  Modeling time-evolving knowledge graphs (KGs) has recently gained increasing
interest. Here, graph representation learning has become the dominant paradigm
for link prediction on temporal KGs. However, the embedding-based approaches
largely operate in a black-box fashion, lacking the ability to interpret their
predictions. This paper provides a link forecasting framework that reasons over
query-relevant subgraphs of temporal KGs and jointly models the structural
dependencies and the temporal dynamics. Especially, we propose a temporal
relational attention mechanism and a novel reverse representation update scheme
to guide the extraction of an enclosing subgraph around the query. The subgraph
is expanded by an iterative sampling of temporal neighbors and by attention
propagation. Our approach provides human-understandable evidence explaining the
forecast. We evaluate our model on four benchmark temporal knowledge graphs for
the link forecasting task. While being more explainable, our model obtains a
relative improvement of up to 20% on Hits@1 compared to the previous best KG
forecasting method. We also conduct a survey with 53 respondents, and the
results show that the evidence extracted by the model for link forecasting is
aligned with human understanding.
