Hierarchical Hypergraph Recurrent Attention Network for Temporal Knowledge Graph Reasoning

Abstract

Temporal knowledge graph (TKG) serves as an essential tool in modeling complex event relations among real-world entities. A temporal knowledge graph can be viewed as a collection of knowledge graph snapshots ordered by time. Reasoning over such graphs remains nontrivial as temporal causal dependencies between events are hard to capture. Current TKG reasoning methods only model pair-wise relations, which are limited in capturing higher-order dependencies between entities that are beyond dyadic connections. In this work, we aim to capture higher-order interactions of entities for TKG reasoning. To achieve this goal, we develop a Hierarchical Hypergraph Recurrent Attention Network on the type-induced entity hypergraph with multiple hierarchies to model the evolutionary pattern under different semantic granularities. The experimental analysis on benchmark datasets demonstrates the proposed model's superiority and elucidates the rationality of the hierarchical hypergraph modeling.