# Benchmark datasets for biomedical knowledge graphs with negative statements

arXiv: https://arxiv.org/abs/2307.11719 · HTML: https://mlsanity.com/paper/2307.11719/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 19 references that are in the map.
- First posted on arXiv 21 Jul 2023.

## Builds on (most cited first, 3 of 3)

- [OWL2Vec*: Embedding of OWL Ontologies](https://mlsanity.com/paper/2009.14654/) (2020)
- [Negative Statements Considered Useful](https://mlsanity.com/paper/2001.04425/) (2020)
- [Predicting Gene-Disease Associations with Knowledge Graph Embeddings over Multiple Ontologies](https://mlsanity.com/paper/2105.04944/) (2021)

## Built on by (most cited first, 4 of 4)

- [Construction and Application of Materials Knowledge Graph in Multidisciplinary Materials Science via Large Language Model](https://mlsanity.com/paper/2404.03080/) (2024)
- [Knowledge Graphs for the Life Sciences: Recent Developments, Challenges and Opportunities](https://mlsanity.com/paper/2309.17255/) (2023)
- [Biomedical Knowledge Graph Embeddings with Negative Statements](https://mlsanity.com/paper/2308.03447/) (2023)
- [A Systematic Evaluation of Knowledge Graph Embeddings for Gene-Disease Association Prediction](https://mlsanity.com/paper/2504.08445/) (2025)

## Abstract (arXiv)

Knowledge graphs represent facts about real-world entities. Most of these facts are defined as positive statements. The negative statements are scarce but highly relevant under the open-world assumption. Furthermore, they have been demonstrated to improve the performance of several applications, namely in the biomedical domain. However, no benchmark dataset supports the evaluation of the methods that consider these negative statements. We present a collection of datasets for three relation prediction tasks - protein-protein interaction prediction, gene-disease association prediction and disease prediction - that aim at circumventing the difficulties in building benchmarks for knowledge graphs with negative statements. These datasets include data from two successful biomedical ontologies, Gene Ontology and Human Phenotype Ontology, enriched with negative statements. We also generate knowledge graph embeddings for each dataset with two popular path-based methods and evaluate the performance in each task. The results show that the negative statements can improve the performance of knowledge graph embeddings.