# Biomedical Knowledge Graph Embeddings with Negative Statements

arXiv: https://arxiv.org/abs/2308.03447 · HTML: https://mlsanity.com/paper/2308.03447/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 51 references that are in the map.
- First posted on arXiv 7 Aug 2023.

## Builds on (most cited first, 16 of 16)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [Embedding Entities and Relations for Learning and Inference in Knowledge Bases](https://mlsanity.com/paper/1412.6575/) (2014)
- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [Counter-fitting Word Vectors to Linguistic Constraints](https://mlsanity.com/paper/1603.00892/) (2016)
- [OWL2Vec*: Embedding of OWL Ontologies](https://mlsanity.com/paper/2009.14654/) (2020)
- [EL Embeddings: Geometric construction of models for the Description Logic EL ++](https://mlsanity.com/paper/1902.10499/) (2019)
- [Neuro-symbolic representation learning on biological knowledge graphs](https://mlsanity.com/paper/1612.04256/) (2016)
- [OPA2Vec: combining formal and informal content of biomedical ontologies to improve similarity-based prediction](https://mlsanity.com/paper/1804.10922/) (2018)
- [Faithiful Embeddings for EL++ Knowledge Bases](https://mlsanity.com/paper/2201.09919/) (2022)
- [Negative Statements Considered Useful](https://mlsanity.com/paper/2001.04425/) (2020)
- [Putting RDF2vec in Order](https://mlsanity.com/paper/2108.05280/) (2021)
- [Predicting Gene-Disease Associations with Knowledge Graph Embeddings over Multiple Ontologies](https://mlsanity.com/paper/2105.04944/) (2021)
- [Benchmark datasets for biomedical knowledge graphs with negative statements](https://mlsanity.com/paper/2307.11719/) (2023)
- [Representation Learning with Ordered Relation Paths for Knowledge Graph Completion](https://mlsanity.com/paper/1909.11864/) (2019)

## Built on by (most cited first, 1 of 1)

- [A Systematic Evaluation of Knowledge Graph Embeddings for Gene-Disease Association Prediction](https://mlsanity.com/paper/2504.08445/) (2025)

## Abstract (arXiv)

A knowledge graph is a powerful representation of real-world entities and their relations. The vast majority of these relations are defined as positive statements, but the importance of negative statements is increasingly recognized, especially under an Open World Assumption. Explicitly considering negative statements has been shown to improve performance on tasks such as entity summarization and question answering or domain-specific tasks such as protein function prediction. However, no attention has been given to the exploration of negative statements by knowledge graph embedding approaches despite the potential of negative statements to produce more accurate representations of entities in a knowledge graph. We propose a novel approach, TrueWalks, to incorporate negative statements into the knowledge graph representation learning process. In particular, we present a novel walk-generation method that is able to not only differentiate between positive and negative statements but also take into account the semantic implications of negation in ontology-rich knowledge graphs. This is of particular importance for applications in the biomedical domain, where the inadequacy of embedding approaches regarding negative statements at the ontology level has been identified as a crucial limitation. We evaluate TrueWalks in ontology-rich biomedical knowledge graphs in two different predictive tasks based on KG embeddings: protein-protein interaction prediction and gene-disease association prediction. We conduct an extensive analysis over established benchmarks and demonstrate that our method is able to improve the performance of knowledge graph embeddings on all tasks.