Question answering over knowledge graph (KGQA), which automatically answers natural language questions by querying the facts in knowledge graph (KG), has drawn significant attention in recent years. In this paper, we focus on single-relation questions, which can be answered through a single fact in KG. This task is a non-trivial problem since capturing the meaning of questions and selecting the golden fact from billions of facts in KG are both challengeable. We propose a pipeline framework for KGQA, which consists of three cascaded components: (1) an entity detection model, which can label the entity mention in the question; (2) a novel entity linking model, which considers the contextual information of candidate entities in KG and builds a question pattern classifier according to the correlations between question patterns and relation types to mitigate entity ambiguity problem; and (3) a simple yet effective relation detection model, which is used to match the semantic similarity between the question and relation candidates. Substantial experiments on the SimpleQuestions benchmark dataset show that our proposed method could achieve better performance than many existing state-of-the-art methods on accuracy, top-N recall and other evaluation metrics.

知识图谱问答（KGQA）通过查询知识图谱（KG）中的事实来自动回答自然语言问题，近年来引起了广泛关注。在本文中，我们专注于单关系问题，可以通过 KG 中的单个事实来回答。这个任务是一个不平凡的问题，因为捕捉问题的含义和从 KG 的数十亿个事实中选择黄金事实都是具有挑战性的。我们为 KGQA 提出了一个流水线框架，它由三个级联组件组成：（1）一个实体检测模型，它可以标记问题中的实体；(2) 一种新颖的实体链接模型，考虑KG中候选实体的上下文信息，并根据问题模式和关系类型之间的相关性构建问题模式分类器，以减轻实体歧义问题；(3) 一个简单而有效的关系检测模型，用于匹配问题和关系候选之间的语义相似度。大量实验SimpleQuestions基准数据集表明，我们提出的方法在准确性、top- N召回率和其他评估指标方面可以比许多现有的最先进方法获得更好的性能。