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Unifying Logic Rules and Machine Learning for Entity Enhancing

CategoryPublications 194

Authors: Wenfei Fan, Ping Lu, Chao Tian

Published in: SCIENCE CHINA Information Sciences (SCIS 2020)

Date of Publication: June 8, 2020

Abstract

This paper proposes a notion of entity enhancing, which unifies entity resolution and conflict resolution, to identify tuples that refer to the same real-world entity and at the same time, correct semantic inconsistencies. We propose to unify rule-based and machine learning (ML) methods for entity enhancing, by embedding ML classifiers as predicates in logic rules. We model entity enhancing by extending the chase. We show that the chase warrants correctness justification and the Church-Rosser property. Moreover, we settle fundamental problems associated with entity enhancing, including the enhancing, consistency, satisfiability, and implication problems, ranging from NP-complete and coNP-complete to Πp2-complete. Taken together, these provide a new theoretical framework for unifying entity resolution and conflict resolution.

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Keywords2020Data qualitySCISWenfei Fan Previous: Next: