Semantic Approach for Auto-Driven Medical Database Construction

Ontologies have become a standard for knowledge representation, supporting the organization and acquisition of knowledge across different domains. However, there are similarities between ontologies and databases in terms of organizing and structuring information, managing relationships between data elements, and supporting querying capabilities. In this paper, our objective is to leverage the information stored in ontologies to automatically construct a database. We propose a novel approach that automates and optimizes the storage of data in a database based on the ontology's structure. We define a set of rules to analyze the ontology, identify concepts, attributes, and relationships, and generate the corresponding database schema. This automation streamlines the database creation process, ensures consistency between the ontology and the database, and enables efficient storage, retrieval, and management of data based on the semantic structure provided by the ontology. To evaluate our approach, we develop a framework that tests the rules using various medical ontologies and generates corresponding databases. We conduct a series of tests to analyze the resulting database and compare it with existing approaches in the literature. Our framework demonstrates effectiveness in terms of generating time and database size.