  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.
