Generating Concise Entity Matching Rules
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sigdm2.pdf
Description
Accepted version
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951.17 KB
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Author(s)
Singh, Rohit
Meduri, Vamsi
Elmagarmid, Ahmed
Madden, Samuel R
Papotti, Paolo
Quiané-Ruiz, Jorge-Arnulfo
Solar-Lezama, Armando
Tang, Nan
Date Issued
May 9, 2017
Publisher
ACM
Citation
Singh, Rohit, Meduri, Vamsi, Elmagarmid, Ahmed, Madden, Samuel, Papotti, Paolo et al. 2017. "Generating Concise Entity Matching Rules."
Version
Author's final manuscript
Abstract
© 2017 ACM. Entity matching (EM) is a critical part of data integration and cleaning. In many applications, the users need to understand why two entities are considered a match, which reveals the need for interpretable and concise EM rules. We model EM rules in the form of General Boolean Formulas (GBFs) that allows arbitrary attribute matching combined by conjunctions (Vee), disjunctions (Wedge), and negations (not). GBFs can generate more concise rules than traditional EM rules represented in disjunctive normal forms (DNFs). We use program synthesis, a powerful tool to automatically generate rules (or programs) that provably satisfy a high-level specification, to automatically synthesize EM rules in GBF format, given only positive and negative matching examples. In this demo, attendees will experience the following features: (1) Interpretability. they can see and measure the conciseness of EM rules defined using GBFs; (2) Easy customization. they can provide custom experiment parameters for various datasets, and, easily modify a rich predefined (default) synthesis grammar, using a Web interface; and (3) High performance. they will be able to compare the generated concise rules, in terms of accuracy, with probabilistic models (e.g., machine learning methods), and hand-written EM rules provided by experts. Moreover, this system will serve as a general platform for evaluating di.erent methods that discover EM rules, which will be released as an opensource tool on GitHub.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/3035918.3058739
