Provide explainable clues: A generative traceable method for knowledge graph completion

Abstract

Improving the quality of Knowledge Graph Completion (KGC) results is an essential topic in the field of knowledge graphs. Recently, generative models (GMs) have gained widespread attention for addressing the generalization issues of traditional approaches. However, the black-box nature of generative models often leads to hallucinations, which reduce the model’s performance. Most methods attempt to mitigate this issue through retrieval enhancement and decoding constraints. However, they overlook one major cause of hallucinations–poor explainability. Based on this concept, we propose a Generative Traceable Method, namely GTM, which aims to improve the KGC capability of GMs by exploring the inhibitory effect of explainability on hallucinations. In GTM, a clue tracker is used to find contextual evidence for explainability. In addition, to measure explainability clues, we propose a context-aware analyzer, which enhances the understanding of context through group analogy. In the reasoning phase, we ensure the validity of the generated results by integrating the interpretive capability of clues. Extensive experiments have demonstrated that GTM can adapt to various KGC tasks and significantly enhance the performance of KGC models.