Robust fuzzy rule base framework for entity resolution

Roger S. Gaborski; Virginia Allen; Paul Yacci

doi:10.1117/12.918898

3 May 2012 Robust fuzzy rule base framework for entity resolution

Roger S. Gaborski, Virginia Allen, Paul Yacci

Proceedings Volume 8402, Evolutionary and Bio-Inspired Computation: Theory and Applications VI; 84020N (2012) https://doi.org/10.1117/12.918898
Event: SPIE Defense, Security, and Sensing, 2012, Baltimore, Maryland, United States

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.

Citation Download Citation

Roger S. Gaborski, Virginia Allen, and Paul Yacci "Robust fuzzy rule base framework for entity resolution", Proc. SPIE 8402, Evolutionary and Bio-Inspired Computation: Theory and Applications VI, 84020N (3 May 2012); https://doi.org/10.1117/12.918898

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