  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
systems that are mainly geared for positive questions; they can also 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 statisticalinference 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.
