DBpedia ontology enrichment for inconsistency detection

In recent years the Web of Data experiences an extraordinary development: an increasing amount of Linked Data is available on the World Wide Web (WWW) and new use cases are emerging continually. However, the provided data is only valuable if it is accurate and without contradictions. One essential part of the Web of Data is DBpedia, which covers the structured data of Wikipedia. Due to its automatic extraction based on Wikipedia resources that have been created by various contributors, DBpedia data often is error-prone. In order to enable the detection of inconsistencies this work focuses on the enrichment of the DBpedia ontology by statistical methods. Taken the enriched ontology as a basis the process of the extraction of Wikipedia data is adapted, in a way that inconsistencies are detected during the extraction. The creation of suitable correction suggestions should encourage users to solve existing errors and thus create a knowledge base of higher quality.