Introduction to Information Retrieval
Introduction to Information Retrieval
Ontology Learning and Reasoning -- Dealing with Uncertainty and Inconsistency
Uncertainty Reasoning for the Semantic Web I
OTM '09 Proceedings of the Confederated International Conferences, CoopIS, DOA, IS, and ODBASE 2009 on On the Move to Meaningful Internet Systems: Part II
DL-Learner: Learning Concepts in Description Logics
The Journal of Machine Learning Research
ORE - a tool for repairing and enriching knowledge bases
ISWC'10 Proceedings of the 9th international semantic web conference on The semantic web - Volume Part II
ESWC'11 Proceedings of the 8th extended semantic web conference on The semantic web: research and applications - Volume Part I
Inductive learning of disjointness axioms
OTM'11 Proceedings of the 2011th Confederated international conference on On the move to meaningful internet systems - Volume Part II
A framework for handling inconsistency in changing ontologies
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
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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.