CORE: A Knowledge Graph Entity Type Prediction Method via Complex Space Regression and Embedding

Research on knowledge graph (KG) construction, completion, inference, and applications has grown rapidly in recent years since it offers a powerful tool for modeling human knowledge in graph forms. Nodes in KGs denote entities and links represent relations between entities. The basic building blocks of KG are entity-relation triples in form of (subject, predicate, object) introduced by the Resource Description Framework (RDF). Learning representations for entities and relations in low dimensional vector spaces is one of the most active research topics in the field. Entity type offers a valuable piece of information to KG learning tasks. Better results in KG-related tasks have been achieved with the help of entity type. For example, TKRL [1] uses a hierarchical type encoder for KG completion by incorporating entity type information. AutoETER [2] adopts a similar approach but encodes the type information with projection matrices. Based on DistMult [3] and ComplEx [4] embedding, [5] propose an improved factorization model without explicit type supervision.