Cited By
View all- Li YLi JSuhara YDoan ATan W(2023)Effective entity matching with transformersThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-023-00779-z32:6(1215-1235)Online publication date: 17-Jan-2023
Landmark Explanation: An Explainer for Entity Matching Models
Authors: 
Andrea Baraldi, Francesco Del Buono, Matteo Paganelli, Francesco GuerraAuthors Info & Claims
Abstract
State-of-the-art approaches model Entity Matching (EM) as a binary classification problem, where Machine (ML) or Deep Learning (DL) based techniques are applied to evaluate if descriptions of pairs of entities refer to the same real-world instance. Despite these approaches have experimentally demonstrated to achieve high effectiveness, their adoption in real scenarios is limited by the lack of interpretability of their behavior.
This paper showcases Landmark Explanation1, a tool that makes generic post-hoc (model-agnostic) perturbation-based explanation systems able to explain the behavior of EM models. In particular, Landmark Explanation computes local interpretations, i.e., given a description of a pair of entities and an EM model, it computes the contribution of each term in generating the prediction. The demonstration shows that the explanations generated by Landmark Explanation are effective even for non-matching pairs of entities, a challenge for explanation systems.
References
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Index Terms
- Landmark Explanation: An Explainer for Entity Matching Models
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Published In
October 2021
4966 pages
ISBN:9781450384469
DOI:10.1145/3459637
- General Chairs:
- Gianluca Demartini,
- Guido Zuccon,
- Program Chairs:
- J. Shane Culpepper,
- Zi Huang,
- Hanghang Tong
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Published: 30 October 2021
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View all- Li YLi JSuhara YDoan ATan W(2023)Effective entity matching with transformersThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-023-00779-z32:6(1215-1235)Online publication date: 17-Jan-2023
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