论文标题
合并常识性知识
Consolidating Commonsense Knowledge
论文作者
论文摘要
常识性推理是构建强大的AI系统的重要方面，并且在自然语言理解，计算机视觉和知识图中受到了极大的关注。目前，存在许多有价值的常识性知识来源，具有不同的焦点，优势和劣势。在本文中，我们列出了代表资源及其属性。基于此调查，我们提出了原理和代表模型，以将其整合到常识知识图（CSKG）中。我们将这种方法应用于将七个单独的源合并到第一个集成的CSKG中。我们介绍了CSKG的统计数据，对其在四个QA数据集上的实用程序进行了初步研究，并列出了学习的课程。
Commonsense reasoning is an important aspect of building robust AI systems and is receiving significant attention in the natural language understanding, computer vision, and knowledge graphs communities. At present, a number of valuable commonsense knowledge sources exist, with different foci, strengths, and weaknesses. In this paper, we list representative sources and their properties. Based on this survey, we propose principles and a representation model in order to consolidate them into a Common Sense Knowledge Graph (CSKG). We apply this approach to consolidate seven separate sources into a first integrated CSKG. We present statistics of CSKG, present initial investigations of its utility on four QA datasets, and list learned lessons.
