Pitfalls in Ontologies and TIPS to Prevent Them

• Published in: Knowledge Discovery, Knowledge Engineering and Knowledge Management Vol. 454; pp. 115 - 131
• Main Authors: Maria Keet, C., Suárez-Figueroa, Mari Carmen, Poveda-Villalón, María
• Format: Book Chapter
• Language: English
• Published: Germany Springer Berlin / Heidelberg 2015; Springer Berlin Heidelberg
• Series: Communications in Computer and Information Science
• Subjects: Artificial intelligence; Common Pitfalls; Data mining; Information retrieval; Object Property Expressions; Ontology Development; Ontology Elements (OE); Ontology Evaluation
• ISBN: 3662465485; 9783662465486
• ISSN: 1865-0929; 1865-0937
• DOI: 10.1007/978-3-662-46549-3_8

A growing number of ontologies are already available thanks to development initiatives in many different fields. In such ontology developments, developers must tackle a wide range of difficulties and handicaps, which can result in the appearance of anomalies in the resulting ontologies. Therefore, ontology evaluation plays a key role in ontology development. OOPS! is an on-line tool that automatically detects pitfalls, considered as potential errors or problems—and thus may help ontology developers to improve their ontologies. To gain insight in the existence of pitfalls and to assess whether there are differences among ontologies developed by novices, a random set of already scanned ontologies, and existing well-known ones, data of 406 OWL ontologies were analysed on OOPS!’s 21 pitfalls, of which 24 ontologies were also examined manually on the detected pitfalls. The various analyses performed show only minor differences between the three sets of ontologies, therewith providing a general landscape of pitfalls in ontologies. We also propose guidelines to avoid the inclusion of such common pitfalls in new ontologies, the Typical pItfalls Prevention Scheme (TIPS), so as to increase the baseline quality of OWL ontologies.