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.
@article{arxiv.2507.22951,
title = {Unifying Post-hoc Explanations of Knowledge Graph Completions},
author = {Alessandro Lonardi and Samy Badreddine and Tarek R. Besold and Pablo Sanchez Martin},
journal= {arXiv preprint arXiv:2507.22951},
year = {2025}
}