M3TQA: Multi-View, Multi-Hop and Multi-Stage Reasoning for Temporal Question Answering

Knowledge Graph (KG) have attained notable triumph over Question Answering (QA) tasks. However, the presence of temporal constraints on numerous facts within the real world has sparked heightened interest towards Temporal KGQA (TKGQA). Although previous methods have achieved great progress, they still have the following limitations: 1)PLMs cannot capture the entity drift caused by time constraints in the question. 2) Complex questions require multi-hop reasoning between entities. 3) Fusion strategies (addition or concatenation) of PLMs and KG information ignore feature differences, resulting in suboptimal solutions. To alleviate the above problems, we propose a novel Multi-view, Multi-hop and Multi-stage reasoning paradigm for TKGQA (M 3 TQA). Specifically, we first design a multi-view calibration module for fusing KG information to calibrate question representation. We next construct graph neural network in a multi-hop modeling module to capture multi-hop message passing between entities. Finally, we design multi-stage aggregation that facilitates the adaptive fusion of heterogeneous information with a two-stage interaction alignment process. The performance on two mainstream benchmark datasets verifies the effectiveness of our proposed model.