An overview of embedding models of entities and relationships for knowledge base completion

03/23/2017

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by   Dat Quoc Nguyen, et al.

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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 knowledge base completion, i.e., predict whether a relationship not in the knowledge base is likely to be true. This article presents an overview of embedding models of entities and relationships for knowledge base completion, with up-to-date experimental results on two standard evaluation tasks of link prediction (i.e. entity prediction) and triple classification.