Knowledge graph question answering (i.e., 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 subgraph. However, the coarsely retrieved KSG may contain thousands of candidate nodes since the knowledge graph involved in querying is often of large scale. 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 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:以信息检索为基础的知识图形解答( 即 KGQA ), 以信息检索为基础的知识图形解答( 即 KGQA ), 旨在通过从大型知识图中检索答案解答一个问题。 大多数现有方法首先粗略地检索可能包含候选答案的知识子图解( KSG ), 然后在子图解中搜索确切答案。 然而, 粗略检索的 KSG 可能包含数千个候选节点, 因为查询所涉及的知识图通常规模很大 。 为了解决这个问题, 我们首先建议通过一个新的子图解分区分区分配算法, 将回收的 KSG 分解给几个小的子KSG, 然后提出一个图表缩放学习模型, 以便从中选择最上排位的子图解密模式 。 我们提议的模型将新的子图匹配网络组合起来, 以捕捉取问题和子图解的全球互动关系, 以及一个强化的双边多视角匹配模型 来捕捉到本地互动。 最后, 我们在完整的 KSG 和最排位子的子组中分别应用一个答案选择模型来验证我们提议的图表缩图解方法的有效性 。 实验性测试结果 。