TY - JOUR

T1 - The Development of a Water Resource Monitoring Ontology as a Research Tool for Sustainable Regional Development

AU - Ospan, Assel

AU - Mansurova, Madina

AU - Barakhnin, Vladimir

AU - Nugumanova, Aliya

AU - Titkov, Roman

N1 - This study was funded by the Ministry of Science and Higher Education of the Republic of Kazakhstan, grant number AP09261344 “Development of methods for automatic extraction of spatial objects from heterogeneous sources for information support of geographic information systems”.

PY - 2023/11

Y1 - 2023/11

N2 - 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.

AB - 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.

UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-85178131360&origin=inward&txGid=40750b56d95fbd3155417f610b69c33c

UR - https://www.mendeley.com/catalogue/644b90fa-d9d2-3b26-ba60-4f5cf75febae/

U2 - 10.3390/data8110162

DO - 10.3390/data8110162

M3 - Article

VL - 8

JO - Data

JF - Data

SN - 2306-5729

IS - 11

M1 - 162

ER -