A Comparison of Word Embeddings and N-gram Models for DBpedia Type and Invalid Entity Detection

• Main Authors: Hanqing Zhou, Amal Zouaq, Diana Inkpen
• Format: Article
• Language: English
• Published: MDPI AG 2018-12-01
• Series: Information
• Subjects: semantic web; DBpedia; entity embedding; n-grams; type identification; entity identification; data mining; machine learning
• Online Access: http://www.mdpi.com/2078-2489/10/1/6

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.