Context-aware temporal knowledge graph embedding

Knowledge graph embedding (KGE) is an important technique used for knowledge graph completion (KGC). However, knowledge in practice is time-variant and many relations are only valid for a certain period of time. This phenomenon highlights the importance of temporal knowledge graph embeddings. Currently, existing temporal KGE methods only focus on one aspect of facts, i.e., the factual plausibility, while ignoring the other aspect, i.e., the temporal consistency. Temporal consistency models the interactions between a fact and its contexts, and thus is able to capture fine-granularity temporal relationships, such as temporal orders, temporal distances and overlapping. In order to determine the useful contexts for the fact to be predicted, we propose a two-way strategy for context selection. In particular, we decompose the target fact into two parts, relation and entities, and measure the usefulness of a context for each part respectively. Furthermore, we adopt deep neural networks to encode contexts and score the temporal consistency. This consistency is used with factual plausibility to model a fact. Due to the incorporation of temporal information and the interactions between facts and contexts, our model learns a more representative embeddings for temporal KG. We conduct extensive experiments on real world datasets and the experimental results verify the effectiveness of our proposals.