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Knowledge graphs represent facts about real-world entities. Most of thesefacts are defined as positive statements. The negative statements are scarcebut highly relevant under the open-world assumption. Furthermore, they havebeen demonstrated to improve the performance of several applications, namely inthe biomedical domain. However, no benchmark dataset supports the evaluation ofthe 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 anddisease prediction - that aim at circumventing the difficulties in buildingbenchmarks for knowledge graphs with negative statements. These datasetsinclude data from two successful biomedical ontologies, Gene Ontology and HumanPhenotype Ontology, enriched with negative statements.  We also generate knowledge graph embeddings for each dataset with two popularpath-based methods and evaluate the performance in each task. The results showthat the negative statements can improve the performance of knowledge graphembeddings.

Benchmark datasets for biomedical knowledge graphs with negative statements