Abstract
As data size grows and computing power evolves, artificial intelligence has become one of the most important tools for assisting data-intensive scientific discoveries. The development of artificial intelligence applications in geoscience requires the understanding of enormous quantities of concepts and thus requires the organization of knowledge into a structured form, which is ontology. Compared with common-sense ontologies, the concepts in geoscience are extremely abstract and difficult to understand. It is challenging to use natural language processing technologies to build ontologies in geoscience from the bottom up. Meanwhile, applications of ontology in deep learning and data integration also reveal the importance of constructing a geoscience ontology. Because of the complexity and transdisciplinary nature, this study focuses on the field of tectonic geomorphology. Based on the understanding and experience of experts in geoscience, a top-down approach is used to construct a tectonic geomorphology ontology as part of the geoscience ontology. This research started with the proposal of a method for constructing ontologies, then built a tectonic geomorphology ontology, and finally checked, validated, and applied the ontology, covering common concepts in geoscience and dedicated concepts in tectonic geomorphology. The tectonic geomorphology ontology is an important part of the whole geoscience ontology.
Similar content being viewed by others
References Cited
Al-Aswadi, F. N., Chan, H. Y., Gan, K. H., 2020. Automatic Ontology Construction from Text: A Review from Shallow to Deep Learning Trend. Artificial Intelligence Review, 53(6): 3901–3928. https://doi.org/10.1007/s10462-019-09782-9
Arp, R., Smith, B., Spear, A. D., 2015. Building Ontologies with Basic Formal Ontology. The MIT Press, Cambridge. 248
Arrowsmith, J. R., Zielke, O., 2009. Tectonic Geomorphology of the San Andreas Fault Zone from High Resolution Topography: An Example from the Cholame Segment. Geomorphology, 113(1/2): 70–81. https://doi.org/10.1016/j.geomorph.2009.01.002
Babaie, H. A., Oldow, J. S., Babaei, A., et al., 2006. Designing a Modular Architecture for the Structural Geology Ontology. In: Sinha, A. K., ed., Geoinformatics: Data to Knowledge. Geological Society of America, 397: 269–282. https://doi.org/10.1130/2006.2397(21)
Burbank, D. W., Anderson, R. S., 2011. Tectonic Geomorphology. Wiley-Blackwell, Hoboken
Chen, J. Y., Lécué, F., Geng, Y. X., et al., 2020. Ontology-Guided Semantic Composition for Zero-Shot Learning. The Seventeenth International Conference on Principles of Knowledge Representation and Reasoning. September 12–18, 2020. Rhodes. https://doi.org/10.24963/kr.2020/87
Cicconeto, F., Vieira, L. V., Abel, M., et al., 2020. A Spatial Relation Ontology for Deep-Water Depositional System Description in Geology. In: Lemos, D. L. D. S., Sales, T. P., Campos, M. L. M., et al., eds., Proceedings of the XIII Seminar on Ontology Research in Brazil and IV Doctoral and Masters Consortium on Ontologies. Ontobras, Vitória. 35–47
Cicconeto, F., Vieira, L. V., Abel, M., et al., 2022. GeoReservoir: An Ontology for Deep-Marine Depositional System Geometry Description. Computers & Geosciences, 159: 105005. https://doi.org/10.1016/j.cageo.2021.105005
Cox, S. J. D., Richard, S. M., 2015. A Geologic Timescale Ontology and Service. Earth Science Informatics, 8(1): 5–19. https://doi.org/10.1007/s12145-014-0170-6
Deng, X. Y., 2015. Study on the Construction of Domain-Specific Ontology in Oil Field. The 2015 International Conference on Education Technology, Management and Humanities Science, Advances in Social Science, Education and Humanities Research. March 21–22, 2015, **’an
DePolo, C. M., Anderson, J. G., 2000. Estimating the Slip Rates of Normal Faults in the Great Basin, USA. Basin Research, 12(3/4): 227–240. https://doi.org/10.1111/j.1365-2117.2000.00131.x
Dragoni, M., da Costa Pereira, C., Tettamanzi, A. G. B., 2012. A Conceptual Representation of Documents and Queries for Information Retrieval Systems by Using Light Ontologies. Expert Systems with Applications, 39(12): 10376–10388. https://doi.org/10.1016/j.eswa.2012.01.188
Fernández-López, M., Gómez-Pérez, A., Juristo, N., 1997. Methontology: from Ontological Art towards Ontological Engineering. The AAAI-97 Spring Symposium Series on Ontological Engineering. AAAI, Stanford. 33–40
