  Conventional static knowledge graphs model entities in relational data as
nodes, connected by edges of specific relation types. However, information and
knowledge evolve continuously, and temporal dynamics emerge, which are expected
to influence future situations. In temporal knowledge graphs, time information
is integrated into the graph by equipping each edge with a timestamp or a time
range. Embedding-based methods have been introduced for link prediction on
temporal knowledge graphs, but they mostly lack explainability and
comprehensible reasoning chains. Particularly, they are usually not designed to
deal with link forecasting -- event prediction involving future timestamps. We
address the task of link forecasting on temporal knowledge graphs and introduce
TLogic, an explainable framework that is based on temporal logical rules
extracted via temporal random walks. We compare TLogic with state-of-the-art
baselines on three benchmark datasets and show better overall performance while
our method also provides explanations that preserve time consistency.
Furthermore, in contrast to most state-of-the-art embedding-based methods,
TLogic works well in the inductive setting where already learned rules are
transferred to related datasets with a common vocabulary.
