The Development of a Water Resource Monitoring Ontology as a Research Tool for Sustainable Regional Development

Assel Ospan (), Madina Mansurova, Vladimir Barakhnin, Aliya Nugumanova and Roman Titkov
Additional contact information
Assel Ospan: Department of Artificial Intelligence and Big Data, Faculty of Information Technology, Al-Farabi Kazakh National University, Almaty 050040, Kazakhstan
Madina Mansurova: Department of Artificial Intelligence and Big Data, Faculty of Information Technology, Al-Farabi Kazakh National University, Almaty 050040, Kazakhstan
Vladimir Barakhnin: Federal Research Center for Information and Computational Technologies, 630090 Novosibirsk, Russia
Aliya Nugumanova: Department of Big Data and Blockchain Technologies, Astana IT University, Astana 010000, Kazakhstan
Roman Titkov: Department of Informatics Systems, Faculty of Information Technology, Novosibirsk State University, 630090 Novosibirsk, Russia

Data, 2023, vol. 8, issue 11, 1-28

Abstract: The development of knowledge graphs about water resources as a tool for studying the sustainable development of a region is currently an urgent task, because the growing deterioration of the state of water bodies affects the ecology, economy, and health of the population of the region. This study presents a new ontological approach to water resource monitoring in Kazakhstan, providing data integration from heterogeneous sources, semantic analysis, decision support, and querying and searching and presenting new knowledge in the field of water monitoring. The contribution of this work is the integration of table extraction and understanding, semantic web rule language, semantic sensor network, time ontology methods, and the inclusion of a module of socioeconomic indicators that reveal the impact of water quality on the quality of life of the population. Using machine learning methods, the study derived six ontological rules to establish new knowledge about water resource monitoring. The results of the queries demonstrate the effectiveness of the proposed method, demonstrating its potential to improve water monitoring practices, promote sustainable resource management, and support decision-making processes in Kazakhstan, and can also be integrated into the ontology of water resources at the scale of Central Asia.

Keywords: knowledge graph; ontology; semantic web; water resource monitoring; spatial data; RDF triples (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2306-5729/8/11/162/pdf (application/pdf)
https://www.mdpi.com/2306-5729/8/11/162/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jdataj:v:8:y:2023:i:11:p:162-:d:1268198

Access Statistics for this article

Data is currently edited by Ms. Cecilia Yang

More articles in Data from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().