The Development of a Water Resource Monitoring Ontology as a Research Tool for Sustainable Regional Development
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 pr...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Data |
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| Online Access: | https://www.mdpi.com/2306-5729/8/11/162 |
| _version_ | 1797459619794124800 |
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| author | Assel Ospan Madina Mansurova Vladimir Barakhnin Aliya Nugumanova Roman Titkov |
| author_facet | Assel Ospan Madina Mansurova Vladimir Barakhnin Aliya Nugumanova Roman Titkov |
| author_sort | Assel Ospan |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T16:54:54Z |
| format | Article |
| id | doaj.art-9665225636ec45fbbbbe327f3959a1d5 |
| institution | Directory Open Access Journal |
| issn | 2306-5729 |
| language | English |
| last_indexed | 2024-03-09T16:54:54Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Data |
| spelling | doaj.art-9665225636ec45fbbbbe327f3959a1d52023-11-24T14:37:17ZengMDPI AGData2306-57292023-10-0181116210.3390/data8110162The Development of a Water Resource Monitoring Ontology as a Research Tool for Sustainable Regional DevelopmentAssel Ospan0Madina Mansurova1Vladimir Barakhnin2Aliya Nugumanova3Roman Titkov4Department of Artificial Intelligence and Big Data, Faculty of Information Technology, Al-Farabi Kazakh National University, Almaty 050040, KazakhstanDepartment of Artificial Intelligence and Big Data, Faculty of Information Technology, Al-Farabi Kazakh National University, Almaty 050040, KazakhstanFederal Research Center for Information and Computational Technologies, 630090 Novosibirsk, RussiaDepartment of Big Data and Blockchain Technologies, Astana IT University, Astana 010000, KazakhstanDepartment of Informatics Systems, Faculty of Information Technology, Novosibirsk State University, 630090 Novosibirsk, RussiaThe 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.https://www.mdpi.com/2306-5729/8/11/162knowledge graphontologysemantic webwater resource monitoringspatial dataRDF triples |
| spellingShingle | Assel Ospan Madina Mansurova Vladimir Barakhnin Aliya Nugumanova Roman Titkov The Development of a Water Resource Monitoring Ontology as a Research Tool for Sustainable Regional Development Data knowledge graph ontology semantic web water resource monitoring spatial data RDF triples |
| title | The Development of a Water Resource Monitoring Ontology as a Research Tool for Sustainable Regional Development |
| title_full | The Development of a Water Resource Monitoring Ontology as a Research Tool for Sustainable Regional Development |
| title_fullStr | The Development of a Water Resource Monitoring Ontology as a Research Tool for Sustainable Regional Development |
| title_full_unstemmed | The Development of a Water Resource Monitoring Ontology as a Research Tool for Sustainable Regional Development |
| title_short | The Development of a Water Resource Monitoring Ontology as a Research Tool for Sustainable Regional Development |
| title_sort | development of a water resource monitoring ontology as a research tool for sustainable regional development |
| topic | knowledge graph ontology semantic web water resource monitoring spatial data RDF triples |
| url | https://www.mdpi.com/2306-5729/8/11/162 |
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