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Proceedings Paper

Robust fuzzy rule base framework for entity resolution
Author(s): Roger S. Gaborski; Virginia Allen; Paul Yacci
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Paper Abstract

Entity resolution is an important area of research with a wide range of applications. In this paper we present a framework for developing a dynamic entity profile that is constructs as matching entity records are discovered. The proposed framework utilizes a fuzzy rule base that can match entities with a given error rate. A genetic algorithm is used to optimize an initial population of random fuzzy rule bases using a set of labeled training data. This approach demonstrated an F-score performance of 84% on a held out test set. The profiles that were linked demonstrated a configurable fitness measure to emphasis different search properties (precision or recall). The approach used for entity resolution in this framework can be extended to other applications, such as, searching for similar video files. Spatial and temporal attributes can be extracted from the video and an optimal fuzzy rule base can be evolved.

Paper Details

Date Published: 3 May 2012
PDF: 9 pages
Proc. SPIE 8402, Evolutionary and Bio-Inspired Computation: Theory and Applications VI, 84020N (3 May 2012); doi: 10.1117/12.918898
Show Author Affiliations
Roger S. Gaborski, Rochester Institute of Technology (United States)
Virginia Allen, IntelliGenesis, LLC (United States)
Paul Yacci, IntelliGenesis, LLC (United States)

Published in SPIE Proceedings Vol. 8402:
Evolutionary and Bio-Inspired Computation: Theory and Applications VI
Olga Mendoza-Schrock; Mateen M. Rizki; Todd V. Rovito, Editor(s)

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