2006 EntityResolutionWithMarkovLogic

• (Singla & Domingos, 2006) ⇒ Parag Singla, Pedro Domingos. (2006). “Entity Resolution with Markov Logic.” In: Proceedings of the Sixth IEEE International Conference on Data Mining (ICDM 2006). doi:10.1109/ICDM.2006.65.

Subject Headings: Record Coreference Resolution Algorithm.

Cited By

• Cited by ~38 http://scholar.google.com/scholar?cites=7485871056462235110
• (Poon & Domingos) ⇒ Hoifung Poon, and Pedro Domingos. (2007). “Joint Inference in Information Extraction.” In: Proceedings of the Twenty-Second National Conference on Artificial Intelligence (AAAI 2007).
• (Yates & Etzioni, 2007) ⇒ Alexander Yates, and Oren Etzioni. (2007). “Unsupervised Resolution of Objects and Relations on the Web.” In: Proceedings of NAACL-HLT 2007.

Quotes

Abstract

• Entity resolution is the problem of determining which records in a database refer to the same entities, and is a crucial and expensive step in the data mining process. Interest in it has grown rapidly, and many approaches have been proposed. However, they tend to address only isolated aspects of the problem, and are often ad hoc. This paper proposes a well-founded, integrated solution to the entity resolution problem based on Markov logic. Markov logic combines first-order logic and probabilistic graphical models by attaching weights to first-order formulas, and viewing them as templates for features of Markov networks. We show how a number of previous approaches can be formulated and seamlessly combined in Markov logic, and how the resulting learning and inference problems can be solved efficiently. Experiments on two citation databases show the utility of this approach, and evaluate the contribution of the different components.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2006 EntityResolutionWithMarkovLogic	Pedro Domingos Parag Singla			Entity Resolution with Markov Logic			http://www.cs.washington.edu/homes/pedrod/papers/icdm06.pdf	10.1109/ICDM.2006.65