摘要：This article presents and evaluates a method for the detection of DBpedia types and entities that can be used for knowledge base completion and maintenance. This method compares entity embeddings with traditional N-gram models coupled with clustering and classification. We tackle two challenges: (a) the detection of entity types, which can be used to detect invalid DBpedia types and assign DBpedia types for type-less entities; and (b) the detection of invalid entities in the resource description of a DBpedia entity. Our results show that entity embeddings outperform n-gram models for type and entity detection and can contribute to the improvement of DBpedia’s quality, maintenance, and evolution.
关键词：semantic web; DBpedia; entity embedding; n-grams; type identification; entity identification; data mining; machine learning semantic web ; DBpedia ; entity embedding ; n-grams ; type identification ; entity identification ; data mining ; machine learning
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