Abstract
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.
Highlights
The Semantic Web is defined by Berners-Lee et al [1] as an extension of the current Web in which information is given a well-defined meaning, in order to allow computers and people to cooperate better than before
We addressed the tasks of building our own entity embedding and n-gram models for DBpedia quality enhancement by detecting invalid DBpedia types, completing missing DBpedia types, and detecting invalid DBpedia entities in resources description
We compared the results of different clustering and classification algorithms, and the results of different entity embedding and n-gram models
Summary
The Semantic Web is defined by Berners-Lee et al [1] as an extension of the current Web in which information is given a well-defined meaning, in order to allow computers and people to cooperate better than before. In this context, linked data is about creating typed links between data from different sources by using the current Web. In this context, linked data is about creating typed links between data from different sources by using the current Web It is a “Web of Data” in Resource Description Format (RDF) [2,3]. The survey proposed a framework to find the most suitable knowledge graph for given settings
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