Learning Knowledge Evolution with Time Duration from Finer-Time-Granularity Temporal Knowledge Graph

How to represent the temporal information corresponding to each fact in temporal knowledge graphs (TKGs) effectively is always challenging. Most existing representation learning methods usually map the timelines of knowledges into multiple points and ignore the underlying continuous time property and periodic characteristics behind those dense timestamps in finer-time granularity temporal knowledge graphs. A novel Knowledge Evolution with Time Duration (KETD) model is proposed for knowledge representation of temporal knowledge graphs with finer-time granularities. It represents the entity embedding as a time-varying nonlinear function to find the specific continuous time property from dense timestamps. To the best of our knowledge, it is the first attempt to learn embeddings of time durations and combines them into embeddings of entities and relations to predict potential periodic facts. It also uses the temporal point process to capture the impact of historical facts on the current fact. The experiments on self-built finer-time granularity TKG of Satellite-to-Earth Communication (STEC) and two public datasets have demonstrated the superiority of KETD compared to some baseline approaches.