HyperKG: Hyperbolic Knowledge Graph Embeddings for Knowledge Base Completion

Understand

Learning embeddings of entities and relations existing in knowledge bases allows the discovery of hidden patterns in data.

• In this work, we examine the geometrical space's contribution to the task of knowledge base completion.
• We focus on the family of translational models, whose performance has been lagging, and propose a model, dubbed HyperKG, which exploits the hyperbolic space in order to better reflect the topological properties of knowledge bases.
• We investigate the type of regularities that our model can capture and we show that it is a prominent candidate for effectively representing a subset of Datalog rules.