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A Network Analysis Model for Disambiguation of Names in Lists

Author

Listed:
  • Bradley Malin

    (Carnegie Mellon University
    School of Computer Science, Carnegie Mellon University)

  • Edoardo Airoldi

    (Carnegie Mellon University)

  • Kathleen M. Carley

    (School of Computer Science, Carnegie Mellon University)

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.

Suggested Citation

  • Bradley Malin & Edoardo Airoldi & Kathleen M. Carley, 2005. "A Network Analysis Model for Disambiguation of Names in Lists," Computational and Mathematical Organization Theory, Springer, vol. 11(2), pages 119-139, July.
    • Handle: RePEc:spr:comaot:v:11:y:2005:i:2:d:10.1007_s10588-005-3940-3
    DOI: 10.1007/s10588-005-3940-3
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    Cited by:

    1. Moradabadi, Behnaz & Meybodi, Mohammad Reza, 2017. "A novel time series link prediction method: Learning automata approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 422-432.
    2. Jan Schulz, 2016. "Using Monte Carlo simulations to assess the impact of author name disambiguation quality on different bibliometric analyses," Scientometrics, Springer;Akadémiai Kiadó, vol. 107(3), pages 1283-1298, June.

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