Semantic Web Technologies for Supporting Fundamental Research In Geology

Main Article Content

Igor Vyacheslavovich Bychkov
Evgeny Alexandrovich Cherkashin
Jin Zhang
Victoria Alexeevna Popova
Oksana Anatolievna Mazaeva
Oksana Viktorovna Lunina

Abstract

The article presents an innovative methodology for applying Semantic Web technologies to support fundamental geological research. The problem of semantic integration of heterogeneous geological data, characterized by different scales and interdisciplinarity, is considered. A five-stage methodology is developed, including domain analysis, ontological conceptual modeling, data transformation into a knowledge graph, deployment of a distributed data access infrastructure based on the conceptual model, and integration with processing and analysis procedures. Practical testing was conducted on three case studies: analysis of geochemical data for assessing territory pollution levels, creation of an information system about faults, and research on reservoir shoreline dynamics. The proposed ontological approach ensures compliance with FAIR principles and overcoming the "semantic barrier" in geological research. It is shown that Semantic Web technologies enable a transition from fragmented information arrays to a holistic semantic space of geological knowledge, opening new opportunities for generating comprehensive scientific hypotheses and cross-disciplinary research.

Article Details

How to Cite
Bychkov, I. V., E. A. Cherkashin, J. Zhang, T. Y. Cherkashina, V. A. Popova, O. A. Mazaeva, and O. V. Lunina. “Semantic Web Technologies for Supporting Fundamental Research In Geology”. Russian Digital Libraries Journal, vol. 28, no. 4, Nov. 2025, pp. 740-8, doi:10.26907/1562-5419-2025-28-4-740-780.

