Knowledge bases (KBs) , pragmatic collections of knowledge about notable entities , are an important asset in applications such as search, question answering and dialogue. Rooted in a long tradition in knowledge representation, all popular KBs only store positive information, while they abstain from taking any stance towards statements not contained in them. In this paper , we make the case for explicitly stating interesting statements which are not true . Negative statements would be important to overcome current limitations of question answering, yet due to their potential abundance , any effort towards compiling them needs a tight coupling with ranking . We introduce two approaches towards compiling negative statements. (i) In peer-based statistical inferences,we compare entities with highly related entities in order to derive potential negative statements, which we then rank using supervised and unsupervised features. (ii) In query-log-based text extraction, we use a pattern-based approach for harvesting search engine query logs. Experimental resultsshow that both approaches hold promising and complementary potential. Along with this paper, we publish the first datasets on interesting negative information, containing over 1.1M statementsfor 100K popular Wikidata entities .
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 .
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interesting statements which are not true
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yet due to their potential abundance
and can often contribute to informative summaries of entities. Due to the abundance of such invalid statements
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towards compiling them needs a tight coupling with ranking . We introduce two approaches towards compiling negative statements. (i) In peer-based statistical inferences,we compare entities with highly
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
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order to derive potential negative statements, which we then rank using supervised and unsupervised features. (ii) In query-log-based text extraction, we use a pattern-based approach for harvesting search engine query logs. Experimental resultsshow that both approaches hold promising and complementary potential. Along with this paper, we publish the first datasets on interesting negative information, containing over 1.1M statementsfor 100K popular Wikidata entities
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