2009 ResolvingIdentityUncertain

• (Sandler et al., 2009) ⇒ Ted Sandler, Lyle H. Ungar, Koby Crammer. (2009). “Resolving Identity Uncertainty with Learned Random Walks.” In: Proceedings of the Ninth IEEE International Conference on Data Mining (ICDM 2009). doi:10.1109/ICDM.2009.69

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Abstract

A pervasive problem in large relational databases is identity uncertainty which occurs when multiple entries in a database refer to the same underlying entity in the world. Relational databases exhibit rich graphical structure and are naturally modeled as graphs whose nodes represent entities and whose typed-edges represent relations between them. We propose using random walk models for resolving identity uncertainty since they have proven effective for finding points which are proximately located in a network. Because not all types of relations are equally helpful in alleviating identity uncertainty, we develop a supervised approach to learning the usefulness of different database relations from a training set of database entries whose true identities are known. When tested on the task of resolving uncertainty of ambiguously named authors in bibliographical data, the learned random walk models yield performance superior to support vector machines, and to a related spectral clustering method.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2009 ResolvingIdentityUncertain	Lyle H. Ungar Ted Sandler Koby Crammer			Resolving Identity Uncertainty with Learned Random Walks		ICDM 2009 Proceedings		10.1109/ICDM.2009.69		2009