CEGRL-TKGR: A Causal Enhanced Graph Representation Learning Framework for Temporal Knowledge Graph Reasoning

• URL: http://arxiv.org/abs/2408.07911v2
• Date: Fri, 24 Jan 2025 10:37:25 GMT
• Title: CEGRL-TKGR: A Causal Enhanced Graph Representation Learning Framework for Temporal Knowledge Graph Reasoning
• Authors: Jinze Sun, Yongpan Sheng, Lirong He, Yongbin Qin, Ming Liu, Tao Jia,
• Abstract summary: We propose an innovative Causal Enhanced Graph Representation Learning framework for TKGR (named CEGRL-TKGR) This framework introduces causal structures in graph-based representation learning to unveil the essential causal relationships between events. We also advocate the utilization of causal representations for predictions, aiming to mitigate the effects of erroneous correlations caused by confounding features.
• Score: 7.738230390425271
• License:
• Abstract: Temporal knowledge graph reasoning (TKGR) is increasingly gaining attention for its ability to extrapolate new events from historical data, thereby enriching the inherently incomplete temporal knowledge graphs. Existing graph-based representation learning frameworks have made significant strides in developing evolving representations for both entities and relational embeddings. Despite these achievements, there's a notable tendency in these models to inadvertently learn biased data representations and mine spurious correlations, consequently failing to discern the causal relationships between events. This often leads to incorrect predictions based on these false correlations. To address this, we propose an innovative Causal Enhanced Graph Representation Learning framework for TKGR (named CEGRL-TKGR). This framework introduces causal structures in graph-based representation learning to unveil the essential causal relationships between events, ultimately enhancing the performance of the TKGR task. Specifically, we first disentangle the evolutionary representations of entities and relations in a temporal knowledge graph sequence into two distinct components, namely causal representations and confounding representations. Then, drawing on causal intervention theory, we advocate the utilization of causal representations for predictions, aiming to mitigate the effects of erroneous correlations caused by confounding features, thus achieving more robust and accurate predictions. Finally, extensive experimental results on six benchmark datasets demonstrate the superior performance of our model in the link prediction task.

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