A Dynamic Graph Reasoning Model with an Auxiliary Task for Knowledge Base Question Answering

In the field of question answering (QA), the methods of large language models (LLMs) cannot learn vertical domain knowledge during the pre-training stage, leading to low accuracy in domain QA. Conversely, knowledge base question answering (KBQA) can combine the knowledge base (KB) that contains domain knowledge with small language models to achieve high accuracy with a low cost. In KBQA, the inference subgraph is composed of entity nodes and their relationships pertinent to the question, with the final answers being derived from the subgraph. However, there are still two critical problems in this field: (i) fixed or decreased scopes of the inference subgraphs over the reasoning process may lead to limited knowledge, restricted in KBQA, and (ii) a lack of alignment between the inference subgraph and the question leads to low accuracy. In this work, we propose a dynamic graph reasoning model with an auxiliary task, the DGRMWAT, which addresses the above challenges through two key innovations, as follows: (i) dynamic graph reasoning, whereby we update the scope of the inference subgraph during each reasoning step to obtain more relevant knowledge and reduce irrelevant knowledge, and (ii) an auxiliary task to enhance the correlation between the inference subgraph and the question by computing the similarities between the inference subgraph and the QA context node. The experiments on two QA benchmark datasets, CommonsenseQA and OpenbookQA, indicate that the DGRMWAT allowed improvements compared to the baseline models and LLMs.