论文标题
对知识基础回答问题的比较研究
A Comparative Study of Question Answering over Knowledge Bases
论文作者
论文摘要
关于知识库（KBQA）的问题回答已成为一种流行的方法，可以帮助用户从知识库中提取信息。尽管存在多个系统，但是很难选择一种适合特定应用程序的情况。在本文中，我们对八个基准数据集上的六个代表性KBQA系统提供了比较研究。在此，我们研究了各种问题类型，属性，语言和域，以提供有关现有系统在哪里挣扎的见解。最重要的是，我们提出了一种先进的映射算法，以帮助现有模型实现卓越的结果。此外，我们还开发了多种语言Covid-kgqa，它鼓励Covid-19-19的研究和多语言对未来AI的多样性。最后，我们讨论了关键发现及其含义以及绩效指南以及一些未来的改进。我们的源代码可在\ url {https://github.com/tamlhp/kbqa}上找到。
Question answering over knowledge bases (KBQA) has become a popular approach to help users extract information from knowledge bases. Although several systems exist, choosing one suitable for a particular application scenario is difficult. In this article, we provide a comparative study of six representative KBQA systems on eight benchmark datasets. In that, we study various question types, properties, languages, and domains to provide insights on where existing systems struggle. On top of that, we propose an advanced mapping algorithm to aid existing models in achieving superior results. Moreover, we also develop a multilingual corpus COVID-KGQA, which encourages COVID-19 research and multilingualism for the diversity of future AI. Finally, we discuss the key findings and their implications as well as performance guidelines and some future improvements. Our source code is available at \url{https://github.com/tamlhp/kbqa}.
