Question Answering (QA) requires understanding of queries expressed in natural languages and identification of relevant information content to provide an answer. For closed-world QAs, information access is obtained by means of either context texts, or a Knowledge Base (KB), or both. KBs are human-generated schematic representations of world knowledge. The representational ability of neural networks to generalize world information makes it an important component of current QA research. In this paper, we study the neural networks and QA systems in the context of KBs. Specifically, we focus on surveying methods for KB embedding, how such embeddings are integrated into the neural networks, and the role such embeddings play in improving performance across different question-answering problems. Our study of multiple question answering methods finds that the neural networks are able to produce state-of-art results in different question answering domains, and inclusion of additional information via KB embeddings further improve the performance of such approaches. Further progress in QA can be improved by incorporating more powerful representations of KBs.

中文翻译：

---

利用神经网络中的知识库进行问题解答的概述

问答（QA）要求理解以自然语言表达的查询，并标识相关信息内容以提供答案。对于封闭世界的质量保证，信息访问是通过上下文文本或知识库（KB）或二者兼有的方式获得的。KB是人为产生的世界知识的示意图。神经网络对世界信息的概括能力使其成为当前质量保证研究的重要组成部分。在本文中，我们研究了知识库环境下的神经网络和质量保证系统。具体来说，我们关注于KB嵌入的调查方法，如何将这些嵌入集成到神经网络中以及此类嵌入在改善不同问答问题的性能方面所起的作用。我们对多种问答方法的研究发现，神经网络能够在不同的问答域中产生最新的结果，并且通过KB嵌入包含其他信息进一步提高了此类方法的性能。通过合并功能更强大的知识库表示，可以改善质量检查的进一步进展。