A Conceptual Paper: Proposed Search of Intelligent Disease Information System Using Semantic Web
Purpose: The purpose of this study is to optimize the search for disease records using the semantic web model.
Background: The development of Information Technology is not only on hardware and software, but also in the development of technology based on Artificial Intelligent (AI). In the field of medicine, the AI is developing rapidly. Information and Communication Technology (ICT) 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 which 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.
Design/Methodology/Approach: Methods and analysis will use Ontology Web Language (OWL) for disease domain modeling 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 (RDF). The study will also create semantic web ontology models and optimize disease information search using genetic algorithms that allow automatic in meta-data matching.
Results/Findings: The results of this study is a repository of knowledge related to such a mapping that disease information search can be found.
Conclusion and Implications: A new algorithms or model will be proposed and it can optimize disease finding with information needed and semantic Web ontology.