Natural language question answering over knowledge graph has received widespread attention. However, the existing methods always aim to improve every phase of natural language question answering and neglect the defects; namely, not all query intentions can be identified and mapped to the correct SPARQL statement. In contrast, keyword search relies on the links among multiple keywords regardless of the exact logic relations in question. Therefore, we propose a framework (abbreviated as NLQSK for title of this paper) that introduces keyword search into natural language question answering to compensate for the defects mentioned above. First, we translate a natural language question into top-k SPARQL statements by using the existing methods. Second, we transform the valuable information that cannot be identified and mapped into keywords, and then, return the neighboring information in a knowledge graph by keyword index. Third, we combine the SPARQL block (i.e., the SPARQL statement and its result) and keyword search to produce the answer to the natural language question. Finally, the experiments on the benchmark dataset confirm that keyword search can compensate for the defects of natural language question answering and that NLQSK can answer more questions than the existing state-of-the-art question answering systems.

知识图上的自然语言问答已经受到广泛关注。然而，现有的方法总是旨在改善自然语言问答的每个阶段并忽略其缺陷。也就是说，并非所有查询意图都可以被识别并映射到正确的SPARQL语句。相反，关键字搜索依赖于多个关键字之间的链接，而与所讨论的确切逻辑关系无关。因此，我们提出了一个框架（本文标题简称为NLQSK），该框架将关键字搜索引入自然语言问答中，以弥补上述缺陷。首先，我们将自然语言问题翻译成top- k使用现有方法的SPARQL语句。其次，将无法识别的有价值信息转换为关键词，然后通过关键词索引在知识图中返回邻近信息。第三，我们结合使用SPARQL块（即SPARQL语句及其结果）和关键字搜索来产生自然语言问题的答案。最后，在基准数据集上进行的实验证实，关键字搜索可以弥补自然语言问答的缺陷，并且与现有的最新问答系统相比，NLQSK可以回答更多的问题。