Pay-As-You-Go Multi-user Feedback Model for Ontology Matching

  • Isabel F. Cruz
  • Francesco Loprete
  • Matteo Palmonari
  • Cosmin Stroe
  • Aynaz Taheri
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8876)

Abstract

Using our multi-user model, a community of users provides feedback in a pay-as-you-go fashion to the ontology matching process by validating the mappings found by automatic methods, with the following advantages over having a single user: the effort required from each user is reduced, user errors are corrected, and consensus is reached. We propose strategies that dynamically determine the order in which the candidate mappings are presented to the users for validation. These strategies are based on mapping quality measures that we define. Further, we use a propagation method to leverage the validation of one mapping to other mappings. We use an extension of the AgreementMaker ontology matching system and the Ontology Alignment Evaluation Initiative (OAEI) Benchmarks track to evaluate our approach. Our results show how Fmeasure and robustness vary as a function of the number of user validations. We consider different user error and revalidation rates (the latter measures the number of times that the same mapping is validated). Our results highlight complex trade-offs and point to the benefits of dynamically adjusting the revalidation rate.

Keywords

Similarity Score Signature Vector Matching Task User Feedback Candidate Selection 
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.
    Euzenat, J., Shvaiko, P.: Ontology Matching. Springer, Heidelberg (2007)MATHGoogle Scholar
  2. 2.
    Cruz, I.F., Sunna, W.: Structural Alignment Methods with Applications to Geospatial Ontologies. Transactions in GIS, Special Issue on Semantic Similarity Measurement and Geospatial Applications 12, 683–711 (2008)Google Scholar
  3. 3.
    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
  4. 4.
    Shi, F., Li, J., Tang, J., Xie, G., Li, H.: Actively Learning Ontology Matching via User Interaction. In: Bernstein, A., Karger, D.R., Heath, T., Feigenbaum, L., Maynard, D., Motta, E., Thirunarayan, K. (eds.) ISWC 2009. LNCS, vol. 5823, pp. 585–600. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  5. 5.
    Cruz, I.F., Stroe, C., Palmonari, M.: Interactive User Feedback in Ontology Matching Using Signature Vectors. In: IEEE International Conference on Data Engineering (ICDE), pp. 1321–1324. IEEE (2012)Google Scholar
  6. 6.
    Belhajjame, K., Paton, N.W., Fernandes, A.A.A., Hedeler, C., Embury, S.M.: User Feedback as a First Class Citizen in Information Integration Systems. In: Conference on Innovative Data Systems Research (CIDR), pp. 175–183 (2011)Google Scholar
  7. 7.
    Sarasua, C., Simperl, E., Noy, N.F.: CrowdMap: Crowdsourcing Ontology Alignment with Microtasks. In: Cudré-Mauroux, P., Heflin, J., Sirin, E., Tudorache, T., Euzenat, J., Hauswirth, M., Parreira, J.X., Hendler, J., Schreiber, G., Bernstein, A., Blomqvist, E. (eds.) ISWC 2012, Part I. LNCS, vol. 7649, pp. 525–541. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  8. 8.
    Bourdaillet, J., Roy, S., Jung, G., Sun, Y.: Crowdsourcing Translation by Leveraging Tournament Selection and Lattice-Based String Alignment. In: AAAI Conference on Human Computation and Crowdsourcing (HCOMP), vol. WS-13-18. AAAI (2013)Google Scholar
  9. 9.
    Meilicke, C., Stuckenschmidt, H., Tamilin, A.: Supporting Manual Mapping Revision Using Logical Reasoning. In: National Conference on Artificial Intelligence (AAAI), pp. 1213–1218. AAAI Press (2008)Google Scholar
  10. 10.
    Cruz, I.F., Palandri Antonelli, F., Stroe, C.: AgreementMaker: Efficient Matching for Large Real-World Schemas and Ontologies. PVLDB 2(2), 1586–1589 (2009)Google Scholar
  11. 11.
    Cruz, I.F., Palandri Antonelli, F., Stroe, C.: Efficient Selection of Mappings and Automatic Quality-driven Combination of Matching Methods. In: ISWC International Workshop on Ontology Matching (OM). CEUR Workshop Proceedings, vol. 551, pp. 49–60 (2009)Google Scholar
  12. 12.
    Demartini, G., Difallah, D.E., Cudré-Mauroux, P.: ZenCrowd: Leveraging Probabilistic Reasoning and Crowdsourcing Techniques for Large-scale Entity Linking. In: International World Wide Web Conference (WWW), pp. 469–478. ACM, New York (2012)Google Scholar
  13. 13.
    McCann, R., Shen, W., Doan, A.: Matching Schemas in Online Communities: A Web 2.0 Approach. In: IEEE International Conference on Data Engineering (ICDE), pp. 110–119. IEEE (2008)Google Scholar
  14. 14.
    Osorno-Gutierrez, F., Paton, N.W., Fernandes, A.A.A.: Crowdsourcing Feedback for Pay-As-You-Go Data Integration. In: VLDB Workshop on Databases and Crowdsourcing (DBCrowd), vol. 1025, pp. 32–37 (2013)Google Scholar
  15. 15.
    Zhang, C.J., Chen, L., Jagadish, H.V., Cao, C.C.: Reducing Uncertainty of Schema Matching via Crowdsourcing. PVLDB 6(9), 757–768 (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Isabel F. Cruz
    • 1
  • Francesco Loprete
    • 2
  • Matteo Palmonari
    • 2
  • Cosmin Stroe
    • 1
  • Aynaz Taheri
    • 1
  1. 1.University of Illinois at ChicagoUSA
  2. 2.Università di Milano-BicoccaItaly

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