Methodology for collecting data on innovation activity and its impact on the potential for economic growth based on building ontologies

Olga N. Korableva
1. ITMO University, St. Petersburg, Russian Federation
2. St. Petersburg State University
on.korableva@gmail.com
Olga V. Kalimullina
1. ITMO University, St. Petersburg, Russian Federation
chemireva@mail.ru
Viktoriya N. Mityakova
1. Reksoft
vnmityakova@gmail.com
The material was received by the Editorial Board: 08/07/2017
Absctract
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.

Keywords:
economy, ontology, methodology of data collection, potential for economic growth, innovative activity, semantic web



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., P. Naggar, A. Bonaccorsi, 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.
  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. 
  6. Wache, T. Vögele, U. Visser. Ontology-Based Integration of Information – A Survey of Existing Approaches. Workshop: Ontologies and Information, 2001, p. 108–117. 
  7. Efimenko I. V., Khoroshevsky V. F. Ontological modeling of the economy of enterprises and branches of modern Russia. Part 1. Ontological modeling: approaches, models, methods, tools, solutions. Gov. Reseach University Higher School of Economics. Moscow, Publ. house of the Higher School of Economics, 2011. (In Russ.) 
  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 in fulfillment of the requirements for the degree of Dr. rer. pol. Claudius Gräbner. 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. Kalimullina O. V. The possibilities of applying hybrid models on the basis of a balanced system of indicators within the framework of the risk management system of a commercial bank. Economy and Management of Control Systems, 2014, № 3 (13), p. 30–39. (In Russ.) 
  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. 

References: 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. Vol. 18, 1. P. 83–95. DOI: 10.25205/2542-0429-2018-18-1-83-95