  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.