References

1. Mattmann C. A vision for data science // Nature. 2013. Vol. 493. P. 473–475. https://doi.org/110.1038/493473a
2. Hitzler P., Janowicz K. Semantic Web for Earth and Environmental Science: Current state and future directions // Semantic Web. 2010. Vol. 1, No. 1–2. P. 85–98. https://doi.org/110.3233/SW-2010-0012
3. Hitzler P. A review of the semantic web field // Communications of the ACM. 2021. Vol. 64, No. 2. P. 76–83. https://doi.org/110.1145/3397512
4. Sinkova E.A., Petrov O.V., Khancuk A.I. Geochronological Atlas and Refer-ence Book of the Main Structural-Material Complexes of Russia – a Basic Information Resource for the Country's Geological Industry // Exploration and Protection of Mineral Resources. 2022. No. 90. P. 5–14.
https://doi.org/110.52349/0869-7892_2022_90_5-14
5. Wilkinson M.D., Dumontier M., Aalbersberg I.J. et al. The FAIR Guiding Principles for scientific data management and stewardship // Scientific Data. 2016. Vol. 3. P. 160018. https://doi.org/110.1038/sdata.2016.18
6. Laney D. 3D Data Management: Controlling Data Volume, Velocity, and Va-riety // META Group Research Note. 2001. Vol. 6, No. 70.
URL: https://diegonogare.net/wp-content/uploads/2020/08/3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf
7. Gandomi A., Haider M. Beyond the hype: Big data concepts, methods, and analytics // International Journal of Information Management. 2015. Vol. 35, No. 2. P. 137–144. https://doi.org/110.1016/j.ijinfomgt.2014.10.007
8. Hogan A., Blomqvist E., Cochez M., D’Amato C. et al. Knowledge Graphs // ACM Computing Surveys. 2021. Vol. 54, No. 4. https://doi.org/110.1145/3447772
9. Pellinen V.A., Cherkashina T.Y., Gustaitis M.A. Assessment of metal pollu-tion and subsequent ecological risk in the coastal zone of Olkhon Island, Lake Baikal, Russia // Science of the Total Environment. 2021. Vol. 786. P. 147441.
https://doi.org/110.1016/j.scitotenv.2021.147441
10. Zerizghi T., Yang Y., Wang W., Zhou Y., Zhang J., Yi Y. Ecological risk as-sessment of heavy metal concentrations in sediment and fish of a shallow lake: a case study of Baiyangdian Lake, North China // Environmental Monitoring and Assessment. 2020. Vol. 192. P. 154. https://doi.org/110.1007/s10661-020-8078-8
11. Zhang J., Wang K., Yi Q., Zhang T., Shi W., Zhou X. Transport and partition-ing of metals in river networks of a plain area with sedimentary resuspension and impli-cations for downstream lakes // Environmental Pollution. 2022. Vol. 294. P. 118668. https://doi.org/110.1016/j.envpol.2021.118668
12. Gopal V., Krishnamurthy R.R., Vignesh R., Nathan C.S. et al. Assessment of heavy metal contamination in the surface sediments of the Vedaranyam coast, South-ern India // Regional Studies in Marine Science. 2023. Vol. 65. P. 103081.
https://doi.org/110.1016/j.rsma.2023.103081
13. Chubarov V., Cherkashina T., Maltsev A., Chuparina E., Amosova A., Prose-kin S. Investigation of Soils and Pine Needles Using WDXRF and TXRF Techniques for As-sessment of the Environmental Pollution of Shelekhov District, Eastern Siberia, by the Aluminum Industry and Heat Power Engineering // Agronomy. 2022. Vol. 12. P. 454. https://doi.org/110.3390/agronomy12020454
14. Lunina O.V. The digital map of the Pliocene–Quaternary crustal faults in the Southern East Siberia and the adjacent Northern Mongolia // Geodynamics and Tectonophysics. 2016. Vol. 7, No. 3. P. 407–434.
https://doi.org/110.5800/GT-2016-7-3-0215
15. Cherkashin E.A., Lunina O.V., Demyanov L.O., Tsygankov A.V. Web-GIS viewer for active faults data represented as a knowledge graph // The 4th Scientific-practical Workshop Information Technologies: Algorithms Models, Systems. September 14, 2021, Irkutsk, Russia / CEUR Workshop Proceedings. 2021. Vol. 2984. P. 55–65. URL: https://ceur-ws.org/Vol-2984/paper8.pdf
16. Mazaeva O., Babicheva V., Kozyreva E. Geomorphological process devel-opment under the impact of man-made reservoir operation, a case study: Bratsk reser-voir, Baikal-Angara hydroengineering system, Russia // Bulletin of Engineering Geology and the Environment. 2019. Vol. 78. P. 4659–4672.
https://doi.org/110.1007/s10064-018-1428-x
17. Ovchinnikov G.I., Pavlov S.Kh., Trzhtsinsky Yu B. Changes in the Geological Environment in the Zones of Influence of the Angara–Yenisei Reservoirs. Novosibirsk: Nauka, 1999. 254 p.
18. Ranatunga S., Ødegård R.S., Jetlund K., Onstein E. Use of Semantic Web Technologies to Enhance the Integration and Interoperability of Environmental Geospa-tial Data: A Framework Based on Ontology-Based Data Access // ISPRS International Journal of Geo-Information. 2025. Vol. 14, No. 2. P. 52.
https://doi.org/110.3390/ijgi14020052
19. Moura A.-M., Porto F., Vidal V., Magalhães R.P. et al. A semantic integra-tion approach to publish and retrieve ecological data // International Journal of Web Information Systems. 2015. Vol. 11, No. 1. P. 87–119.
https://doi.org/110.1108/IJWIS-08-2014-0028
20. Marcus S., Tim D. GeoSciML: Development of a generic GeoScience Markup Language // Computers & Geosciences. 2005. Vol. 31, No. 9. P. 1095–1103.
https://doi.org/10.1016/j.cageo.2004.12.003.
21. Raskin R., Pan M. Knowledge representation in the semantic web for Earth and environmental terminology (SWEET) // Computers & Geosciences. 2005. Vol. 31, No. 9. P. 1119–1125. https://doi.org/110.1016/j.cageo.2004.12.004
22. Simons B.A., Raymond O., Jackson I., Lee K. OneGeology – Improving global access to geoscience // The 5th Global Workshop on Digital Soil Mapping. April 10–13, 2012, Sydney, Australia / Digital Soil Assessments and Beyond: Proceedings of CRC Press, 2012. P. 265–275.
23. Allison M.L., Ahern T., Arctur D. et al. EarthCube Governance Framework: A Proposal to the Community (Version 1.0) // EarthCube Governance Working Group Technical Report. 2012. 237 p.
24. Atakan K., Bjerrum L.W., Bungum H. et al. The European Plate Observing System and the Arctic // Arctic. 2015. Vol. 68, Suppl. 1. https://doi.org/110.14430/arctic4446


Most read articles by the same author(s)