Serving DBpedia with DOLCE – More than Just Adding a Cherry on Top

  • Heiko PaulheimEmail author
  • Aldo Gangemi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9366)


Large knowledge bases, such as DBpedia, are most often created heuristically due to scalability issues. In the building process, both random as well as systematic errors may occur. In this paper, we focus on finding systematic errors, or anti-patterns, in DBpedia. We show that by aligning the DBpedia ontology to the foundational ontology DOLCE-Zero, and by combining reasoning and clustering of the reasoning results, errors affecting millions of statements can be identified at a minimal workload for the knowledge base designer.


Data quality Formal ontologies Foundational ontologies Anti-pattern DBpedia DOLCE 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Research Group Data and Web ScienceUniversity of MannheimMannheimGermany
  2. 2.Université Paris 13 - Sorbonne Paris Cité - CNRSParisFrance
  3. 3.STLabISTC-CNRRomeItaly

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