Liu Weitong, Hasikin Khairunnisa, Khairuddin Anis Salwa Mohd, Liu Meizhen, Zhao Xuechen
School of Data and Computer Science, Shandong Women's University, Shandong, China.
Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia.
PeerJ Comput Sci. 2025 Jan 23;11:e2595. doi: 10.7717/peerj-cs.2595. eCollection 2025.
Temporal knowledge graphs (TKGs) are critical tools for capturing the dynamic nature of facts that evolve over time, making them highly valuable in a broad spectrum of intelligent applications. In the domain of temporal knowledge graph extrapolation reasoning, the prediction of future occurrences is of great significance and presents considerable obstacles. While current models consider the fact changes over time and recognize that historical facts may recur, they often overlook the influence of past events on future predictions. Motivated by these considerations, this work introduces a novel temporal knowledge graph reasoning model, named Temporal Reasoning with Recurrent Encoding and Contrastive Learning (TRCL), which integrates recurrent encoding and contrastive learning techniques. The proposed model has the ability to capture the evolution of historical facts, generating representations of entities and relationships through recurrent encoding. Additionally, TRCL incorporates a global historical matrix to account for repeated historical occurrences and employs contrastive learning to alleviate the interference of historical facts in predicting future events. The TKG reasoning outcomes are subsequently derived through a time decoder. A quantity of experiments conducted on four benchmark datasets demonstrate the exceptional performance of the proposed TRCL model across a range of metrics, surpassing state-of-the-art TKG reasoning models. When compared to the strong baseline Time-Guided Recurrent Graph Network (TiRGN) model, the proposed TRCL achieves 1.03% improvements on ICEWS14 using mean reciprocal rank (MRR) evaluation metric. This innovative proposed method not only enhances the accuracy of TKG extrapolation, but also sets a new standard for robustness in dynamic knowledge graph applications, paving the way for future research and practical applications in predictive intelligence systems.
时态知识图谱（TKGs）是捕捉随时间演变的事实动态本质的关键工具，使其在广泛的智能应用中具有极高价值。在时态知识图谱外推推理领域，预测未来事件具有重要意义且存在诸多障碍。虽然当前模型考虑了事实随时间的变化，并认识到历史事实可能会重现，但它们往往忽略了过去事件对未来预测的影响。受这些因素的启发，这项工作引入了一种新颖的时态知识图谱推理模型，名为带循环编码和对比学习的时态推理（TRCL），它集成了循环编码和对比学习技术。所提出的模型能够捕捉历史事实的演变，通过循环编码生成实体和关系的表示。此外，TRCL纳入了一个全局历史矩阵来考虑历史事件的重复出现，并采用对比学习来减轻历史事实对预测未来事件的干扰。随后通过时间解码器得出TKG推理结果。在四个基准数据集上进行的大量实验表明，所提出的TRCL模型在一系列指标上具有卓越性能，超过了现有最先进的TKG推理模型。与强大的基线时间引导循环图网络（TiRGN）模型相比，所提出的TRCL在使用平均倒数排名（MRR）评估指标时，在ICEWS14上的性能提升了1.03%。这种创新的方法不仅提高了TKG外推的准确性，还为动态知识图谱应用中的鲁棒性设定了新的标准，为预测智能系统的未来研究和实际应用铺平了道路。
