International Semantic Web Conference

The Semantic Web - ISWC 2015 pp 286-302 | Cite as

Effective Online Knowledge Graph Fusion

  • Haofen Wang
  • Zhijia Fang
  • Le Zhang
  • Jeff Z. Pan
  • Tong Ruan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9366)

Abstract

Recently, Web search engines have empowered their search with knowledge graphs to satisfy increasing demands of complex information needs about entities. Each engine offers an online knowledge graph service to display highly relevant information about the query entity in form of a structured summary called knowledge card. The cards from different engines might be complementary. Therefore, it is necessary to fuse knowledge cards from these engines to get a comprehensive view. Such a problem can be considered as a new branch of ontology alignment, which is actually an on-the-fly online data fusion based on the users’ needs. In this paper, we present the first effort to work on knowledge cards fusion. We propose a novel probabilistic scoring algorithm for card disambiguation to select the most likely entity a card should refer to. We then design a learning-based method to align properties from cards representing the same entity. Finally, we perform value deduplication to group equivalent values of the aligned properties as value clusters. The experimental results show that our approach outperforms the state of the art ontology alignment algorithms in terms of precision and recall.

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References

  1. 1.
    Balog, K., Meij, E., de Rijke, M.: Entity search: building bridges between two worlds. In: Proceedings of the 3rd Semsearch Workshop, p. 9 (2010)Google Scholar
  2. 2.
    Blanco, R., Cambazoglu, B.B., Mika, P., Torzec, N.: Entity recommendations in web search. In: Alani, H., et al. (eds.) ISWC 2013, Part II. LNCS, vol. 8219, pp. 33–48. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  3. 3.
    Blanco, R., Halpin, H., Herzig, D.M., Mika, P., Pound, J., Thompson, H.S., Tran, T.: Repeatable and reliable semantic search evaluation. Web Semantics: Science, Services and Agents on the World Wide Web 21, 14–29 (2013)CrossRefGoogle Scholar
  4. 4.
    Dalton, J., Blanco, R., Mika, P.: Coreference aware web object retrieval. In: Proceedings of the 20th ACM CIKM, pp. 211–220 (2011)Google Scholar
  5. 5.
    Flouris, G., Huang, Z., Pan, J.Z., Plexousakis, D., Wache, H.: Inconsistencies, negations and changes in ontologies. In: Proceedings of AAAI, pp. 1295–1300 (2006)Google Scholar
  6. 6.
    Gabrilovich, E., Markovitch, S.: Computing semantic relatedness using wikipedia-based explicit semantic analysis. In: Proceedings of the 20th IJCAI, pp. 6–12 (2007)Google Scholar
  7. 7.
    Gupta, R., Halevy, A., Wang, X., Whang, S.E., Wu, F.: Biperpedia: An ontology for search applications. Proceedings of the VLDB Endowment 7(7), 505–516 (2014)CrossRefGoogle Scholar
  8. 8.
    Herzig, D.M., Mika, P., Blanco, R., Tran, T.: Federated entity search using on-the-fly consolidation. In: Alani, H., et al. (eds.) ISWC 2013, Part I. LNCS, vol. 8218, pp. 167–183. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  9. 9.
    Herzig, D.M., Tran, T.: Heterogeneous web data search using relevance-based on the fly data integration. In: Proceedings of the 21st WWW, pp. 141–150 (2012)Google Scholar
  10. 10.
    Hu, W., Qu, Y., Cheng, G.: Matching large ontologies: A divide-and-conquer approach. Data & Knowledge Engineering 67(1), 140–160 (2008)CrossRefGoogle Scholar
  11. 11.
    Jiménez-Ruiz, E., Grau, B.C., Zhou, Y.: Logmap 2.0: towards logic-based, scalable and interactive ontology matching. In: Ontology Matching, pp. 45–46 (2011)Google Scholar
  12. 12.
    Li, Y., Li, J.Z., Zhang, D., Tang, J.: Result of ontology alignment with RiMOM at OAEI 2006. In: Ontology Matching, p. 181 (2006)Google Scholar
  13. 13.
    Liu, X., Dong, X.L., Ooi, B.C., Srivastava, D.: Online data fusion. Proceedings of the VLDB Endowment 4(11) (2011)Google Scholar
  14. 14.
    Medelyan, O., Witten, I.H., Milne, D.: Topic indexing with wikipedia. In: Proceedings of the AAAI WikiAI workshop, pp. 19–24 (2008)Google Scholar
  15. 15.
    Milne, D., Witten, I.H.: Learning to link with wikipedia. In: Proceedings of the 17th ACM CIKM, pp. 509–518 (2008)Google Scholar
  16. 16.
    Pound, J., Mika, P., Zaragoza, H.: Ad-hoc object retrieval in the web of data. In: Proceedings of the 19th WWW, pp. 771–780 (2010)Google Scholar
  17. 17.
    Rinser, D., Lange, D., Naumann, F.: Cross-lingual entity matching and infobox alignment in wikipedia. Information Systems 38(6), 887–907 (2013)CrossRefGoogle Scholar
  18. 18.
    Stefanidis, K., Efthymiou, V., Herschel, M., Christophides, V.: Entity resolution in the web of data. In: WWW (Companion Volume), pp. 203–204 (2014)Google Scholar
  19. 19.
    Suchanek, F.M., Abiteboul, S., Senellart, P.: PARIS: Probabilistic Alignment of Relations, Instances, and Schema. PVLDB 5(3), 157–168 (2011)Google Scholar
  20. 20.
    Wu, Z., Palmer, M.: Verbs semantics and lexical selection. In: Proceedings of the 32nd ACL, pp. 133–138 (1994)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Haofen Wang
    • 1
  • Zhijia Fang
    • 1
  • Le Zhang
    • 1
  • Jeff Z. Pan
    • 2
  • Tong Ruan
    • 1
  1. 1.East China University of Science and TechnologyShanghaiChina
  2. 2.University of AberdeenAberdeenUK

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