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Recently there is an increasing scholarly interest in time-varying knowledgegraphs, or temporal knowledge graphs (TKG). Previous research suggests diverseapproaches to TKG reasoning that uses historical information. However, lessattention has been given to the hierarchies within such information atdifferent timestamps. Given that TKG is a sequence of knowledge graphs based ontime, the chronology in the sequence derives hierarchies between the graphs.Furthermore, each knowledge graph has its hierarchical level which may differfrom one another. To address these hierarchical characteristics in TKG, wepropose HyperVC, which utilizes hyperbolic space that better encodes thehierarchies than Euclidean space. The chronological hierarchies betweenknowledge graphs at different timestamps are represented by embedding theknowledge graphs as vectors in a common hyperbolic space. Additionally, diversehierarchical levels of knowledge graphs are represented by adjusting thecurvatures of hyperbolic embeddings of their entities and relations.Experiments on four benchmark datasets show substantial improvements,especially on the datasets with higher hierarchical levels.

Bending the Future: Autoregressive Modeling of Temporal Knowledge Graphs in Curvature-Variable Hyperbolic Spaces