Generating concise entity matching rules

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publicationSIGMOD 2017 - Proceedings of the 2017 ACM International Conference on Management of Data
PublisherAssociation for Computing Machinery
Pages1635-1638
Number of pages4
ISBN (Electronic)9781450341974
DOIs
StatePublished - May 9 2017
Event2017 ACM SIGMOD International Conference on Management of Data, SIGMOD 2017 - Chicago, United States
Duration: May 14 2017May 19 2017

Publication series

NameProceedings of the ACM SIGMOD International Conference on Management of Data
VolumePart F127746
ISSN (Print)0730-8078

Conference

Conference2017 ACM SIGMOD International Conference on Management of Data, SIGMOD 2017
CountryUnited States
CityChicago
Period5/14/175/19/17

ASJC Scopus subject areas

  • Software
  • Information Systems

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  • Cite this

    Singh, R., Meduri, V., Elmagarmid, A., Madden, S., Papotti, P., Quiané-Ruiz, J. A., Solar-Lezama, A., & Tang, N. (2017). Generating concise entity matching rules. In SIGMOD 2017 - Proceedings of the 2017 ACM International Conference on Management of Data (pp. 1635-1638). (Proceedings of the ACM SIGMOD International Conference on Management of Data; Vol. Part F127746). Association for Computing Machinery. https://doi.org/10.1145/3035918.3058739