CEGRL - TKGR : A Causal Enhanced Graph Representation Learning Framework for Temporal Knowledge Graph Reasoning (2025)

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 C ausal E nhanced G raph R epresentation L earning 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.

## introduction knowledge graphs (kgs) have gained significant promise in natural language processing or knowledge engineering perception tasks @xcite . they model real-world factual knowledge using multi-relationship graph structures. however, factual knowledge in reality is constantly evolving, resulting in the form of event knowledge. this has led to the development and application of temporal knowledge graphs (tkgs). tkg encodes the relationship information of entities and events and their timing for capturing the dynamics of entities and their relationships over time @xcite . thus, analyzing the tkg provides a comprehensive understanding of the evolving events, based on which various time-dependent applications have been developed, including time-sensitive semantic search @xcite , policy making @xcite , stock forecasting @xcite , and more @xcite . the reliability of applications depends on accurate predicting, which highly relies on data integrality. however, existing tkgs are inevitably incomplete due to the partial observation of realworld @xcite . to address this limitation and enhance the representation capability of the tkg, temporal knowledge graph reasoning (tkgr) models are proposed and aim to extrapolate new facts and relationships in the tkg according to their historical temporal information. existing models explore different strategies to achieve satisfactory results on the tkgr task. ghnn @xcite and ght @xcite model historical facts as point-in-time processes. tkgr-rhetne @xcite jointly models the relevant historical event and temporal neighborhood event context of events in the tkg. re-net @xcite and re-gcn @xcite introduce graph neural networks (gnn) into sequence models to capture structural and temporal dependencies between entities. tkgr-gprscl @xcite captures complex structure-aware information by encoding paths across entities and obtaining temporal correlations in the complex plane. tlogic @xcite and titer @xcite design interpretable models based on logical rules and reinforcement learning, respectively. despite the achievements of previous studies, they have overlooked the reality that there are numerous confounding factors in the tkg, such as shallow patterns and noisy links. however, these confounding factors commonly misguide the reasoning process in the tkg, resulting in the acquisition of incorrect dependencies and the generation of non-causal predictions @xcite . to address the aforementioned issues, we advocate for the integration of causal theory into tkgr to guide learning of the essential causal relationships between events and mitigate the impact of confounding factors on the tkgr task. specifically, we first construct a structural causal model @xcite to comprehensively analyze and model the tkgr task from a causal perspective. then, based on the causal model, we propose a new framework, namely causal enhanced graph representation learning (cegrl-tkgr), to disentangle confounding factors from the essential causal factors in the tkg. to the best of our knowledge, this is the first study to incorporate causal intervention in a graph representation learning framework for learning the evolutionary representations of entities and relations in the tkg. to conclude, our contributions in this paper are 3folds: ## conclusion in this paper, we revisit the gnn-based tkgr model from the causality perspective, on this basis, we propose a novel cegrl-tkgr framework. by synergistically integrating causal structures with graph representation learning of the tkg, our framework overcomes the problem of existing tkgr models' learning biased data representations and mining for false correlations unintentionally. comprehensive experimental results and analysis have proved the effectiveness of cegrl-tkgr. limitations and future work. the proposed cegrl-tkgr is an innovational causal enhanced graph representation learning framework for optimizing feature representations directly using causal technology for the tgkr task. the limitations of cegrl-tkgr are as follows: