Unifying Post-hoc Explanations of Knowledge Graph Completions
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913967148892160 |
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| author | Lonardi, Alessandro Badreddine, Samy Besold, Tarek R. Martin, Pablo Sanchez |
| author_facet | Lonardi, Alessandro Badreddine, Samy Besold, Tarek R. Martin, Pablo Sanchez |
| contents | Post-hoc explainability for Knowledge Graph Completion (KGC) lacks formalization and consistent evaluations, hindering reproducibility and cross-study comparisons. This paper argues for a unified approach to post-hoc explainability in KGC. First, we propose a general framework to characterize post-hoc explanations via multi-objective optimization, balancing their effectiveness and conciseness. This unifies existing post-hoc explainability algorithms in KGC and the explanations they produce. Next, we suggest and empirically support improved evaluation protocols using popular metrics like Mean Reciprocal Rank and Hits@$k$. Finally, we stress the importance of interpretability as the ability of explanations to address queries meaningful to end-users. By unifying methods and refining evaluation standards, this work aims to make research in KGC explainability more reproducible and impactful. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_22951 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Unifying Post-hoc Explanations of Knowledge Graph Completions Lonardi, Alessandro Badreddine, Samy Besold, Tarek R. Martin, Pablo Sanchez Artificial Intelligence Machine Learning Post-hoc explainability for Knowledge Graph Completion (KGC) lacks formalization and consistent evaluations, hindering reproducibility and cross-study comparisons. This paper argues for a unified approach to post-hoc explainability in KGC. First, we propose a general framework to characterize post-hoc explanations via multi-objective optimization, balancing their effectiveness and conciseness. This unifies existing post-hoc explainability algorithms in KGC and the explanations they produce. Next, we suggest and empirically support improved evaluation protocols using popular metrics like Mean Reciprocal Rank and Hits@$k$. Finally, we stress the importance of interpretability as the ability of explanations to address queries meaningful to end-users. By unifying methods and refining evaluation standards, this work aims to make research in KGC explainability more reproducible and impactful. |
| title | Unifying Post-hoc Explanations of Knowledge Graph Completions |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2507.22951 |