STransE: a novel embedding model of entities and relationships in knowledge bases

NAACL 2016 Dat Quoc NguyenKairit SirtsLizhen QuMark Johnson

Knowledge bases of real-world facts about entities and their relationships are useful resources for a variety of natural language processing tasks. However, because knowledge bases are typically incomplete, it is useful to be able to perform link prediction or knowledge base completion, i.e., predict whether a relationship not in the knowledge base is likely to be true... (read more)

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