Temporal Knowledge Graph Embedding using Householder Transformations

Sensen Zhang, Xun Liang, Simin Niu, Junlan Feng, Chen Feng, Mengwei Wang

Abstract: The rapid development of Knowledge Graph (KG) technology has led to the emergence of Temporal Knowledge Graphs (TKGs), which hold significant research importance and value. Temporal Knowledge Graph Embedding (TKGE) techniques complement TKGs and predict links within them. The efficacy of TKGE hinges upon its capability to effectively model intrinsic temporal relation patterns. However, existing methodologies often need to capture temporal relation patterns adequately or establish intrinsic connections between evolving relations. We propose a highly robust TKGE framework called HouRP to address this limitation and incorporate a more comprehensive range of relational information. This framework introduces two types of Householder transformations into TKGE. Specifically, the Householder projections enable the generation of temporal-specific representations for each entity. At the same time, the relational Householder rotations facilitate high-dimensional rotations between projected entities, thereby capturing relation-specific properties. Our proposed model’s effectiveness is demonstrated through extensive experimentation on four widely used TKGs datasets.