Neuro-symbolic representation learning on biological knowledge graphs.

ABSTRACT:

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

SUBMITTER: Alshahrani M 

PROVIDER: S-EPMC5860058 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature