Joint Representation Learning of Text and Knowledge for Knowledge Graph Completion

Understand

Joint representation learning of text and knowledge within a unified semantic space enables us to perform knowledge graph completion more accurately.

• In this work, we propose a novel framework to embed words, entities and relations into the same continuous vector space.
• In this model, both entity and relation embeddings are learned by taking knowledge graph and plain text into consideration.
• In experiments, we evaluate the joint learning model on three tasks including entity prediction, relation prediction and relation classification from text.