Interpreting Knowledge Graph Relation Representation from Word Embeddings

Understand

Many models learn representations of knowledge graph data by exploiting its low-rank latent structure, encoding known relations between entities and enabling unknown facts to be inferred.

• To predict whether a relation holds between entities, embeddings are typically compared in the latent space following a relation-specific mapping.
• Whilst their predictive performance has steadily improved, how such models capture the underlying latent structure of semantic information remains unexplained.
• Building on recent theoretical understanding of word embeddings, we categorise knowledge graph relations into three types and for each derive explicit requirements of their representations.