论文标题
基于语义结构的查询图预测问题回答知识图
Semantic Structure based Query Graph Prediction for Question Answering over Knowledge Graph
论文作者
论文摘要
从自然语言问题中构建查询图是在知识图上回答复杂问题（复杂KGQA）的重要一步。通常，如果正确构建其查询图，可以正确回答一个问题，然后通过针对kg发出查询图来检索正确的答案。因此，本文着重于自然语言问题的查询图生成。查询图生成的现有方法忽略了一个问题的语义结构，从而导致大量嘈杂的查询图候选者破坏了预测精度。在本文中，我们从kgqa中的常见问题定义了六个语义结构，并开发了一种新颖的结构，以预测问题的语义结构。通过这样做，我们可以首先过滤嘈杂的候选查询图，然后使用基于BERT的排名模型对剩余的候选人进行排名。与最先进的工厂相比，对两个流行的基准元基准和WebQuestionsSP（WSP）进行了广泛的实验，证明了我们方法的有效性。
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.
