Linked Data and Ontology Reference Model for Infectious Disease Reporting Systems

Abstract

Linked data and ontologies are already in wide use in many fields. Especially systems based on medical data can be valuably improved by enhancing their contents and meta-models semantically, using ontologies in their backbone. This semantic enhancement brings an add-on value to standard systems, enabling an overall better data management and allowing a more intelligent data processing. In our work we focus on such a standard system, which we enhance semantically, transferring its classic relational models together with its data into a semantic model. This information system processes and analyzes data related to infectious disease reports in Germany. Data from reports on infectious diseases not only contains specific parts of microbiological and medical information, but also a combination of various aspects of contextual knowledge, that is needed in order to take measures preventing a wider spread and reducing further transmissions. In this paper we describe our practical approach for transferring the relational data models into ontologies, establishing an improved data standard for the current system in use. Moreover, we propose a semantic reference model based on different contexts, covering the requirements of semantified data from infectious disease reports.