A context-aware temporal knowledge graph completion method based on logical paths

Abstract

Temporal Knowledge Graph Completion (TKGC) plays a critical role in dynamic knowledge expansion by predicting entity relationships at specific time points based on existing facts. This enhances the performance of downstream applications. Existing TKGC methods mainly generate dynamic entity representations by capturing the structural characteristics of facts and the sequential properties of data. Some approaches also attempt to extract temporal logical rules from historical query data. However, these methods do not fully account for the contextual relevance of queries, which limits the accuracy of entity predictions. To address this gap, we propose a Context-Aware TKGC method based on Logical Paths (LPCA). This method integrates embedding representations and logical rules to create an interpretable TKGC completion framework, improving both prediction transparency and accuracy. Our approach mines query-specific logical paths, incorporating a time-aware sampling mechanism that prioritizes temporally recent facts to enhance temporal relevance and adaptability. Additionally, we design a self-attention mechanism to capture enriched contextual features of these temporal logical paths, modeling dependencies among the head entity, tail entity, query relation, and associated path elements to strengthen semantic and structural relevance. Experimental results show that the proposed method improves MRR scores by 3.59%, 0.99%, and 2.67% on the ICEWS14, ICEWS18, and ICEWS05-15 datasets, respectively.