Knowledge bases (KBs) about notable entities and their properties are an important asset in applications such as search, question answering and dialogue. All popular KBs capture virtually only positive statements, and abstain from taking any stance on statements not stored in the KB. This paper makes the case for explicitly stating salient statements that do not hold. Negative statements are useful to overcome limitations of question answering, and can often contribute to informative summaries of entities. Due to the abundance of such invalid statements, any effort to compile them needs to address ranking by saliency. We present a statistical inference method for compiling and ranking negative statements,based on expectations from positive statements of related entities in peer groups. Experimental results, with a variety of datasets, show that the method can effectively discover notable negative statements, and extrinsic studies underline their usefulness for entity summarization. Datasets and code are released as resources for further research.

中文翻译：

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被认为有用的否定陈述

关于显着实体及其属性的知识库 (KB) 是搜索、问答和对话等应用程序中的重要资产。所有流行的 KB 实际上只捕获正面的陈述，并且不对未存储在 KB 中的陈述采取任何立场。本文为明确陈述不成立的突出陈述提供了理由。否定陈述有助于克服问答的局限性，并且通常有助于提供实体的信息摘要。由于此类无效语句大量存在，任何编译它们的工作都需要通过显着性来解决排名问题。我们提出了一种统计推断方法，用于根据对等群体中相关实体的正面陈述的期望，对负面陈述进行编译和排序。实验结果，各种数据集，表明该方法可以有效地发现显着的否定陈述，并且外在研究强调了它们对实体摘要的有用性。数据集和代码作为资源发布以供进一步研究。