Knowledge graph question answering (KGQA) based on information retrieval aims to answer a question by retrieving answer from a large-scale knowledge graph. Most existing methods first roughly retrieve the knowledge subgraphs (KSG) that may contain candidate answer, and then search for the exact answer in the KSG. However, the KSG may contain thousands of candidate nodes since the knowledge graph involved in querying is often of large scale, thus decreasing the performance of answer selection. To tackle this problem, we first propose to partition the retrieved KSG to several smaller sub-KSGs via a new subgraph partition algorithm and then present a graph-augmented learning to rank model to select the top-ranked sub-KSGs from them. Our proposed model combines a novel subgraph matching networks to capture global interactions in both question and subgraphs, and an Enhanced Bilateral Multi-Perspective Matching model is proposed to capture local interactions. Finally, we apply an answer selection model on the full KSG and the top-ranked sub-KSGs respectively to validate the effectiveness of our proposed graph-augmented learning to rank method. The experimental results on multiple benchmark datasets have demonstrated the effectiveness of our approach.

翻译：基于信息检索的知识图形解答( KGQA ), 以信息检索为基础的知识图形解答( KGQA ), 目的是通过检索大型知识图解的答案来回答问题。 大多数现有方法先是检索可能包含候选答案的知识子集( KSG ), 然后在 KSG 中搜索确切答案。 但是, KSG 可能包含数千个候选节点, 因为查询中涉及的知识图通常规模很大, 从而降低答案选择的性能 。 为了解决这个问题, 我们首先提议通过一个新的子绘图分区分区分区分配算法将已回收的 KSG 分解给几个较小的次组, 然后提出图表缩略学习模型, 以便从中选择最上排位的子集子集( KSG ) 。 我们提议的模型将一个新的子图匹配网络组合起来, 以捕捉到问题和子谱中的全球互动, 并提议一个强化的双边多视角匹配模型来捕捉本地互动。 最后, 我们建议对完整的 KSG 和最排位子的子组分别应用一个答案选择模型来验证我们提议的图表缩图分析方法的效能, 测试结果 。