  Motivation: Biological data and knowledge bases increasingly rely on Semantic
Web technologies and the use of knowledge graphs for data integration,
retrieval and federated queries. In the past years, feature learning methods
that are applicable to graph-structured data are becoming available, but have
not yet widely been applied and evaluated on structured biological knowledge.
Results: We develop a novel method for feature learning on biological knowledge
graphs. Our method combines symbolic methods, in particular knowledge
representation using symbolic logic and automated reasoning, with neural
networks to generate embeddings of nodes that encode for related information
within knowledge graphs. Through the use of symbolic logic, these embeddings
contain both explicit and implicit information. We apply these embeddings to
the prediction of edges in the knowledge graph representing problems of
function prediction, finding candidate genes of diseases, protein-protein
interactions, or drug target relations, and demonstrate performance that
matches and sometimes outperforms traditional approaches based on manually
crafted features. Our method can be applied to any biological knowledge graph,
and will thereby open up the increasing amount of Semantic Web based knowledge
bases in biology to use in machine learning and data analytics. Availability
and Implementation:
https://github.com/bio-ontology-research-group/walking-rdf-and-owl Contact:
<EMAIL_ADDRESS>