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.
CITATION STYLE
Paulheim, H., & Gangemi, A. (2015). Serving DBpedia with DOLCE - more than just adding a cherry on top. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9366, pp. 180–196). Springer Verlag. https://doi.org/10.1007/978-3-319-25007-6_11
Mendeley helps you to discover research relevant for your work.