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.
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Here we speak in terms of the Robert Koch Institute’s system, however we furthermore hope to motivate also a general, canonical and re-usable linked data standard that may be successfully applied to any information system handling infectious disease data.
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From here on we will drop all prefix definitions and assume them to be found on https://prefix.cc/.
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We have used here rdfs:seeAlso to follow the weaker SKOS approach. We could have used a stronger relation instead, e.g. owl:sameAs.
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References
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Acknowledgments
This project is an ongoing work under the funding of the German Federal Ministry of Health. The work described above is part of DEMIS (Deutsches Elektronisches Melde- und Informationssystem für den Infektionsschutz, German Electronic Reporting and Information System for Infectious Disease Control) project work. We would like to thank all our project colleagues involved in it, especially our technical DEMIS project leader, Hermann Claus, for making our contribution possible.
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Streibel, O., Kybranz, F., Kirchner, G. (2017). Linked Data and Ontology Reference Model for Infectious Disease Reporting Systems. In: Panetto, H., et al. On the Move to Meaningful Internet Systems. OTM 2017 Conferences. OTM 2017. Lecture Notes in Computer Science(), vol 10574. Springer, Cham. https://doi.org/10.1007/978-3-319-69459-7_8
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DOI: https://doi.org/10.1007/978-3-319-69459-7_8
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