<![CDATA[Ontology and Semantic Web technologies have become important approaches for improving the accessibility, integration, and semantic accuracy of medical information, particularly in supporting early awareness and information retrieval related to cervical cancer. This study proposes a hybrid ontology and Semantic Web model to enhance cervical cancer information retrieval by transforming heterogeneous web-based health information into structured and machine-interpretable knowledge. The research was conducted through several stages, including data collection using purposive sampling, preprocessing, data cleaning, labelling, ontology modelling, and Semantic Web implementation. A total of 645 data records were collected from 62 web sources and organized into eight main domain features: symptoms, affected organs, maintenance, treatment, characteristic features, causes, prevention, and types of cervical cancer. The proposed system adopts a layered Semantic Web architecture consisting of XML, RDF, OWL, and logic layers. The XML layer represents the data structure, the RDF layer defines semantic relationships, and the OWL-based ontology layer models domain knowledge and rules. In contrast, the logic layer enables reasoning and knowledge inference. In addition, heuristic-based mapping is applied to connect relational database schemas with ontology models to support semantic interoperability. The results show that the proposed model can represent cervical cancer knowledge more systematically and improve semantic search capabilities in healthcare information systems. Therefore, this study contributes to the development of intelligent, interoperable medical information retrieval systems to support cervical cancer education, prevention, and early detection.]]>
