It is well acknowledged that incorporating explicit knowledge graphs (KGs) can benefit question answering. Existing approaches typically follow a grounding-reasoning pipeline in which entity nodes are first grounded for the query (question and candidate answers), and then a reasoning module reasons over the matched multi-hop subgraph for answer prediction. Although the pipeline largely alleviates the issue of extracting essential information from giant KGs, efficiency is still an open challenge when scaling up hops in grounding the subgraphs. In this paper, we target at finding semantically related entity nodes in the subgraph to improve the efficiency of graph reasoning with KG. We propose a grounding-pruning-reasoning pipeline to prune noisy nodes, remarkably reducing the computation cost and memory usage while also obtaining decent subgraph representation. In detail, the pruning module first scores concept nodes based on the dependency distance between matched spans and then prunes the nodes according to score ranks. To facilitate the evaluation of pruned subgraphs, we also propose a graph attention network (GAT) based module to reason with the subgraph data. Experimental results on CommonsenseQA and OpenBookQA demonstrate the effectiveness of our method.

翻译：论文摘要：众所周知，融入显式知识图谱能够提升问答系统的性能。现有方法通常遵循“基础-推理”流水线：首先将查询（问题及候选答案）中的实体节点进行基准化，随后通过推理模块对匹配的多跳子图进行推理以预测答案。尽管这一流程显著缓解了从大规模知识图谱中提取关键信息的难题，但在扩展子图基准化的跳数时，效率问题仍是一个开放性挑战。本文旨在通过寻找子图中语义相关的实体节点，提升基于知识图谱的图推理效率。我们提出一种“基准化-剪枝-推理”（Grounding-Pruning-Reasoning）流水线，通过剪枝噪声节点大幅降低计算成本与内存占用，同时获得高质量的子图表示。具体而言，剪枝模块首先依据匹配跨度间的依存距离对概念节点进行评分，再根据评分排序对节点实施剪枝。为促进剪枝子图的评估，我们还提出一种基于图注意力网络的模块用于子图推理。在CommonsenseQA与OpenBookQA数据集上的实验结果验证了本方法的有效性。