Generative Adversarial Zero-Shot Relational Learning for Knowledge Graphs

Understand

Large-scale knowledge graphs (KGs) are shown to become more important in current information systems.

• To expand the coverage of KGs, previous studies on knowledge graph completion need to collect adequate training instances for newly-added relations.
• In this paper, we consider a novel formulation, zero-shot learning, to free this cumbersome curation.
• For newly-added relations, we attempt to learn their semantic features from their text descriptions and hence recognize the facts of unseen relations with no examples being seen.