提出新方法捕捉知识图谱中实体关系的动态演化模式。
DECRL: A Deep Evolutionary Clustering Jointed Temporal Knowledge Graph Representation Learning Approach
- 用深度演化聚类建模实体间高阶关联随时间的变化。
- 在7个真实数据集上，各项指标平均提升超10%。
- 适合研究动态知识图谱与时序数据挖掘的学者。
时序知识图谱表示学习旨在将随时间演化的实体与关系映射到低维连续向量空间。然而，现有方法难以捕捉知识图谱中高阶关联的时序演化特性。为此，本文提出一种深度演化聚类联合时序知识图谱表示学习方法（DECRL）。具体而言，设计深度演化聚类模块以捕获实体间高阶关联的时序变化；引入聚类感知的无监督对齐机制，确保不同时刻软重叠聚类间精确的一一对应，从而保持聚类的时序平滑性；此外，通过全局图引导的隐式关联编码器，捕捉任意两聚类间的潜在关联。在七个真实世界数据集上的大量实验表明，DECRL达到当前最优性能，相较最佳基线在MRR、Hits@1、Hits@3和Hits@10上平均提升9.53%、12.98%、10.42%和14.68%。
原文摘要 · Abstract (English)
Temporal Knowledge Graph (TKG) representation learning aims to map temporal evolving entities and relations to embedded representations in a continuous low-dimensional vector space. However, existing approaches cannot capture the temporal evolution of high-order correlations in TKGs. To this end, we propose a Deep Evolutionary Clustering jointed temporal knowledge graph Representation Learning approach (DECRL). Specifically, a deep evolutionary clustering module is proposed to capture the temporal evolution of high-order correlations among entities. Furthermore, a cluster-aware unsupervised alignment mechanism is introduced to ensure the precise one-to-one alignment of soft overlapping clusters across timestamps, thereby maintaining the temporal smoothness of clusters. In addition, an implicit correlation encoder is introduced to capture latent correlations between any pair of clusters under the guidance of a global graph. Extensive experiments on seven real-world datasets demonstrate that DECRL achieves the state-of-the-art performances, outperforming the best baseline by an average of 9.53%, 12.98%, 10.42%, and 14.68% in MRR, Hits@1, Hits@3, and Hits@10, respectively.
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