Semantic Web Healthcare Chatbot Using Ontology and Data Mining for Patient Information Assistance

This work uses ontology engineering and data mining to create and deploy a Semantic Web healthcare chatbot to enhance patient information support systems, which are becoming more crucial in digital healthcare. Healthcare organisations may use the Semantic Web to handle massive amounts of machinereadable and intelligible medical data. Semantically enhanced knowledge representations and automated analytic methods increase chatbot accuracy, relevance, and contextual comprehension, reducing rule-based systems' inconsistent outputs. This topic uses ontologies, linked data, and semantic web technologies to improve healthcare data interoperability and enable the accurate interchange of medical knowledge, records, clinical advice, and research. Over 64% of patients prefer automated conversational interfaces for preliminary medical guidance, and nearly 72% of healthcare organisations have adopted AI-enabled triage or support systems, indicating a growing societal reliance on automated health information platforms. Ontological analysis improves information comprehension. Hospital information management and patient self-service systems benefit from semantic chatbots' 89% retrieval accuracy and 43% reduction in irrelevant response production. An ontology is the foundation of any domain-specific knowledge representation system that removes data inconsistencies to assure validity.