Proposed Search of Intelligent Information System for Disease Diagnosis Using Semantic Web

  • S.R.Candra Nursari
  • Setyawan Widyarto
  • Haslinda Sutan Ahmad Nawi
Keywords: Disease Information, Genetic Algorithms,, Ontology Web Language


The development of Information Technology is not only on hardware and software but also in the development of technology in the field of medicine, based on AI. In the field of medicine, AI is developing rapidly. Information and Communication Technology in the health field is widely used, one of which is disease information data. Disease information data availability has increased electronically but the data is not categorized and stored semantically. No categorization and semantic store make the data difficult to find. Semantic Web is an intelligent service as an information intermediary, search agent, and information filter, which offers more functionality and interoperability than a standalone service. The Semantic Web context is meta-data that allows the machine to interpret it where the query execution time depends on this order. A good algorithm for determining query paths can thus contribute to making queries fast and efficient. Methods and analysis will use Ontology Web Language for disease domain modelling and meta-search systems for ontology mapping and Web services. The Mapping component includes domain ontologies, taxonomic information and collection databases and changes them in the Resource Description Framework. The study proposed a research direction to create semantic web ontology models and optimize disease information search using genetic algorithms that allow automatic meta-data matching. The ultimate purpose is a repository of knowledge related to such a mapping that disease information search can be found. A new algorithm or model will be proposed and it can optimize disease finding with the information needed and semantic Web ontology.


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How to Cite
Nursari, S., Widyarto, S., & Sutan Ahmad Nawi, H. (2022). Proposed Search of Intelligent Information System for Disease Diagnosis Using Semantic Web. Selangor Science & Technology Review (SeSTeR), 5(5), 25-29. Retrieved from

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