Ontology-driven context-aware recommendation method for indoor navigation in large hospitals

Abstract

Navigating in complex and dynamic indoor spaces of large hospitals is challenging. Since improving efficiency is a common goal for hospitals, there is an urgent need for an accurate and personalized service recommendation method in hospital navigation. To address this challenge, we propose a context-aware recommendation method for personalized hospital navigation. Firstly, an ontology-based contextual framework is designed for hospital navigation using Protégé Web Ontology Language (OWL)-2. Then rule-based contextual reasoning and information recommendation using Semantic Web Rule Language (SWRL) are proposed to overcome the limitations of ontology reasoning. Finally, some case queries are conducted using RDF Query Language (SPARQL) to evaluate the usability of the contextual ontology and rules.