  Building query graphs from natural language questions is an important step in
complex question answering over knowledge graph (Complex KGQA). In general, a
question can be correctly answered if its query graph is built correctly and
the right answer is then retrieved by issuing the query graph against the KG.
Therefore, this paper focuses on query graph generation from natural language
questions. Existing approaches for query graph generation ignore the semantic
structure of a question, resulting in a large number of noisy query graph
candidates that undermine prediction accuracies. In this paper, we define six
semantic structures from common questions in KGQA and develop a novel
Structure-BERT to predict the semantic structure of a question. By doing so, we
can first filter out noisy candidate query graphs, and then rank the remaining
candidates with a BERT-based ranking model. Extensive experiments on two
popular benchmarks MetaQA and WebQuestionsSP (WSP) demonstrate the
effectiveness of our method as compared to state-of-the-arts.
