The Semantic Web represents a fundamental shift in how we structure and query information on the internet. By combining RDF/OWL ontologies with SPARQL queries, we can create intelligent systems that understand meaning rather than just processing keywords. In the EcoTourism Semantic Web Platform project, I built a system that recommends low-carbon tourism activities and accommodations by leveraging these semantic technologies combined with natural language processing.
The core architecture involves a knowledge graph built on RDF/OWL ontologies that capture domain-specific information about sustainable tourism destinations, transportation methods, and environmental impact metrics. A bilingual NLP pipeline powered by Gemini API converts user queries into optimized SPARQL requests, enabling the system to understand natural language questions and translate them into precise graph queries. The FastAPI backend integrates with Apache Fuseki, a powerful SPARQL server, to execute these queries and retrieve context-aware recommendations that prioritize sustainability.
What makes this approach powerful is its scalability and semantic reasoning capabilities. Unlike traditional databases that store data, semantic systems understand relationships and can make intelligent inferences. The system can recommend accommodations not just based on price, but on their carbon footprint, proximity to public transportation, and alignment with environmental standards. This project demonstrates how Semantic Web technologies bridge the gap between AI/ML systems and structured knowledge representation, enabling smarter, more contextual applications that serve real-world sustainability goals.