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.

本研究主要关注于如何从自然语言问题中构建查询图谱，提出了一种基于语义结构的 BERT 模型进行分析和预测，通过过滤噪声查询图，提高了问题解答的准确性。实验结果表明该方法比现有技术有更好的效果。