  Knowledge graphs represent real-world entities and their relations in a
semantically-rich structure supported by ontologies. Exploring this data with
machine learning methods often relies on knowledge graph embeddings, which
produce latent representations of entities that preserve structural and local
graph neighbourhood properties, but sacrifice explainability. However, in tasks
such as link or relation prediction, understanding which specific features
better explain a relation is crucial to support complex or critical
applications.
  We propose SEEK, a novel approach for explainable representations to support
relation prediction in knowledge graphs. It is based on identifying relevant
shared semantic aspects (i.e., subgraphs) between entities and learning
representations for each subgraph, producing a multi-faceted and explainable
representation.
  We evaluate SEEK on two real-world highly complex relation prediction tasks:
protein-protein interaction prediction and gene-disease association prediction.
Our extensive analysis using established benchmarks demonstrates that SEEK
achieves significantly better performance than standard learning representation
methods while identifying both sufficient and necessary explanations based on
shared semantic aspects.
