Building Subject Domain Ontology on the Base of a Logical Data Mod

Main Article Content

Alexander M. Gusenkov
Naille R. Bukharaev
Evgeny V. Biryaltsev

Abstract

The technology of automated construction of the subject domain ontology, based on information extracted from the comments of the TATNEFT oil company relational databases, is considered. The technology is based on building a converter (compiler) translating the logical data model of Epicenter Petrotechnical Open Software Corporation (POSC), presented in the form of ER diagrams and a set of the EXPRESS object-oriented language descriptions, into the OWL ontology description language, recommended by the W3C consortium. The basic syntactic and semantic aspects of the transformation are described.

Article Details

Author Biographies

Alexander M. Gusenkov

Assistant professor, Institute of Computational Mathematics and Information Technologies of Kazan Federal University. Ph.D. Current scientific interests: knowledge extraction technologies, Natural Language Processing, big data, data mining.

Naille R. Bukharaev

Assistant professor, Institute of Computational Mathematics and Information Technologies, Kazan Federal University. Ph.D. Current scientific interests: IT education methodology, knowledge extraction technologies, big data.

Evgeny V. Biryaltsev

Specialist in the field of specialized information systems, Ph. D., author of more than 50 publications, including 5 certificates of registration of programs, 3 inventions. Head of the Center of digital technologies of the Institute of applied research of the Academy of Sciences of the Republic of Tatarstan.

References

Гаврилова Т.А., Хорошевский Т.А. Базы знаний интеллектуальных систем. СПб.: Питер, 2001. 384 с.

Дейт К.Д. Введение в системы баз данных. М.: Изд. Дом «Вильямс», 2001. 72 с.

Epicentre v3.0. URL: http://www.energistics.org/energistics-standards-directory/epicentre-archive.

OWL Web Ontology Language. URL: https://www.w3.org/TR/2004/REC-owl-features-20040210/

Towards the Semantic Web: Ontology-Driven Knowledge Management. Chicester, UK: John Wiley & Sons, 2003. 312 p.

The World Wide Web Consortium (W3C). URL: http://www.w3c.org.

RDF 1.1 Concepts and Abstract Syntax. URL: https://www.w3.org/ TR/2014/REC-rdf11-concepts-20140225/

RDF Schema 1.1. URL: https://www.w3.org/TR/rdf-schema/

Extensible Markup Language (XML). URL: https://www.w3.org/XML/.

Льюис Ф., Розенкранц Д., Стирнз Р. Теоретические основы проектирования компиляторов. М.: Мир, 1979. 654 с.

Allmon B.J., Anderson J. Flex on Java. Manning Publications Co. Greenwich, CT, USA, ISBN: 1933988797, 2010. 264 p.

CUP Parser Generator for Java. URL: https://www.cs.princeton.edu/~appel/ modern/java/CUP/

Protégé. URL: http://protege.stanford.edu/.

Birialtsev E., Bukharaev N., Gusenkov A. Intelligent search in Big Data // Journal of Physics: Conference Series. V. 913, conference 1. Published online: 25 October 2017.

Гусенков А.М. Интеллектуальный поиск сложных объектов в массивах больших данных // Электронные библиотеки. 2016. Т. 19. № 1. С. 3–39.

Гусенков А., Биряльцев Е., Жибрик О. Интеллектуальный поиск в структурированных массивах информации. LAP LAMBERT Academic Publishing. Deutschland: OmniScriptum Marceting DEU GmbH, ISBN 978-3-659-76919-1, 2015. 129 c.

Гусенков А.М., Биряльцев Е.В. Интеграция реляционных баз данных на основе онтологий // Ученые записки Казанского государственного университета. Серия Физико-математические науки. 2007. Т. 149. Кн. 2. С. 13–34.

Gusenkov А., Bukharaev N., Birialtsev E. On ontology based data integration: problems and solutions // Journal of Physics: Conference Series. V. 1203, conf. 1. 012059. URL: https://iopscience.iop.org/article/10.1088/1742-6596/1203/1/012059 /meta

Gusenkov A., Bukharaev N. On Semantic Search Algorithm Optimization // New Knowledge in Information Systems and Technologies. WorldCIST'19. Advances in Intelligent Systems and Computing, V. 930. Springer, Cham, 2019. URL: https://link.springer.com/chapter/10.1007/978-3-030-16181-1_45.