research-article

Kelpie: an explainability framework for embedding-based link prediction models

Authors:

Donatella Firmani,

Paolo Merialdo,

Tommaso TeofiliAuthors Info & Claims

Proceedings of the VLDB Endowment, Volume 15, Issue 12

Pages 3566 - 3569

https://doi.org/10.14778/3554821.3554845

Published: 01 August 2022 Publication History

Abstract

The latest generations of Link Prediction (LP) models rely on embeddings to tackle incompleteness in Knowledge Graphs, achieving great performance at the cost of interpretability. Their opaqueness limits the trust that users can place in them, hindering their adoption in real-world applications. We have recently introduced Kelpie, an explainability framework tailored specifically for embedding-based LP models. Kelpie can be applied to any embedding-based LP model, and supports two explanation scenarios that we have called necessary and sufficient. In this demonstration we showcase Kelpie's capability to explain the predictions of models based on vastly different architectures on the 5 major datasets in literature.

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Cited By

Zhang JYing RShao J(2024)Online Detection of Anomalies in Temporal Knowledge Graphs with InterpretabilityProceedings of the ACM on Management of Data10.1145/36988232:6(1-26)Online publication date: 20-Dec-2024
https://dl.acm.org/doi/10.1145/3698823
Sousa RSilva SPesquita CMarquis PSon TKern-Isberner G(2023)Explainable representations for relation prediction in knowledge graphsProceedings of the 20th International Conference on Principles of Knowledge Representation and Reasoning10.24963/kr.2023/62(635-646)Online publication date: 2-Sep-2023
https://dl.acm.org/doi/10.24963/kr.2023/62
Wang YSachurengui SZhang DAziguli Wulamu Bao H(2023)A Factor Marginal Effect Analysis Approach and Its Application in E-Commerce Search SystemInternational Journal of Intelligent Systems10.1155/2023/69688542023Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1155/2023/6968854

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Information

Published In

cover image Proceedings of the VLDB Endowment

Proceedings of the VLDB Endowment Volume 15, Issue 12

August 2022

551 pages

ISSN:2150-8097

Editors:
Fatma Özcan
Google
,
Juliana Freire
New York University
,
Xuemin Lin
University of New South Wales

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VLDB Endowment

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Published: 01 August 2022

Published in PVLDB Volume 15, Issue 12

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Cited By

Zhang JYing RShao J(2024)Online Detection of Anomalies in Temporal Knowledge Graphs with InterpretabilityProceedings of the ACM on Management of Data10.1145/36988232:6(1-26)Online publication date: 20-Dec-2024
https://dl.acm.org/doi/10.1145/3698823
Sousa RSilva SPesquita CMarquis PSon TKern-Isberner G(2023)Explainable representations for relation prediction in knowledge graphsProceedings of the 20th International Conference on Principles of Knowledge Representation and Reasoning10.24963/kr.2023/62(635-646)Online publication date: 2-Sep-2023
https://dl.acm.org/doi/10.24963/kr.2023/62
Wang YSachurengui SZhang DAziguli Wulamu Bao H(2023)A Factor Marginal Effect Analysis Approach and Its Application in E-Commerce Search SystemInternational Journal of Intelligent Systems10.1155/2023/69688542023Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1155/2023/6968854

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