Thoughtful and cautious reasoning: A fine-tuned knowledge graph-based multi-hop question answering framework

The aim of Knowledge Graph Question Answering (KGQA) is to find the answer entity by utilizing the Knowledge Graph (KG). Despite remarkable successes in recent years, the existing multi-hop KGQA research still faces numerous challenges. First, a multi-hop question often contains multiple entities and their relationships, and the semantic information is complex. The current methods extract the semantics of the question through an encoder that cannot completely extract the complex and rich semantic information in the multi-hop questions. Second, current question answering models use the coarse information filtering mechanism in the process of reasoning, which lead to the loss of effective information and introduce additional noise. To address these issues, we propose a Thoughtful and Cautious Reasoning framework for Knowledge Graph Question Answering (TCR-KGQA). We design a new question encoder that can extract and fully fuse the local semantic information of the question at different levels, focusing on the unique local features of the multi-hop question text. Based on the advantages of Gated Recurrent Unit (GRU) for information filtering, we propose a loop instruction update framework based on residual-GRU to effectively capture key information in the reasoning process. Extensive experiments on three broad benchmark datasets demonstrate the effectiveness of our model on KGQA tasks, and it also yields excellent results in the case of incomplete knowledge graphs with missing question–answer pairs.