<![CDATA[Conventional chatbots in fashion e-commerce rely on keyword matching and lack the semantic structure needed to correctly handle multi-entity product queries. This leads to inaccurate, context-blind responses that frustrate users and reduce platform engagement. This paper proposes and evaluates an ontology-driven chatbot system for fashion e-commerce and compares it directly against a non-ontology baseline built on the same dataset. Both systems were developed using Google Dialogflow for natural language processing, with product data collected through Python web scraping from the Alkaram fashion website. The ontology knowledge base was engineered in OWL 2 using Protégé and queried via SPARQL, with a class hierarchy covering product categories, attributes, and brand entities. To evaluate both models, five analysts conducted structured 10-minute conversations using a fixed set of 50 product queries, scoring each response across seven criteria, including accuracy, context retention, and grammatical quality, on a 0 to 9 scale. The ontology-based system achieved 88% accuracy compared to 67% for the non-ontology model, a 21% improvement. It also outperformed across all other criteria, with inter-analyst agreement within 0.5 points confirming evaluation reliability. These results show that integrating an OWL/SPARQL ontology into a Dialogflow-based chatbot meaningfully improves product query handling in fashion e-commerce, and the approach is practical enough to scale to other retail domains.]]>
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