A Network Analysis Model for Disambiguation of Names in Lists

Bradley Malin (), Edoardo Airoldi () and Kathleen M. Carley ()
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Bradley Malin: Carnegie Mellon University
Edoardo Airoldi: Carnegie Mellon University
Kathleen M. Carley: School of Computer Science, Carnegie Mellon University

Computational and Mathematical Organization Theory, 2005, vol. 11, issue 2, No 3, 119-139

Abstract: Abstract In research and application, social networks are increasingly extracted from relationships inferred by name collocations in text-based documents. Despite the fact that names represent real entities, names are not unique identifiers and it is often unclear when two name observations correspond to the same underlying entity. One confounder stems from ambiguity, in which the same name correctly references multiple entities. Prior name disambiguation methods measured similarity between two names as a function of their respective documents. In this paper, we propose an alternative similarity metric based on the probability of walking from one ambiguous name to another in a random walk of the social network constructed from all documents. We experimentally validate our model on actor-actor relationships derived from the Internet Movie Database. Using a global similarity threshold, we demonstrate random walks achieve a significant increase in disambiguation capability in comparison to prior models.

Keywords: disambiguation; social networks; link analysis; random walks; clustering (search for similar items in EconPapers)
Date: 2005
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10588-005-3940-3

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