Commonsense question answering is a crucial task that requires machines to employ reasoning according to commonsense. Previous studies predominantly employ an extracting-and-modeling paradigm to harness the information in KG, which first extracts relevant subgraphs based on pre-defined rules and then proceeds to design various strategies aiming to improve the representations and fusion of the extracted structural knowledge. Despite their effectiveness, there are still two challenges. On one hand, subgraphs extracted by rule-based methods may have the potential to overlook critical nodes and result in uncontrollable subgraph size. On the other hand, the misalignment between graph and text modalities undermines the effectiveness of knowledge fusion, ultimately impacting the task performance. To deal with the problems above, we propose a novel framework: \textbf{S}ubgraph R\textbf{E}trieval Enhanced by Gra\textbf{P}h-\textbf{T}ext \textbf{A}lignment, named \textbf{SEPTA}. Firstly, we transform the knowledge graph into a database of subgraph vectors and propose a BFS-style subgraph sampling strategy to avoid information loss, leveraging the analogy between BFS and the message-passing mechanism. In addition, we propose a bidirectional contrastive learning approach for graph-text alignment, which effectively enhances both subgraph retrieval and knowledge fusion. Finally, all the retrieved information is combined for reasoning in the prediction module. Extensive experiments on five datasets demonstrate the effectiveness and robustness of our framework.

翻译：常识问答是一项关键任务，要求机器依据常识进行推理。先前研究主要采用抽取-建模范式来利用知识图谱中的信息，即首先基于预定义规则抽取相关子图，随后设计各种策略以改进所抽取结构知识的表示与融合。尽管这些方法有效，但仍面临两个挑战。一方面，基于规则方法抽取的子图可能忽略关键节点，并导致子图规模不可控。另一方面，图与文本模态之间的错位会削弱知识融合的效果，最终影响任务性能。为解决上述问题，我们提出一种新颖框架：基于图-文本对齐增强的子图检索（SEPTA）。首先，我们将知识图谱转化为子图向量数据库，并提出一种广度优先搜索风格的子图采样策略以避免信息损失，该策略利用了广度优先搜索与消息传递机制之间的类比关系。此外，我们提出一种用于图-文本对齐的双向对比学习方法，可有效增强子图检索与知识融合。最后，所有检索到的信息在预测模块中结合进行推理。在五个数据集上的大量实验证明了我们框架的有效性与鲁棒性。