  Static knowledge graph (SKG) embedding (SKGE) has been studied intensively in
the past years. Recently, temporal knowledge graph (TKG) embedding (TKGE) has
emerged. In this paper, we propose a Recursive Temporal Fact Embedding (RTFE)
framework to transplant SKGE models to TKGs and to enhance the performance of
existing TKGE models for TKG completion. Different from previous work which
ignores the continuity of states of TKG in time evolution, we treat the
sequence of graphs as a Markov chain, which transitions from the previous state
to the next state. RTFE takes the SKGE to initialize the embeddings of TKG.
Then it recursively tracks the state transition of TKG by passing updated
parameters/features between timestamps. Specifically, at each timestamp, we
approximate the state transition as the gradient update process. Since RTFE
learns each timestamp recursively, it can naturally transit to future
timestamps. Experiments on five TKG datasets show the effectiveness of RTFE.
