# Negative Statements Considered Useful

arXiv: https://arxiv.org/abs/2001.04425 · HTML: https://mlsanity.com/paper/2001.04425/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jan 2020.

## Built on by (most cited first, 9 of 9)

- [Relational Message Passing for Fully Inductive Knowledge Graph Completion](https://mlsanity.com/paper/2210.03994/) (2022)
- [UnCommonSense: Informative Negative Knowledge about Everyday Concepts](https://mlsanity.com/paper/2208.09292/) (2022)
- [Completeness, Recall, and Negation in Open-World Knowledge Bases: A Survey](https://mlsanity.com/paper/2305.05403/) (2023)
- [Benchmark datasets for biomedical knowledge graphs with negative statements](https://mlsanity.com/paper/2307.11719/) (2023)
- [Predicting Document Coverage for Relation Extraction](https://mlsanity.com/paper/2111.13611/) (2021)
- [How Stable is Knowledge Base Knowledge?](https://mlsanity.com/paper/2211.00989/) (2022)
- [A Minimal Deductive System for RDFS with Negative Statements](https://mlsanity.com/paper/2202.13750/) (2022)
- [Biomedical Knowledge Graph Embeddings with Negative Statements](https://mlsanity.com/paper/2308.03447/) (2023)
- [Three Types of Negation of Triple and its Elements and an Extension of Triple](https://mlsanity.com/paper/2609.08271/) (2026)

## Abstract (arXiv)

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.