Detecting hidden errors in an ontology using contextual knowledge. (1st April 2018)

• Record Type: Journal Article
• Authors: Teymourlouie, Mehdi; Zaeri, Ahmad; Nematbakhsh, Mohammadali; Thimm, Matthias; Staab, Steffen
• Is Part Of: Expert systems with applications. Volume 95(2018)
• Page Start: 312
• Page End: 323
• Publication Date: 2018-04-01
• Subjects: Ontology debugging -- Hidden modeling errors -- Contextual knowledge -- Incoherency -- Inconsistency; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2017.11.034
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Using background knowledge in ontology debugging is proposed for the first time. The evaluations show a remarkable increase in the number of discovered errors. Error detection should not be limited only to the contradictions inside the ontology. Logically coherent ontologies became incoherent beside some background knowledge. Abstract: Due to modeling errors in designing ontologies, an ontology may carry incorrect information. Ontology debugging can be helpful in detecting errors in ontologies that are increasing in size and expressiveness day by day. While current ontology debugging methods can detect logical errors (incoherences and inconsistencies), they are incapable of detecting hidden modeling errors in coherent and consistent ontologies. From the logical perspective, there are no errors in such ontologies, but this study shows some modeling errors may not break the coherency of the ontology by not participating in any contradiction. In this paper, contextual knowledge is exploited to detect such hidden errors. Our experiments show that adding general ontologies like DBpedia as contextual knowledge in the ontology debugging process results in detecting contradictions in ontologies that are coherent.