ONTOLOGY-BASED SYSTEM DEVELOPMENT FOR MEDICAL DATABASE ACCESS
DOI:
https://doi.org/10.17770/etr2017vol2.2572Keywords:
Database analysis, intelligent system development, ontologyAbstract
Medical research is a complex multi-disciplinary task involving specialists from different fields and professions, not only medical professionals. Medical databases are structured by information technology experts, but the contents must be tailored to the medical field. When the medical staff defines the information they use, terminology from their particular field of expertise is employed. This leads to misunderstandings between the maintainers and developers of information technology solutions, and the users of those solutions. When the time comes that a user, who is a medical professional, requires very specific data from the database, the chance of obtaining the data incorrectly is very high. By defining specific concepts and relationships between the data, in an explicit shared specification, some of the above problems can be avoided. The developed ontology-based data access system, described in this paper, provides a tool to store, manage and use definitions of common terminology and their mappings to the database. It is also capable of reasoning about the relationships between terms and indicates inconsistencies of term definitions, if any are present. By defining these interconnected terms in the ontology and by working through the system, all experts and software tools, who use the data, are able to use and reuse these terms to obtain data in a reliable and predefined way. This paper discusses the development and implementation of the ontology-based data access system, the ontology describing the medical data and the data mapping system, linking data from the database to concepts and virtual ontology individuals.Downloads
References
O. Daramola, M. Adigun and C. Ayo, Building an Ontology-Based Framework for Tourism Recommendation Services: Information and Communication Technologies in Tourism 2009: Proceedings of the International Conference in Amsterdam, The Netherlands, 2009, 135-147, doi: 10.1007/978-3-211-93971-0_12
M. del Mar Roldán García, J. García-Nieto, J. F. Aldana-Montes, An ontology-based data integration approach for web analytics in e-commerce, Expert Systems with Applications, Volume 63, 30 November 2016, Pages 20-34, ISSN 0957-4174, http://dx.doi.org/10.1016/j.eswa.2016.06.034.
D. Calvanese, P. Liuzzo, A. Mosca, J. Remesal, M. Rezk, G. Rull, Ontology-based data integration in EPNet: Production and distribution of food during the Roman Empire, Engineering Applications of Artificial Intelligence, Volume 51, May 2016, Pages 212-229, ISSN 0952-1976, http://dx.doi.org/10.1016/j.engappai.2016.01.005.
R. Dieng-Kuntz, D. Minier, M. Růžička, F. Corby, O. Corby, L. Alamarguy, Building and using a medical ontology for knowledge management and cooperative work in a health care network, Computers in Biology and Medicine, Volume 36, Issues 7–8, July–August 2006, Pages 871-892, ISSN 0010-4825, http://dx.doi.org/10.1016/j.compbiomed.2005.04.015.
J. D. Cameron, A. Ramaprasad, T. Syn, An ontology of and roadmap for mHealth research, International Journal of Medical Informatics, Volume 100, April 2017, Pages 16-25, ISSN 1386-5056, http://dx.doi.org/10.1016/j.ijmedinf.2017.01.007.
A. Sigov, V. Baranyuk, V. Nechaev, O. Smirnova, A. Melikhov, Approach for Forming the Bionic Ontology, Procedia Computer Science, Volume 103, 2017, Pages 495-498, ISSN 1877-0509, http://dx.doi.org/10.1016/j.procs.2017.01.033.
A. Kumar, Y. L. Yip, B. Smith, P. Grenon, Bridging the gap between medical and bioinformatics: An ontological case study in colon carcinoma, Computers in Biology and Medicine, Volume 36, Issues 7–8, July–August 2006, Pages 694-711, ISSN 0010-4825, http://dx.doi.org/10.1016/j.compbiomed.2005.07.001.
D. Calvanese, B. Cogrel, S. Komla-Ebri, R. Kontchakov, D. Lanti, M. Rezk, M. Rodriguez-Muro, G. Xiao, Ontop: Answering SPARQL queries over relational databases (2017) Semantic Web, 8 (3), pp. 471-487.
H. Gorskis, A. Borisovs, Storing an OWL 2 Ontology in a Relational Database Structure. In: Environment. Technology. Resources : Proceedings of the 10th International Scientific and Practical Conference, Latvia, Rezekne, 18-20 June, 2015. Rezekne: Rezeknes Augstskola, 2015, pp.71-75. ISBN 978-9984-44-173-3. ISSN 1691-5402. e-ISSN 2256-070X. Available from: doi:10.17770/etr2015vol3.168
S. Chuprina, I. Postanogov, O. Nasraoui, Ontology Based Data Access Methods to Teach Students to Transform Traditional Information Systems and Simplify Decision Making Process, Procedia Computer Science, Volume 80, 2016, Pages 1801-1811, ISSN 1877-0509, http://dx.doi.org/10.1016/j.procs.2016.05.458.
H. Gorskis, L. Aleksejeva, I. Poļaka, Database Analysis for Ontology Learning. Procedia Computer Science, 2016, Vol.102, pp.113-120. ISSN 1877-0509. Available from: doi:10.1016/j.procs.2016.09.377
N. F. Noy and D. L. McGuinness, Ontology Development 101: A Guide to Creating Your First Ontology. Stanford Knowledge Systems Laboratory Technical Report KSL-01-05 and Stanford Medical Informatics Technical Report SMI-2001-0880, March 2001.
