A Collection of Benchmark Data Sets for Knowledge Graph-based Similarity in the Biomedical Domain.

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 the biomedical domain, where semantic similarity can be applied to the prediction of protein-protein interactions, associations between diseases and genes, cellular localization of proteins, among others. In recent years, several knowledge graph-based semantic similarity measures have been developed, but building a gold standard data set to support their evaluation is non-trivial. 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 si

SUBMITTER: Cardoso C 

PROVIDER: S-EPMC7661097 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature