Abstract
An embedding is a function that maps entities from
one algebraic structure into another while preserving certain characteristics. Embeddings are being used successfully for mapping relational data
or text into vector spaces where they can be used
for machine learning, similarity search, or similar tasks. We address the problem of finding vector space embeddings for theories in the Description Logic EL++that are also models of the TBox.
To find such embeddings, we define an optimization problem that characterizes the model-theoretic
semantics of the operators in EL++within Rn,
thereby solving the problem of finding an interpretation function for an EL++theory given a particular domain ?. Our approach is mainly relevant
to large EL++theories and knowledge bases such
as the ontologies and knowledge graphs used in the
life sciences. We demonstrate that our method can
be used for improved prediction of protein–protein
interactions when compared to semantic similarity
measures or knowledge graph embeddings