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. © Springer International Publishing Switzerland 2015.
Paulheim H, G.A. (2015). Serving DBpedia with DOLCE – More than Just Adding a Cherry on Top. BERLIN : Springer [10.1007/978-3-319-25007-6_11].
Serving DBpedia with DOLCE – More than Just Adding a Cherry on Top
GANGEMI, ALDO
2015
Abstract
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. © Springer International Publishing Switzerland 2015.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
