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Knowledge graphs (KGs) of real-world facts about entities and theirrelationships are useful resources for a variety of natural language processingtasks. However, because knowledge graphs are typically incomplete, it is usefulto perform knowledge graph completion or link prediction, i.e. predict whethera relationship not in the knowledge graph is likely to be true. This paperserves as a comprehensive survey of embedding models of entities andrelationships for knowledge graph completion, summarizing up-to-dateexperimental results on standard benchmark datasets and pointing out potentialfuture research directions.

A survey of embedding models of entities and relationships for knowledge graph completion