Abstract
Entity Correspondence seeks to find instances that refer to the same real world entity. Usually, a fixed set of properties exists, for each of which the similarity score is computed to support entity correspondence. However, in a knowledge base that has properties incrementally recognized, we can no longer rely only on the belief that two instances sharing value for the same property are likely to correspond with each other: a pair of different properties that are of hierarchies or specific relations can also be evidential to corresponding instances. This paper proposes the use of second-order Markov Logic to perform entity correspondence. With second-order Markov Logic, we regard properties as variables, explicitly define and exploit relations between properties and enable interaction between entity correspondence and property relation discovery. We also prove that second-order Markov Logic can be rephrased to first-order in practice. Experiments on a real world knowledge base show promising entity correspondence results, particularly in recall.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
Etzioni, O., Banko, M., Soderland, S., Weld, D.S.: Open Information Extraction from the Web. Communications of the ACM 51(12), 68–74 (2008)
Carlson, A., Betteridge, J., Kisiel, B., Hruschka Jr., E.R., Mitchell, T.M.: Toward an Architecture for Never-Ending Language Learning. In: 24th AAAI, vol. 2(4), pp. 1306–1313 (2010)
Mrabet, Y., Bennacer, N., Pernelle, N.: Controlled Knowledge Base Enrichment from Web Documents. In: Wang, X.S., Cruz, I., Delis, A., Huang, G. (eds.) WISE 2012. LNCS, vol. 7651, pp. 312–325. Springer, Heidelberg (2012)
Kok, S., Domingos, P.: Statistical Predicate Invention. In: 24th Annual International Conference on Machine Learning, pp. 433–440. ACM (2007)
Richardson, M., Domingos, P.: Markov Logic Networks. Machine Learning 62(1-2), 107–136 (2006)
Koller, D., Friedman, N.: Probabilistic Graphical Models: Principles and Techniques. MIT Press (2009)
Davis, J., Domingos, P.: Deep Transfer via Second-order Markov logic. In: 26th Annual International Conference on Machine Learning, pp. 217–224. ACM (2009)
Leivant, D.: Higher Order Logic. In: Handbook of Logic in Artificial Intelligence and Logic Programming, pp. 229–321 (1994)
McCallum, A., Nigam, K., Ungar, L.H.: Efficient Clustering of High-dimensional Data Sets with Application to Reference Matching. In: 6th ACM SIGKDD, pp. 169–178. ACM (2000)
Singla, P., Domingos, P.: Entity Resolution with Markov Logic. In: 6th International Conference on Data Mining, pp. 572–582. IEEE (2006)
Manning, C.D., Schütze, H.: Foundations of Statistical Natural Language Processing. MIT Press (1999)
Singla, P., Domingos, P.: Discriminative Training of Markov Logic Networks. In: 20th AAAI, vol. 5, pp. 868–873. AAAI Press (2005)
Fellegi, I.P., Sunter, A.B.: A Theory for Record Linkage. Journal of the American Statistical Association 64(328), 1183–1210 (1969)
Domingos, P.: Multi-Relational Record Linkage. In: Proceedings of the KDD 2004 Workshop on Multi-Relational Data Mining, pp. 31–48 (2004)
Brocheler, M., Mihalkova, L., Getoor, L.: Probabilistic Similarity Logic. Technical report, University of Maryland, College Park (2010)
Bhattacharya, I., Getoor, L.: Collective Entity Resolution in Relational Data. TKDD 1(1), 1–35 (2007)
Whang, S.E., Garcia-Molina, H.: Joint Entity Resolution. In: 28th International Conference on Data Engineering (ICDE). IEEE (2012)
Poon, H., Domingos, P.: Joint Inference in Information Extraction. In: Proceedings of the National Conference on Artificial Intelligence (2007)
Singh, S., Schultz, K., McCallum, A.: Bi-directional Joint Inference for Entity Resolution and Segmentation using Imperatively-defined Factor Graphs. In: Buntine, W., Grobelnik, M., Mladenić, D., Shawe-Taylor, J. (eds.) ECML PKDD 2009, Part II. LNCS, vol. 5782, pp. 414–429. Springer, Heidelberg (2009)
Haas, L.M., Hentschel, M., Kossmann, D., Miller, R.J.: Schema and Data: A Holistic Approach to Mapping, Resolution and Fusion in Information Integration. In: Laender, A.H.F., Castano, S., Dayal, U., Casati, F., de Oliveira, J.P.M. (eds.) ER 2009. LNCS, vol. 5829, pp. 27–40. Springer, Heidelberg (2009)
Niepert, M., Noessner, J., Meilicke, C., Stuckenschmidt, H.: Probabilistic-Logical Web Data Integration. In: Polleres, A., d’Amato, C., Arenas, M., Handschuh, S., Kroner, P., Ossowski, S., Patel-Schneider, P. (eds.) Reasoning Web 2011. LNCS, vol. 6848, pp. 504–533. Springer, Heidelberg (2011)
Whang, S.E., Garcia-Molina, H.: Entity Resolution with Evolving Rules. Proceedings of the VLDB Endowment 3(1-2), 1326–1337 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Xu, Y., Gao, Z., Wilson, C., Zhang, Z., Zhu, M., Ji, Q. (2013). Entity Correspondence with Second-Order Markov Logic. In: Lin, X., Manolopoulos, Y., Srivastava, D., Huang, G. (eds) Web Information Systems Engineering – WISE 2013. WISE 2013. Lecture Notes in Computer Science, vol 8180. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41230-1_1
Download citation
DOI: https://doi.org/10.1007/978-3-642-41230-1_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-41229-5
Online ISBN: 978-3-642-41230-1
eBook Packages: Computer ScienceComputer Science (R0)