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 by the predicted semantic structures, 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.
翻译:建立来自自然语言问题的查询图是回答知识图(Complex KGQA)的复杂问题的一个重要步骤。 一般来说,如果查询图的构建正确,然后通过发布针对KG的查询图来检索正确的答案,问题就可以得到正确的回答。 因此,本文侧重于从自然语言问题的查询图生成。 现有的查询图生成方法忽略了问题的语义结构,导致大量噪音查询图候选人破坏预测的准确性。 在本文中,我们从 KGQA 的共同问题中定义了六个语义结构,并开发了一个新的结构-BERT来预测问题的语义结构。 通过这样做,我们可以首先通过预测的语义结构过滤噪音的候选人查询图,然后将其余的候选人排在BERT的排名模型中。 在两个流行的基准MetQA 和 WebQSP (WSP) 上进行的广泛实验,展示了我们的方法相对于最新条款的有效性。