T-GAP: Learning to Walk across Time for Temporal Knowledge Graph Completion
This work provides an incremental improvement for relational reasoning in temporal knowledge graphs, which is relevant for researchers working on more realistic modeling of transient real-world knowledge.
This paper addresses temporal knowledge graph completion by proposing T-GAP, a model that leverages both temporal information and graph structure. T-GAP encodes query-specific substructures by focusing on temporal displacement and performs path-based inference via attention propagation, achieving superior performance against state-of-the-art baselines and generalizing to unseen timestamps.
Temporal knowledge graphs (TKGs) inherently reflect the transient nature of real-world knowledge, as opposed to static knowledge graphs. Naturally, automatic TKG completion has drawn much research interests for a more realistic modeling of relational reasoning. However, most of the existing mod-els for TKG completion extend static KG embeddings that donot fully exploit TKG structure, thus lacking in 1) account-ing for temporally relevant events already residing in the lo-cal neighborhood of a query, and 2) path-based inference that facilitates multi-hop reasoning and better interpretability. In this paper, we propose T-GAP, a novel model for TKG completion that maximally utilizes both temporal information and graph structure in its encoder and decoder. T-GAP encodes query-specific substructure of TKG by focusing on the temporal displacement between each event and the query times-tamp, and performs path-based inference by propagating attention through the graph. Our empirical experiments demonstrate that T-GAP not only achieves superior performance against state-of-the-art baselines, but also competently generalizes to queries with unseen timestamps. Through extensive qualitative analyses, we also show that T-GAP enjoys from transparent interpretability, and follows human intuition in its reasoning process.