An ontology-based web decision support system to find entertainment points of interest in an urban area

In recent years, decision support systems (DSSs) have successfully deployed ontologies in their architecture. The result of such a use is information systems that assist users and organizations in semi-structured decision-making activities. Visitors from throughout Iran travel to different cities and regions every year, and they need help making their choices. Some of these tourists are unable to visit the beautiful areas of the destination city due to a lack of awareness. In this study, we design an ontology-based spatial DSS to find entertainment and tourism centers in Arak, Iran. The objective is to provide users with recommendations appropriate for the location, time, age group, type of activity, and other factors. In this model, the demands and concerns of tourists have been managed by creating a domain Web Ontology Language (OWL) for entertainment centers as a knowledge base in the Protege environment. The developed web-based DSS operates on a client-server architecture using technologies such as Werkzeug and Flask. As a result, it makes it possible to ontology reasoning based on the HermiT engine to choose the right center and conduct a semantic search on classes related to the appropriate point of interest. The main distinction between the proposed methodology and the previous studies on spatial DSS is that criteria are object properties in an ontology. Therefore, decision support relies on real-time reasoning rather than transforming criteria into geospatial layers. The evaluation results confirmed efficient interaction with this system, purposeful information retrieval, and rapid decision-making process. The results also indicated that searching for a POI (point of interest) in the study area using the developed system is at least 30% more successful than a search engine or social media. Moreover, to overcome the cold start problem, the proposed technique might be utilized in conjunction with the POI recommender systems.