KGC-Explainer: Toward Explainable Knowledge Graph Completion

Chengbin Hou

doi:10.1109/TR.2025.3650538

KGC-Explainer: Toward Explainable Knowledge Graph Completion

2026-01-01

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AI

William Cheng‐Chung Chu

DOI：10.1109/TR.2025.3650538

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Abstract

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Knowledge graph completion (KGC) aims to infer missing triples for a given knowledge graph, which can be adopted to various fields ranging from scientific research to real-world applications. Despite the success of numerous KGC methods, the lack of explainability remains a common drawback. However, the explanation of KGC results is crucial in many cases, such as providing medical diagnosis and recommending candidates for costly experiments, which could increase the reliability of these techniques to humans. Although a few explainable KGC methods have been proposed, the explainability module is designed for the specific KGC model and cannot be utilized for state-of-the-art KGC models. In this work, we present a post-hoc generic method, namely KGC-Explainer, that can be applied to any KGC model providing triplet scores. KGC-Explainer not only incorporates the input KGC model itself, but also leverages the structural and textual information in knowledge graphs. To demonstrate KGC-Explainer achieving design goals and the superiority of it over other methods, we conduct extensive experiments on two real-world knowledge graphs with different domains and languages, one coming from medical domain in Chinese and another coming from general domain in English. In addition, we compare the KGC explanation with human explanation, showcasing the practical significance of KGC-Explainer.

Keywords:

Explainability

graph learning

knowledge graph completion (KGC)

knowledge graphs

trainable mask

KGC-Explainer: Toward Explainable Knowledge Graph Completion

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