Abstract

The ability to compare entities within a knowledge graph is a cornerstone technique for several applications, ranging from the integration of heterogeneous data to machine learning. It is of particular importance in biomedical applications such as prediction of protein-protein interactions, associations between diseases and genes, cellular localization of proteins, among others. However, building a gold standard data set to support their evaluation is non-trivial, due to size, diversity and complexity of biomedical knowledge graphs

We present a collection of 21 benchmark data sets that aim at circumventing the difficulties in building benchmarks for large biomedical knowledge graphs by exploiting proxies for biomedical entity similarity. These data sets include data from two successful biomedical ontologies, the Gene Ontology and the Human Phenotype Ontology, and explore proxy similarities based on protein and gene properties. Data sets have varying sizes and cover four different species at different levels of annotation completion. For each data set we also provide semantic similarity computations with state of the art representative measures. Available at: https://github.com/liseda-lab/kgsim-benchmark.