G., Decker, S.: Context Dependent Reasoning for Semantic Documents in Sindice. In: Proc. of 4th SSWS Workshop (2008)Google Scholar
  6. 6.
    Grau, B.C., Motik, B., Wu, Z., Fokoue, A., Lutz, C.: OWL 2 Web Ontology Language: Profiles. W3C Recommendation (October 2009), http://www.w3.org/TR/owl2-profiles/
  7. 7.
    Halpin, H., Hayes, P.J., McCusker, J.P., McGuinness, D.L., Thompson, H.S.: When owl:sameAs Isn’t the Same: An Analysis of Identity in Linked 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. 305–320. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  8. 8.
    Heath, T., Bizer, C.: Linked Data: Evolving the Web into a Global Data Space, 1st edn. Morgan & Claypool (2011)Google Scholar
  9. 9.
    Hogan, A.: Exploiting RDFS and OWL for Integrating Heterogeneous, Large-Scale, Linked Data Corpora. PhD thesis, Digital Enterprise Research Institute, National University of Ireland, Galway (2011), http://aidanhogan.com/docs/thesis/
  10. 10.
    Hogan, A., Harth, A., Polleres, A.: Scalable Authoritative OWL Reasoning for the Web. Int. J. Semantic Web Inf. Syst. 5(2) (2009)Google Scholar
  11. 11.
    Hogan, A., Pan, J.Z., Polleres, A., Decker, S.: SAOR: Template Rule Optimisations for Distributed Reasoning over 1 Billion Linked Data Triples. 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. 337–353. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  12. 12.
    Jain, P., Hitzler, P., Yeh, P.Z., Verma, K., Sheth, A.P.: Linked Data is Merely More Data. In: AAAI Spring Symposium on Linked Data Meets Artificial Intelligence (March 2010)Google Scholar
  13. 13.
    Kolovski, V., Wu, Z., Eadon, G.: Optimizing Enterprise-scale OWL 2 RL Reasoning in a Relational Database System. 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. 436–452. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  14. 14.
    Meditskos, G., Bassiliades, N.: DLEJena: A practical forward-chaining OWL 2 RL reasoner combining Jena and Pellet. J. Web Sem. 8(1), 89–94 (2010)CrossRefGoogle Scholar
  15. 15.
    Urbani, J., Kotoulas, S., Maassen, J., van Harmelen, F., Bal, H. E.: OWL Reasoning with WebPIE: Calculating the Closure of 100 Billion Triples. In: Aroyo, L., Antoniou, G., Hyvönen, E., ten Teije, A., Stuckenschmidt, H., Cabral, L., Tudorache, T. (eds.) ESWC 2010. LNCS, vol. 6088, pp. 213–227. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  16. 16.
    Urbani, J., Kotoulas, S., Oren, E., van Harmelen, F.: Scalable Distributed Reasoning Using MapReduce. In: Bernstein, A., Karger, D.R., Heath, T., Feigenbaum, L., Maynard, D., Motta, E., Thirunarayan, K. (eds.) ISWC 2009. LNCS, vol. 5823, pp. 634–649. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  17. 17.
    Weaver, J., Hendler, J.A.: Parallel Materialization of the Finite RDFS Closure for Hundreds of Millions of Triples. In: Bernstein, A., Karger, D.R., Heath, T., Feigenbaum, L., Maynard, D., Motta, E., Thirunarayan, K. (eds.) ISWC 2009. LNCS, vol. 5823, pp. 682–697. Springer, Heidelberg (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Aidan Hogan
    • 1
  1. 1.Digital Enterprise Research InstituteNational University of IrelandGalwayIreland

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