Version 1
: Received: 27 June 2023 / Approved: 28 June 2023 / Online: 29 June 2023 (13:16:38 CEST)
How to cite:
Ospan, A.; Mansurova, M.; Barakhnin, V.; Nugumanova, A.; Titkov, R. The Development of the Monitoring Water Resources Ontology as a Research Tool for Sustainable Regional Development. Preprints2023, 2023062065. https://doi.org/10.20944/preprints202306.2065.v1
Ospan, A.; Mansurova, M.; Barakhnin, V.; Nugumanova, A.; Titkov, R. The Development of the Monitoring Water Resources Ontology as a Research Tool for Sustainable Regional Development. Preprints 2023, 2023062065. https://doi.org/10.20944/preprints202306.2065.v1
Ospan, A.; Mansurova, M.; Barakhnin, V.; Nugumanova, A.; Titkov, R. The Development of the Monitoring Water Resources Ontology as a Research Tool for Sustainable Regional Development. Preprints2023, 2023062065. https://doi.org/10.20944/preprints202306.2065.v1
APA Style
Ospan, A., Mansurova, M., Barakhnin, V., Nugumanova, A., & Titkov, R. (2023). The Development of the Monitoring Water Resources Ontology as a Research Tool for Sustainable Regional Development. Preprints. https://doi.org/10.20944/preprints202306.2065.v1
Chicago/Turabian Style
Ospan, A., Aliya Nugumanova and Roman Titkov. 2023 "The Development of the Monitoring Water Resources Ontology as a Research Tool for Sustainable Regional Development" Preprints. https://doi.org/10.20944/preprints202306.2065.v1
Abstract
The development of knowledge graphs for water resources as a tool for studying sustainable regional development is a relevant task, as the increasing deterioration of water bodies affects the ecology, economy of the country, as well as the health of the population living near water bodies. Solving this problem requires the development of water resource monitoring methods based on semantic data analysis is required to detect and track sources of pollution and provide recommendations to support decision-making. This research proposes a method of semantic analysis for modeling knowledge graphs and constructing rules that enable the integration of data from heterogeneous sources, semantic compatibility, support for reasoning and inference, facilitate queries and search, and represent knowledge in the field of water resource monitoring. As a result, an ontology based on the SWRL language is created, which enables the expression of complex relationships and derivation of new knowledge, SSN for interaction between the ontology and sensor data, and Time Ontology, which models and analyzes information related to temporal aspects.After populating the ontology and establishing logical connections, SPARQL queries are implemented to retrieve new knowledge about water resources.
Keywords
knowledge graph; ontology; semantic web; water resources monitoring; open data; sustainable development; RDF triples; SPARQL
Subject
Engineering, Control and Systems Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
