Towards Synergistic Path-based Explanations for Knowledge Graph Completion: Exploration and Evaluation

Tengfei Ma, Xiang song, Wen Tao, Mufei Li, Jiani Zhang, Xiaoqin Pan, Yijun Wang, Bosheng Song, xiangxiang Zeng

Keywords: Knowledge Graph Completion, Model Explainability, Knowledge Graph Embedding, Link Prediction

TL;DR: KGExplainer explore and evaluate multiple synergistic path-based explanations to promote the transparency of KGE-based KGC models.

Abstract: Knowledge graph completion (KGC) aims to alleviate the inherent incompleteness of knowledge graphs (KGs), a crucial task for numerous applications such as recommendation systems and drug repurposing. The success of knowledge graph embedding (KGE) models provokes the question about the explainability: ``\textit{Which the patterns of the input KG are most determinant to the prediction}?'' Particularly, path-based explainers prevail in existing methods because of their strong capability for human understanding. In this paper, based on the observation that a fact is usually determined by the synergy of multiple reasoning chains, we propose a novel explainable framework, dubbed KGExplainer, to explore synergistic pathways. KGExplainer is a model-agnostic approach that employs a perturbation-based greedy search algorithm to identify the most crucial synergistic paths as explanations within the local structure of target predictions. To evaluate the quality of these explanations, KGExplainer distills an evaluator from the target KGE model, allowing for the examination of their fidelity. We experimentally demonstrate that the distilled evaluator has comparable predictive performance to the target KGE. Experimental results on benchmark datasets demonstrate the effectiveness of KGExplainer, achieving a human evaluation accuracy of 83.3\% and showing promising improvements in explainability. Code is available at \url{https://github.com/xiaomingaaa/KGExplainer}

Primary Area: interpretability and explainable AI

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Submission Number: 6102