EXPLAINER: Entity resolution explanations

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

2 Citations (Scopus)

Abstract

Entity Resolution is a fundamental data cleaning and integration problem that has received considerable attention in the past few decades. While rule-based methods have been used in many practical scenarios and are often easy to understand, machine-learning-based methods provide the best accuracy. However, the state-of-the-art classifiers are very opaque. There has been some work towards understanding and debugging the early stages of the entity resolution pipeline, e.g. blocking and generating features (similarity scores). However, there are no such efforts for explaining the model or its predictions. In this demo, we propose ExplainER, a tool to understand and explain entity resolution classifiers with different granularity levels of explanations. Using several benchmark datasets, we will demonstrate how ExplainER can handle different scenarios for a variety of classifiers.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE 35th International Conference on Data Engineering, ICDE 2019
PublisherIEEE Computer Society
Pages2000-2003
Number of pages4
ISBN (Electronic)9781538674741
DOIs
Publication statusPublished - 1 Apr 2019
Event35th IEEE International Conference on Data Engineering, ICDE 2019 - Macau, China
Duration: 8 Apr 201911 Apr 2019

Publication series

NameProceedings - International Conference on Data Engineering
Volume2019-April
ISSN (Print)1084-4627

Conference

Conference35th IEEE International Conference on Data Engineering, ICDE 2019
CountryChina
CityMacau
Period8/4/1911/4/19

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Keywords

  • Data cleaning
  • Data cleaning explanations
  • Data integration
  • Entity resolution
  • Explainable ai

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Information Systems

Cite this

Ebaid, A., Thirumuruganathan, S., Aref, W. G., Elmagarmid, A., & Ouzzani, M. (2019). EXPLAINER: Entity resolution explanations. In Proceedings - 2019 IEEE 35th International Conference on Data Engineering, ICDE 2019 (pp. 2000-2003). [8731597] (Proceedings - International Conference on Data Engineering; Vol. 2019-April). IEEE Computer Society. https://doi.org/10.1109/ICDE.2019.00224