Hyperbolic-based Feature Learning for Temporal Knowledge Graph Relation Prediction

In the realm of real-world knowledge graphs, the dynamism of facts is a prevailing characteristic. To illustrate, a popular restaurant was awarded a Michelin star in 2004 and retained this prestigious recognition in 2008, but lost it in 2012 due to changes in management and menu quality. This sequence highlights how neglecting temporal context can lead to misconceptions about factual accuracy. Furthermore, the relations intertwining distinct entities or the same entity across different chronological markers exhibit complexity and hierarchy. Regrettably, existing methods for temporal knowledge graph relation prediction fall short in following challenges: they lack a nuanced, hierarchical comprehension of knowledge structure and fail to adeptly integrate temporal dynamics with static attributes. Addressing these issues, this study introduces Hyperbolic-based Temporal Knowledge Graph Relation Prediction (HTKGP) approach to harness the power of hyperbolic geometry. Our innovation is an attention-guided, learnable curvature mechanism designed to preserve and enrich the intricate semantic hierarchy inherent in data. Besides, we propose a longitudinal information entity embedding strategy due to the plentiful temporal information. This not only captures the enduring impact of past events on present states but also achieves efficiency through parameter reduction. Empirical validation across multiple datasets shows HTKGP efficiently navigates the rich semantic landscape within hyperbolic spaces and yields superior predictive performance. Our implementations are publicly available at: https://github.com/jianruichen/HTKGP .