TRHyTE: Temporal Knowledge Graph Embedding Based on Temporal-Relational Hyperplanes

Temporal Knowledge Graph Embedding (TKGE) aims at encoding evolving facts with high-dimensional vectorial representations. Although a representative hyperplane-based TKGE approach, namely HyTE, has achieved remarkable performance, it still suffers from several problems including (i) ignorance of latent temporal properties and diversity of relations; (ii) neglect of temporal dependency between adjacent hyperplanes; (iii) inefficient static random negative sampling method; (iv) incomplete testing on partial time information. To address these issues, we propose TRHyTE, a novel Temporal-Relational Hyperplane based TKGE model, which defines three typical properties, including interval, open-interval, and instantaneousness temporal, for relations and correspondingly constructs three relational sub-KGs, supporting distinguishing learning for facts. Within each sub-KG, TRHyTE transforms entities into relation space first, and then explicitly projects transformed entities and relations into temporal-relational hyperplanes to learn time-relation-aware embeddings. Moreover, Gate Recurrent Unit is leveraged to simulate TKG evolution so as to capture temporal dependency between adjacent hyperplanes. Additionally, we develop a dynamic negative samples mechanism for robust training. In testing phase, an expand-and-best-merge strategy is crafted to realize a complete testing on all valid time intervals. Extensive experiments on two well-known benchmarks verify the effectiveness of our proposals.