ER 2013: Conceptual Modeling pp 212-226 | Cite as
Minimizing Human Effort in Reconciling Match Networks
Abstract
Schema and ontology matching is a process of establishing correspondences between schema attributes and ontology concepts, for the purpose of data integration. Various commercial and academic tools have been developed to support this task. These tools provide impressive results on some datasets. However, as the matching is inherently uncertain, the developed heuristic techniques give rise to results that are not completely correct. In practice, post-matching human expert effort is needed to obtain a correct set of correspondences. We study this post-matching phase with the goal of reducing the costly human effort. We formally model this human-assisted phase and introduce a process of matching reconciliation that incrementally leads to identifying the correct correspondences. We achieve the goal of reducing the involved human effort by exploiting a network of schemas that are matched against each other.We express the fundamental matching constraints present in the network in a declarative formalism, Answer Set Programming that in turn enables to reason about necessary user input. We demonstrate empirically that our reasoning and heuristic techniques can indeed substantially reduce the necessary human involvement.
Keywords
User Input Constraint Violation User Feedback Schema Match Ground AtomPreview
Unable to display preview. Download preview PDF.
References
- 1.Aberer, K., Cudré-Mauroux, P., Hauswirth, M.: Start making sense: The Chatty Web approach for global semantic agreements. Journal of Web Semantics 1(1), 89–114 (2003)CrossRefGoogle Scholar
- 2.Belhajjame, K., Paton, N., Fernandes, A.A.A., Hedeler, C., Embury, S.: User feedback as a first class citizen in information integration systems. In: CIDR, pp. 175–183 (2011)Google Scholar
- 3.Belhajjame, K., Paton, N.W., Embury, S.M., Fernandes, A.A., Hedeler, C.: Incrementally improving dataspaces based on user feedback. Information Systems 38(5), 656–687 (2013)CrossRefGoogle Scholar
- 4.Bernstein, P.A., Madhavan, J., Rahm, E.: Generic Schema Matching, Ten Years Later. PVLDB 4(11), 695–701 (2011)Google Scholar
- 5.Cudré-Mauroux, P., Aberer, K., Feher, A.: Probabilistic Message Passing in Peer Data Management Systems. In: ICDE, p. 41 (2006)Google Scholar
- 6.Do, H., Melnik, S., Rahm, E.: Comparison of schema matching evaluations. In: Proceedings of the 2nd Int. Workshop on Web Databases (German Informatics Society) (2002)Google Scholar
- 7.Do, H.H., Rahm, E.: COMA - A System for Flexible Combination of Schema Matching Approaches. In: VLDB, pp. 610–621 (2002)Google Scholar
- 8.Duchateau, F., Bellahsene, Z., Coletta, R.: Matching and Alignment: What Is the Cost of User Post-Match Effort? In: Meersman, R., Dillon, T., Herrero, P., Kumar, A., Reichert, M., Qing, L., Ooi, B.-C., Damiani, E., Schmidt, D.C., White, J., Hauswirth, M., Hitzler, P., Mohania, M. (eds.) OTM 2011, Part I. LNCS, vol. 7044, pp. 421–428. Springer, Heidelberg (2011)CrossRefGoogle Scholar
- 9.Eiter, T., Ianni, G., Krennwallner, T.: Answer set programming: A primer. In: Tessaris, S., Franconi, E., Eiter, T., Gutierrez, C., Handschuh, S., Rousset, M.-C., Schmidt, R.A. (eds.) Reasoning Web. LNCS, vol. 5689, pp. 40–110. Springer, Heidelberg (2009)CrossRefGoogle Scholar
- 10.Euzenat, J., Shvaiko, P.: Ontology matching. Springer, Heidelberg, DE (2007)MATHGoogle Scholar
- 11.Fagin, R., Haas, L.M., Hernández, M., Miller, R.J., Popa, L., Velegrakis, Y.: Clio: Schema mapping creation and data exchange. In: Borgida, A.T., Chaudhri, V.K., Giorgini, P., Yu, E.S. (eds.) Conceptual Modeling: Foundations and Applications. LNCS, vol. 5600, pp. 198–236. Springer, Heidelberg (2009)CrossRefGoogle Scholar
- 12.Gal, A.: Uncertain Schema Matching. Morgan & Calypool Publishers (2011)Google Scholar
- 13.Gal, A., Sagi, T., Weidlich, M., Levy, E., Shafran, V., Miklós, Z., Hung, N.: Making sense of top-k matchings: A unified match graph for schema matching. In: Proceedings of SIGMOD Workshop on Information Integration on the Web, IIWeb 2012 (2012)Google Scholar
- 14.Galhardas, H., Lopes, A., Santos, E.: Support for user involvement in data cleaning. In: Cuzzocrea, A., Dayal, U. (eds.) DaWaK 2011. LNCS, vol. 6862, pp. 136–151. Springer, Heidelberg (2011)CrossRefGoogle Scholar
- 15.Gelfond, M., Lifschitz, V.: The stable model semantics for logic programming. In: ICLP/SLP, pp. 1070–1080. MIT Press (1988)Google Scholar
- 16.Gelfond, M., Lifschitz, V.: Classical negation in logic programs and disjunctive databases. Journal of New Generation Computing 9(3/4), 365–386 (1991)CrossRefGoogle Scholar
- 17.Jeffery, S.R., Franklin, M.J., Halevy, A.Y.: Pay-as-you-go user feedback for dataspace systems. In: SIGMOD, pp. 847–860 (2008)Google Scholar
- 18.McCann, R., Shen, W., Doan, A.: Matching Schemas in Online Communities: A Web 2.0 Approach. In: ICDE, pp. 110–119 (2008)Google Scholar
- 19.Peukert, E., Eberius, J., Rahm, E.: AMC - A framework for modelling and comparing matching systems as matching processes. In: ICDE, pp. 1304–1307 (2011)Google Scholar
- 20.Qi, Y., Candan, K.S., Sapino, M.L.: Ficsr: feedback-based inconsistency resolution and query processing on misaligned data sources. In: SIGMOD, pp. 151–162 (2007)Google Scholar
- 21.Rahm, E., Bernstein, P.A.: A Survey of Approaches to Automatic Schema Matching. The VLDB Journal 10(4), 334–350 (2001)CrossRefMATHGoogle Scholar
- 22.Reiter, R.: A logic for default reasoning. Artificial Intelligence 13(1-2), 81–132 (1980)MathSciNetCrossRefMATHGoogle Scholar
- 23.Shvaiko, P., Euzenat, J.: Ontology matching: state of the art and future challenges. IEEE Transactions on Knowledge and Data Engineering (2012)Google Scholar
- 24.Smith, K.P., Morse, M., Mork, P., Li, M., Rosenthal, A., Allen, D., Seligman, L., Wolf, C.: The role of schema matching in large enterprises. In: CIDR (2009)Google Scholar
- 25.Su, W., Wang, J., Lochovsky, F.: Holistic schema matching for web query interfaces. In: Ioannidis, Y., Scholl, M.H., Schmidt, J.W., Matthes, F., Hatzopoulos, M., Böhm, K., Kemper, A., Grust, T., Böhm, C. (eds.) EDBT 2006. LNCS, vol. 3896, pp. 77–94. Springer, Heidelberg (2006)CrossRefGoogle Scholar
- 26.Yakout, M., Elmagarmid, A.K., Neville, J., Ouzzani, M., Ilyas, I.F.: Guided data repair. Proc. VLDB Endow. 4(5), 279–289 (2011)Google Scholar