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

Graph-augmented Learning to Rank for Querying Large-scale Knowledge Graph