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

Semantic Structure based Query Graph Prediction for Question Answering over Knowledge Graph