Architecture and Implementation of Semantic Web-Based Tourism Recommender Systems
DOI:
https://doi.org/10.63313/CS.8019Keywords:
Semantic Web, Tourism Recommendation, Ontology Modeling, Reasoning Engine, System Architecture, Personalized ServicesAbstract
Traditional tourism recommender systems face limitations in processing heterogeneous data, capturing users' semantic preferences, and delivering explainable results. This study explores a semantic Web-based architecture for tourism recommendation systems, highlighting a layered model driven by ontologies. The proposed architecture comprises four key layers: RDF-based data integration, OWL-based ontology modeling, semantic reasoning, and service-oriented applications. Comparative analysis of reasoning engines (Jena, Pellet, Hermit) and real-world systems (e.g., SWTOUR) reveals critical challenges such as reasoning complexity, ontology construction costs, and difficulties in user semantic modeling. The study further outlines future directions incorporating microservice architecture, edge computing, and lightweight semantic services to enhance system scalability and intelligence.
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