论文标题
CSKG：常识知识图
CSKG: The CommonSense Knowledge Graph
论文作者
论文摘要
常识性知识的来源支持自然语言理解，计算机视觉和知识图中的应用。鉴于他们的互补性，他们的整合是必需的。然而，他们不同的焦点，建模方法和稀疏的重叠使集成变得困难。在本文中，我们通过以下五个原则来巩固常识知识，我们将七个关键来源合并为第一个集成的常识知识图（CSKG）。我们分析了CSKG及其各种文本和图形嵌入，表明CSKG连接良好，并且其嵌入式为图提供了有用的切入点。我们展示了CSKG如何为可推广的下游推理和语言模型的预培训提供证据。 CSKG及其所有嵌入均可公开使用，以支持有关常识性知识整合和推理的进一步研究。
Sources of commonsense knowledge support applications in natural language understanding, computer vision, and knowledge graphs. Given their complementarity, their integration is desired. Yet, their different foci, modeling approaches, and sparse overlap make integration difficult. In this paper, we consolidate commonsense knowledge by following five principles, which we apply to combine seven key sources into a first integrated CommonSense Knowledge Graph (CSKG). We analyze CSKG and its various text and graph embeddings, showing that CSKG is well-connected and that its embeddings provide a useful entry point to the graph. We demonstrate how CSKG can provide evidence for generalizable downstream reasoning and for pre-training of language models. CSKG and all its embeddings are made publicly available to support further research on commonsense knowledge integration and reasoning.
