  This paper presents a novel approach based on semantic parsing to improve the
performance of Knowledge Base Question Answering (KBQA). Specifically, we focus
on how to select an optimal query graph from a candidate set so as to retrieve
the answer from knowledge base (KB). In our approach, we first propose to
linearize the query graph into a sequence, which is used to form a sequence
pair with the question. It allows us to use mature sequence modeling, such as
BERT, to encode the sequence pair. Then we use a ranking method to sort
candidate query graphs. In contrast to the previous studies, our approach can
efficiently model semantic interactions between the graph and the question as
well as rank the candidate graphs from a global view. The experimental results
show that our system achieves the top performance on ComplexQuestions and the
second best performance on WebQuestions.
