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

Saravanan Thirumuruganathan; Mourad Ouzzani; Nan Tang

doi:10.1145/3328519.3329130

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

Saravanan Thirumuruganathan, Mourad Ouzzani, Nan Tang

Qatar Computing Research Institute

Hamad bin Khalifa University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

14 Citations (Scopus)

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 language	English
Title of host publication	Proceedings of the Workshop on Human-In-the-Loop Data Analytics, HILDA 2019
Publisher	Association for Computing Machinery
ISBN (Electronic)	9781450367912
DOIs	https://doi.org/10.1145/3328519.3329130
Publication status	Published - 5 Jul 2019
Event	2019 Workshop on Human-In-the-Loop Data Analytics, HILDA 2019, co-located with SIGMOD 2019 - Amsterdam, Netherlands Duration: 5 Jul 2019 → …

Publication series

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

Conference

Conference	2019 Workshop on Human-In-the-Loop Data Analytics, HILDA 2019, co-located with SIGMOD 2019
Country/Territory	Netherlands
City	Amsterdam
Period	5/07/19 → …

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10.1145/3328519.3329130

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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 Article a10 (Proceedings of the ACM SIGMOD International Conference on Management of Data). Association for Computing Machinery. https://doi.org/10.1145/3328519.3329130

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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.",

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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, 2019 Workshop on Human-In-the-Loop Data Analytics, HILDA 2019, co-located with SIGMOD 2019, Amsterdam, Netherlands, 5/07/19. https://doi.org/10.1145/3328519.3329130

Explaining entity resolution predictions: Where are we and what needs to be done? / Thirumuruganathan, Saravanan; Ouzzani, Mourad; Tang, Nan.
Proceedings of the Workshop on Human-In-the-Loop Data Analytics, HILDA 2019. Association for Computing Machinery, 2019. a10 (Proceedings of the ACM SIGMOD International Conference on Management of Data).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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

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