Benchmark datasets for biomedical knowledge graphs with negative statements

TL;DR

This paper introduces benchmark datasets for biomedical knowledge graphs that include negative statements, enabling better evaluation of methods that leverage such statements to improve predictive performance.

Contribution

It provides the first datasets with negative statements for biomedical knowledge graph tasks, facilitating research and evaluation in this area.

Findings

Negative statements improve knowledge graph embedding performance

Datasets cover protein-protein, gene-disease, and disease prediction tasks

Enriched with data from Gene Ontology and Human Phenotype Ontology

Abstract

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.