A Methodological Approach to Compare Ontologies: Proposal and Application for SLAM Ontologies

• Autores: Cardinale Y., Cornejo-Lupa M.A., Ticona-Herrera R., Barrios-Aranibar D.
• Formato: artículo
• Fecha de Publicación: 2020
• Institución: Consejo Nacional de Ciencia Tecnología e Innovación
• Repositorio: CONCYTEC-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.concytec.gob.pe:20.500.12390/2459
• Enlace del recurso: https://hdl.handle.net/20.500.12390/2459; https://doi.org/10.1145/3428757.3429091
• Nivel de acceso: acceso abierto
• Materia: SLAM; Autonomous and Mobile Robots; Ontologies Evaluation; Ontology; http://purl.org/pe-repo/ocde/ford#3.03.12

Representation of the knowledge related to any domain with flexible and well-defined models, such as ontologies, provides the base to develop efficient and interoperable solutions. Hence, a proliferation of ontologies in many domains is unleashed. It is necessary to define how to compare such ontologies to decide which one is the most suitable for specific needs of users/developers. Since the emerging developing of ontologies, several studies have proposed criteria to evaluate them. Nevertheless, there is still a lack of practical and reproducible guidelines to drive a comparative evaluation of ontologies as a systematic process. In this paper, we propose a methodological process to qualitatively and quantitatively compare ontologies at Lexical, Structural, and Domain Knowledge levels, considering Correctness and Quality perspectives. Since the evaluation methods of our proposal are based in a golden-standard, it can be customized to compare ontologies in any domain. To show the suitability of our proposal, we apply our methodological approach to conduct a comparative study of ontologies in the robotic domain, in particularly for the Simultaneous Localization and Mapping (SLAM) problem. With this study case, we demonstrate that with this methodological comparative process, we are able to identify the strengths and weaknesses of ontologies, as well as the gaps still needed to fill in the target domain (SLAM for our study case). © 2020 ACM.