Temporal knowledge graph question answering via subgraph reasoning

Knowledge graph question answering (KGQA) has recently received a lot of attention and many innovative methods have been proposed in this area, but few have been developed for temporal KGQA. Most of the existing temporal KGQA methods focus on semantic or temporal level matching and lack the ability to reason about time constraints. In this paper we propose a subgraph-based model for answering complex questions over temporal knowledge graphs (TKG), inspired by human cognition. Our method, called SubGraph Temporal Reasoning (), consists of three main modules: implicit knowledge extraction, relevant facts search, and subgraph logic reasoning. First, the question is reformulated using background knowledge stored in the temporal knowledge graph to acquire explicit time constraints. Then, the TKG is being searched to identify relevant entities and obtain an initial scoring of them. Finally the time constraints are quantified and applied using temporal logic to reach to the final answer. To evaluate our model we experiment against temporal QA benchmarks. We observe that existing benchmarks contain many pseudo-temporal questions, and we propose , which a filtered version of and which can better demonstrate the model’s inference ability for complex temporal questions. Experimental results show that achieves state-of-the-art performance on both and . Moreover, our model shows better performance in handling the entity cold-start problem compared to existing temporal KGQA methods.