Monitoring Entities in an Uncertain World: Entity Resolution and Referential Integrit

Authors

  • Steven N. Minton Inferlink Corporation
  • Sofus A. Macskassy Fetch Technologies
  • Peter LaMonica U. S. Air Force Research Laboratory
  • Kane See Fetch Technologies
  • Craig A. Knoblock University of Southern California
  • Greg Barish noreply@aaai.org
  • Matthew Michelson Fetch Technologies
  • Raymond Liuzzi Raymond Technologies

DOI:

https://doi.org/10.1609/aaai.v25i2.18860

Abstract

This paper describes a system to help intelligence analysts track and analyze information being published in multiple sources, particularly open sources on the Web. The system integrates technology for Web harvesting, natural language extraction, and network analytics, and allows analysts to view and explore the results via a Web application. One of the difficult problems we address is the entity resolution problem, which occurs when there are multiple, differing ways to refer to the same entity. The problem is particularly complex when noisy data is being aggregated over time, there is no clean master list of entities, and the entities under investigation are intentionally being deceptive. Our system must not only perform entity resolution with noisy data, but must also gracefully recover when entity resolution mistakes are subsequently corrected. We present a case study in arms trafficking that illustrates the issues, and describe how they are addressed.

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Published

2011-08-11

How to Cite

Minton, S., Macskassy, S., LaMonica, P., See, K., Knoblock, C., Barish, G., Michelson, M., & Liuzzi, R. (2011). Monitoring Entities in an Uncertain World: Entity Resolution and Referential Integrit. Proceedings of the AAAI Conference on Artificial Intelligence, 25(2), 1681-1688. https://doi.org/10.1609/aaai.v25i2.18860