Facilitating Human Intervention in Coreference Resolution with Comparative Entity Summaries

  • Danyun Xu
  • Gong Cheng
  • Yuzhong Qu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8465)


A primary challenge to Web data integration is coreference resolution, namely identifying entity descriptions from different data sources that refer to the same real-world entity. Increasingly, solutions to coreference resolution have humans in the loop. For instance, many active learning, crowdsourcing, and pay-as-you-go approaches solicit user feedback for verifying candidate coreferent entities computed by automatic methods. Whereas reducing the number of verification tasks is a major consideration for these approaches, very little attention has been paid to the efficiency of performing each single verification task. To address this issue, in this paper, instead of showing the entire descriptions of two entities for verification which are possibly lengthy, we propose to extract and present a compact summary of them, and expect that such length-limited comparative entity summaries can help human users verify more efficiently without significantly hurting the accuracy of their verification. Our approach exploits the common and different features of two entities that best help indicate (non-)coreference, and also considers the diverse information on their identities. Experimental results show that verification is 2.7–2.9 times faster when using our comparative entity summaries, and its accuracy is not notably affected.


#eswc2014Xu comparative entity summary coreference resolution entity consolidation entity summarization 


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  1. 1.
    Bilgic, M., Licamele, L., Getoor, L., Shneiderman, B.: D-Dupe: An Interactive Tool for Entity Resolution in Social Networks. In: 2006 IEEE Symposium on Visual Analytics Science and Technology, pp. 43–50. IEEE, Washington, DC (2006)CrossRefGoogle Scholar
  2. 2.
    Cheng, G., Qu, Y.: Searching Linked Objects with Falcons: Approach, Implementation and Evaluation. Int’l J. Semant. Web Inf. Syst. 5(3), 49–70 (2009)CrossRefGoogle Scholar
  3. 3.
    Cheng, G., Tran, T., Qu, Y.: RELIN: Relatedness and Informativeness-based Centrality for Entity Summarization. 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. 114–129. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  4. 4.
    Falconer, S.M., Storey, M.-A.: A Cognitive Support Framework for Ontology Mapping. In: Aberer, K., et al. (eds.) ISWC/ASWC 2007. LNCS, vol. 4825, pp. 114–127. Springer, Heidelberg (2007)Google Scholar
  5. 5.
    Hogan, A., Zimmermann, A., Umbrich, J., Polleres, A., Decker, S.: Scalable and Distributed Methods for Entity Matching, Consolidation and Disambiguation over Linked Data Corpora. J. Web Semant. 10, 76–110 (2012)CrossRefGoogle Scholar
  6. 6.
    Madhavan, J., Jeffery, S.R., Cohen, S., Dong, X., Ko, D., Yu, C., Halevy, A.: Web-scale Data Integration: You Can Only Afford to Pay As You Go. In: 3rd Biennial Conference on Innovative Data Systems Research, pp. 342–350. (2007)Google Scholar
  7. 7.
    Ngonga Ngomo, A.-C., Lehmann, J., Auer, S., Höffner, K.: RAVEN - Active Learning of Link Specifications. In: 6th International Workshop on Ontology Matching, pp. 25–36. (2011)Google Scholar
  8. 8.
    Pisinger, D.: The Quadratic Knapsack Problem - A Survey. Discrete Appl. Math. 155(15), 623–648 (2007)CrossRefzbMATHMathSciNetGoogle Scholar
  9. 9.
    Shvaiko, P., Euzenat, J.: Ontology Matching: State of the Art and Future Challenges. IEEE Trans. Knowl. Data Eng. 25(1), 158–176 (2013)CrossRefGoogle Scholar
  10. 10.
    Stoilos, G., Stamou, G., Kollias, S.: A String Metric for Ontology Alignment. In: Gil, Y., Motta, E., Benjamins, V.R., Musen, M.A. (eds.) ISWC 2005. LNCS, vol. 3729, pp. 624–637. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  11. 11.
    Sydow, M., Pikuła, M., Schenkel, R.: The Notion of Diversity in Graphical Entity Summarisation on Semantic Knowledge Graphs. J. Intell. Inf. Syst. 41(2), 109–149 (2013)CrossRefGoogle Scholar
  12. 12.
    Thalhammer, A., Toma, I., Roa-Valverde, A.J., Fensel, D.: Leveraging Usage Data for Linked Data Movie Entity Summarization. In: 2nd International Workshop on Usage Analysis and the Web of Data (2012)Google Scholar
  13. 13.
    Wang, J., Kraska, T., Franklin, M.J., Feng, J.: CrowdER: Crowdsourcing Entity Resolution. Proc. VLDB Endowment 5(11), 1483–1494 (2012)CrossRefGoogle Scholar
  14. 14.
    Yang, Z., Wang, G., Chu, F.: An Effective GRASP and Tabu Search for the 0-1 Quadratic Knapsack Problem. Comput. Oper. Res. 40(5), 1176–1185 (2013)CrossRefMathSciNetGoogle Scholar
  15. 15.
    Zhang, L., Zhang, Y., Chen, Y.: Summarizing Highly Structured Documents for Effective Search Interaction. In: 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 145–154. ACM, New York (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Danyun Xu
    • 1
  • Gong Cheng
    • 1
  • Yuzhong Qu
    • 1
  1. 1.State Key Laboratory for Novel Software TechnologyNanjing UniversityNanjingP.R. China

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