Entity Resolution for Probabilistic Data

Ayat Naser 1, * Reza Akbarinia 2 Hamideh Afsarmanesh 1 Patrick Valduriez 2, 3
* Corresponding author
2 ZENITH - Scientific Data Management
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier, CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : Entity resolution is the problem of identifying the tuples that represent the same real world entity. In this paper, we address the problem of entity resolution over probabilistic data (ERPD), which arises in many ap-plications that have to deal with probabilistic data. To deal with the ERPD problem, we distinguish between two classes of similarity functions, i.e. context-free and context-sensitive. We propose a PTIME algorithm for context-free similarity functions, and a Monte Carlo approximation algorithm for context-sensitive similarity functions. We also propose improvements over our proposed algorithms. We validated our algorithms through experiments over both synthetic and real datasets. Our extensive performance evaluation shows the effectiveness of our algorithms.
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Ayat Naser, Reza Akbarinia, Hamideh Afsarmanesh, Patrick Valduriez. Entity Resolution for Probabilistic Data. Information Sciences, Elsevier, 2014, 277, pp.492-511. ⟨10.1016/j.ins.2014.02.135⟩. ⟨lirmm-01076096⟩

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