METHODOLOGY FOR COLLECTING DATA ON INNOVATION ACTIVITY AND ITS IMPACT ON THE POTENTIAL FOR ECONOMIC GROWTH BASED ON BUILDING ONTOLOGIES
https://doi.org/10.25205/2542-0429-2018-18-1-83-95
Abstract
The economy, as a conceptually diverse area, is a complex system where large volumes of qualitative and quantitative information are often presented in poorly structured or unstructured form. The use of semantic web technologies allows to adjust to a single, flexible structure and to integrate not only quantitative but, what is especially important, qualitative indicators and create conditions for computer processing in the future. Within the framework of the research, a methodology and algorithms for searching for sources and collecting economic data and their reduction to the structure of the established ontology of innovation activity and economic potential have been developed. The relevance and scientific novelty of the study is due to the subject area, as well as the application of semantic web technologies to the collection of information from heterogeneous distributed sources, including weakly structured and unstructured ones.
About the Authors
O. N. Korableva
ITMO University; St. Petersburg State University; St. Petersburg State University
Russian Federation
Russian Federation
V. N. Mityakova
Reksoft
Russian Federation
Russian Federation
O. V. Kalimullina
ITMO University
Russian Federation
Russian Federation
References
1. Xin Liu, Chungjin Hu, Jianyi Huang, Feng Liu. OPSDS: a semantic data integration and service system based on domain ontology // Data Science in Cyberspace (DSC), IEEE International Conference, 2016.
2. Girardi D., Giretzlehner M., Arthofer K. Ontology-Guided Data Acquisition and Analysis. Data analytics // The First International Conference on Data Analytics, 2012
3. Daraio C., Lenzerini M., Leporelli Cl., Moed H. F., Naggar P., Bonaccorsi A., Bartolucci Al. Data integration for research and innovation policy: an Ontology-Based Data Management approach // Scientometrics. February 2016. Vol. 106. Iss. 2. P. 857-871. URL: https://link.springer. com/article/10.1007/s11192-015-1814-/.
4. Kurt Uwe Stoll. Doctoral Thesis. Using Existing Structured Data as a Learning Set for Web Information Extraction in E-Commerce. Universität der Bundeswehr. München, 2016.
5. Gaihua Fu. FCA based ontology development for data integration // Information Processing & Management. 2016. Vol. 52. Iss. 5. P. 765-782. URL: http://www.sciencedirect.com/science/ article/pii/S030645731630019X/.
6. Wache T. Vögele, Visser U. Ontology-Based Integration of Information - A Survey of Existing Approaches // Workshop: Ontologies and Information. 2001. P. 108-117.
7. Ефименко И. В., Хорошевский В. Ф. Онтологическое моделирование экономики предприятий и отраслей современной России. Часть 1. Онтологическое моделирование: подходы, модели, методы, средства, решения / Нац. исслед. ун-т «Высшая школа экономики». М.: ИД ВШЭ, 2011.
8. Gräbner C. A systemic framework for the computational analysis of complex economies. An evolutionary-institutional perspective on the ontology, epistemology, and methodology of complexity economics. A thesis submitted to the Doctoral Commission Dr. rer. pol. of the University of Bremen. Bremen, 2016.
9. Blums I., Weigand H. Towards a reference ontology of complex economic exchanges for Accounting Information Systems // Proc. Of 20th IEEE International Enterprise Distributed Object Computing Conference (EDOC 2016). Vienna, Austria, 2016. P. 1-10.
10. Калимуллина О. В. Возможности применения гибридных моделей на основе сбалансированной системы показателей в рамках системы риск-менеджмента коммерческого банка // Экономика и менеджмент систем управления. 2014. № 3 (13). C. 30-39.
11. Korableva O., Kalimullina O. Strategic Approach to the Optimization of Organization Based on the BSC SWOT Matrix // Proceedings of the International Conference on Knowledge Engineering and Applications. ICKEA, 2016. Singapore, 2016. P. 212-215.
12. Bleiholder J., Naumann F. Conflict Handling Strategies in an Integrated Information System // Proceeding of the International Workshop on information integration on the Web (IIWEB), 2006.
13. Korableva O. N., Kalimullina O. V., Kurbanova E. S. Building the monitoring systems for complex distributed systems: Problems & solutions. ICEIS 2017 // Proceedings of the 19th International Conference on Enterprise Information Systems. Portugal, Porto, 2017. P. 221-228.
14. Korableva O. N., Razumova I. A., Kalimullina O. V. Research of Innovation Cycles and the Peculiarities Associated with the Innovations Life Cycle Stages // Proceedings of 29th IBIMA Conference. Vienna, Austria, 2017. P. 1853-1862.
Review
For citations:
Korableva O.N., Mityakova V.N., Kalimullina O.V. METHODOLOGY FOR COLLECTING DATA ON INNOVATION ACTIVITY AND ITS IMPACT ON THE POTENTIAL FOR ECONOMIC GROWTH BASED ON BUILDING ONTOLOGIES. World of Economics and Management. 2018;18(1):83-95. (In Russ.) https://doi.org/10.25205/2542-0429-2018-18-1-83-95
Views:
97
Email this article 
