  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.
