Synthesizing Entity Matching Rules by Examples

Entity matching (EM) is a critical part of data integration. We study how to synthesize entity matching rules from positive-negative matching examples. The core of our solution is program synthesis, a powerful tool to automatically generate rules (or programs) that satisfy a given highlevel specification, via a predefined grammar. This grammar describes a General Boolean Formula (GBF) that can include arbitrary attribute matching predicates combined by conjunctions (Ź), disjunctions (Ž) and negations ( ), and is expressive enough to model EM problems, from capturing arbitrary attribute combinations to handling missing attribute values. The rules in the form of GBF are more concise than traditional EM rules represented in Disjunctive Normal Form (DNF). Consequently, they are more interpretable than decision trees and other machine learning algorithms that output deep trees with many branches. We present a new synthesis algorithm that, given only positivenegative examples as input, synthesizes EM rules that are effective over the entire dataset. Extensive experiments show that we outperform other interpretable rules (e.g., decision trees with low depth) in effectiveness, and are comparable with non-interpretable tools (e.g., decision trees with high depth, gradient-boosting trees, random forests and SVM).

Rohit Singh, Venkata Vamsikrishna Meduri, Ahmed K. Elmagarmid, Samuel Madden, Paolo Papotti, Jorge-Arnulfo Quiané-Ruiz, Armando Solar-Lezama, Nan Tang

PVLDB

‘Synthesizing Entity Matching Rules by Examples’ (PDF)

Uber AI