Development of an ontology for the inclusion of app users with visual impairments
MetadataShow full item record
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
OntologyDisabilityICFAccessibilityUser profileInclusionAppsMobile devices
M. I. Torres-Carazo, M. J. Rodríguez-Fórtiz and M. V. H. Torres, "Development of an Ontology for the Inclusion of App Users With Visual Impairments," in IEEE Access, vol. 9, pp. 44339-44353, 2021, doi: 10.1109/ACCESS.2021.3065274.
SponsorshipSpanish Ministry of Economy and Competitiveness (Agencia Estatal de Investigacion) PID2019-109644RB-I00/AEI/10.13039/501100011033
Approximately 15% of the world’s population have some form of disability and the majority use apps on their mobile devices to help them in their daily lives with communication, healthcare, or for entertainment purposes. It is not, however, easy for users with impairments to choose the most suitable apps since this will depend on their particular personal characteristics or circumstances in a specific context, and because such users require apps with certain accessibility features which are not always specified in the app description. In order to overcome such difficulties, it is necessary to obtain a user profile that gathers the user’s personal details, abilities, disabilities, skills, and interests to facilitate selection. The basis for our research work is to develop an app that recommends a set of apps to users with disabilities. In this respect, the focus of this paper is to obtain a semantic user profile model on which more precise search requests can be performed. The disability we have chosen to concentrate on is that of visual impairment. We propose an ontology-based user profile that matches users’ characteristics, disabilities, and interests, and which not only simplifies the classification process but also provides a mechanism for linking them with existing disability ontologies, assistive devices, accessibility concepts, etc. Moreover, thanks to the inclusion of semantic relations and rules, it is possible to reason and infer new information that can be used to make more personalized recommendations than a simple app store search.