Integrating Subgraph-aware Relation and DirectionReasoning for Question Answering
This work improves question answering models for knowledge bases by better utilizing subgraph structure and directionality, though it appears incremental as it builds on existing neural approaches.
The paper tackled the problem of question answering over knowledge bases by addressing the oversight of subgraph structure and direction information in reasoning, resulting in substantial improvements on two widely used datasets.
Question Answering (QA) models over Knowledge Bases (KBs) are capable of providing more precise answers by utilizing relation information among entities. Although effective, most of these models solely rely on fixed relation representations to obtain answers for different question-related KB subgraphs. Hence, the rich structured information of these subgraphs may be overlooked by the relation representation vectors. Meanwhile, the direction information of reasoning, which has been proven effective for the answer prediction on graphs, has not been fully explored in existing work. To address these challenges, we propose a novel neural model, Relation-updated Direction-guided Answer Selector (RDAS), which converts relations in each subgraph to additional nodes to learn structure information. Additionally, we utilize direction information to enhance the reasoning ability. Experimental results show that our model yields substantial improvements on two widely used datasets.