Abstract
This paper investigates the possibility of creating ontology concepts from information contained in a database, by finding random queries with the help of a genetic algorithm. This is done, with the aim to help ontology building. Based on the structure of the database random chromosomes are created. Their genes describe possible selection criteria. By using a genetic algorithm, 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 finished improving the chromosomes in the population, the best chromosomes are chosen. They are considered for implementation as ontology concepts. These ontology concepts can be used as descriptions of the information contained in the database. Because genetic algorithms are not usually used for ontology building, this paper investigates the feasibility of such an approach.
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Acknowledgements
The Faculty of Computer Science and Information Technology (Riga Technical University) funds this work by an assigned Doctoral studies grant (3412000-DOK.DITF).
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Gorskis, H., Borisov, A., Aleksejeva, L. (2017). Genetic Algorithm Based Random Selection-Rule Creation for Ontology Building. In: Matoušek, R. (eds) Recent Advances in Soft Computing. ICSC-MENDEL 2016. Advances in Intelligent Systems and Computing, vol 576. Springer, Cham. https://doi.org/10.1007/978-3-319-58088-3_4
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DOI: https://doi.org/10.1007/978-3-319-58088-3_4
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