Explaining entity resolution predictions: Where are we and what needs to be done?

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

Abstract

Entity resolution (ER) seeks to identify the set of tuples in a dataset that refer to the same real-world entity. It is one of the fundamental and well studied problems in data integration with applications in diverse domains such as banking, insurance, e-commerce, and so on. Machine Learning and Deep Learning based methods provide the state-of-the-art results. For practitioners, it is often challenging to understand why the classifier made a particular prediction. While there has been extensive work in the ML community on explaining classifier predictions, we found that a direct application of those techniques is not appropriate for ER. There is a huge gap between the needs of lay ER practitioners and the explanation community. In this paper, we provide a comprehensive taxonomy of these challenges, discuss research opportunities and propose preliminary solutions.

Original languageEnglish
Title of host publicationProceedings of the Workshop on Human-In-the-Loop Data Analytics, HILDA 2019
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450367912
DOIs
Publication statusPublished - 5 Jul 2019
Event2019 Workshop on Human-In-the-Loop Data Analytics, HILDA 2019, co-located with SIGMOD 2019 - Amsterdam, Netherlands
Duration: 5 Jul 2019 → …

Publication series

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

Conference

Conference2019 Workshop on Human-In-the-Loop Data Analytics, HILDA 2019, co-located with SIGMOD 2019
CountryNetherlands
CityAmsterdam
Period5/7/19 → …

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ASJC Scopus subject areas

  • Software
  • Information Systems

Cite this

Thirumuruganathan, S., Ouzzani, M., & Tang, N. (2019). Explaining entity resolution predictions: Where are we and what needs to be done? In Proceedings of the Workshop on Human-In-the-Loop Data Analytics, HILDA 2019 [a10] (Proceedings of the ACM SIGMOD International Conference on Management of Data). Association for Computing Machinery. https://doi.org/10.1145/3328519.3329130