Genetic Algorithm Based Random Selection-Rule Creation for Ontology Building

Recent Advances in Soft Computing : Proceedings of the 22nd International Conference on Soft Computing (MENDEL 2016). Advances in Intelligent Systems and Computing. Vol.576 2017
Henrihs Gorskis, Arkādijs Borisovs, Ludmila Aleksejeva

This paper investigates the possibility of creating ontology concepts from infor-mation contained in a database, by finding random queries with the help of a ge-netic algorithm. Based on the structure of the database random chromosomes are created. Their genes describe possible selection criteria. By using a genetic algo-rithm, these selections are improved. Due to the size of the database, an approach for finding fitness from general characteristics, instead of an in-depth analysis of the data is considered. After the algorithm improving the chromosomes in the population, bet best chromosomes are considered for implementation as ontology concepts for a description of the information contained in the database, in order to help describe data using ontology concepts.

Keywords
Ontology, databases, genetic algorithm, data mining
DOI
10.1007/978-3-319-58088-3_4
Hyperlink
https://link.springer.com/book/10.1007%2F978-3-319-58088-3

Gorskis, H., Borisovs, A., Aleksejeva, L. Genetic Algorithm Based Random Selection-Rule Creation for Ontology Building. In: Recent Advances in Soft Computing : Proceedings of the 22nd International Conference on Soft Computing (MENDEL 2016). Advances in Intelligent Systems and Computing. Vol.576, Czech Republic, Brno, 8-10 June, 2016. Cham: Springer International Publishing AG, 2017, pp.34-45. ISBN 978-3-319-58087-6. e-ISBN 978-3-319-58088-3. ISSN 2194-5357. e-ISSN 2194-5365. Available from: doi:10.1007/978-3-319-58088-3_4

Publication language
English (en)