Fossen, H., 2010. Structural Geology. Cambridge University Press, Cambridge. 524
Garcia, L. F., Abel, M., Perrin, M., et al., 2020. The GeoCore Ontology: A Core Ontology for General Use in Geology. Computers & Geosciences, 135: 104387. https://doi.org/10.1016/j.cageo.2019.104387
Ge, J. K., Li, Z. S., Li, T. F., 2012. A Novel Chinese Domain Ontology Construction Method for Petroleum Exploration Information. Journal of Computers, 7(6): 1445–1452. https://doi.org/10.4304/jcp.7.6.1445-1452
Gruber, T. R., 1993. A Translation Approach to Portable Ontology Specifications. Knowledge Acquisition, 5(2): 199–220. https://doi.org/10.1006/knac.1993.1008
Grüninger, M., Fox, M. S., 1995. Methodology for the Design and Evaluation of Ontologies. The IJCAI95 on Basic Ontological Issues in Knowledge Sharing, Montreal
Hotho, A., Maedche, A., Staab, S., 2002. Ontology-Based Text Document Clustering. KI, 16(4): 48–54
Jiang, S. J., Zheng, Y., Solomatine, D., 2020. Improving AI System Awareness of Geoscience Knowledge: Symbiotic Integration of Physical Approaches and Deep Learning. Geophysical Research Letters, 47(13): e2020GL088229. https://doi.org/10.1029/2020g1088229
Kalbasi, R., Janowicz, K., Reitsma, F., et al., 2014. Collaborative Ontology Development for the Geosciences. Transactions in GIS, 18(6): 834–851. https://doi.org/10.1111/tgis.12070
Keller, E. A., Pinter, N., 2002. Active Tectonics: Earthquakes, Uplift, and Landscape. Prentice Hall, Upper Saddle River. 362
Liu, J., Zhang, J., Ge, Y., et al., 2018. Tectonic Geomorphology: An Interdisciplinary Study of the Interaction among Tectonic Climatic and Surface Processes. Chinese Science Bulletin, 63(30): 3070–3088 (in Chinese with English Abstract)
Matoš, B., Pérez-Peña, J. V., Tomljenović, B., 2016. Landscape Response to Recent Tectonic Deformation in the SW Pannonian Basin: Evidence from DEM-Based Morphometric Analysis of the Bilogora Mt. Area, NE Croatia. Geomorphology, 263: 132–155. https://doi.org/10.1016/j.geomorph.2016.03.020
Musen, M. A., Protégé Team, 2015. The Protégé Project: A Look Back and a Look Forward. AI Matters, 1(4): 4–12. https://doi.org/10.1145/2757001.2757003
Nimmagadda, S. L., Dreher, H., 2008. Petroleum Ontology: An Effective Data Integration and Mining Methodology Aiding Exploration of Commercial Petroleum Plays. 2008 6th IEEE International Conference on Industrial Informatics. July 13–16, 2008, Daejeon, 1289–1295. https://doi.org/10.1109/indin.2008.4618302
Noy, N. F., Mcguinness, D. L., 2001. Ontology Development 101: A Guide to Creating Your First Ontology. https://protege.stanford.edu/conference/2004/slides/Ontology101_tutorial.pdf
Owen, L. A., 2022. Tectonic Geomorphology: A Perspective. In: Shroder, J. F., ed., Treatise on Geomorphology. Elsevier, Amsterdam. https://doi.org/10.1016/b978-0-12-818234-5.00155-3
Peraketh, B., Menzel, C. P., Mayer, R. J., et al., 1994. Ontology Capture Method (IDEF5), Armstrong Laboratory, Ohio
Perrin, M., Mastella, L. S., Morel, O., et al., 2011. Geological Time Formalization: An Improved Formal Model for Describing Time Successions and Their Correlation. Earth Science Informatics, 4(2): 81–96. https://doi.org/10.1007/s12145-011-0080-9
Poveda-Villalón, M., Gómez-Pérez, A., Suárez-Figueroa, M. C., 2014. OOPS! (OntOlogy Pitfall Scanner!): An On-Line Tool for Ontology Evaluation. International Journal on Semantic Web & Information Systems, 10(2): 7–34. https://doi.org/10.4018/ijswis.2014040102
Rajpathak, D., Xu, Y. M., Gibbs, I., 2020. An Integrated Framework for Automatic Ontology Learning from Unstructured Repair Text Data for Effective Fault Detection and Isolation in Automotive Domain. Computers in Industry, 123: 103338. https://doi.org/10.1016/j.compind.2020.103338
Sbissi, S., Mahfoudh, M., Gattoufi, S., 2019. Ontology Learning from Clinical Practice Guidelines. The 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management. September 17–19, 2019. Vienna
Sevindik Mentes, H., 2012. Design and Development of a Mineral Exploration Ontology: [Dissertation]. Georgia State University, Atlanta
Shoval, P., Maidel, V., Shapira, B., 2008. An Ontology-Content-Based Filtering Method. International Journal “Information Theories & Applications”, 15(4): 303–314
Sirin, E., Parsia, B., Grau, B. C., et al., 2007. Pellet: A Practical OWL-DL Reasoner. Journal of Web Semantics, 5(2): 51–53. https://doi.org/10.1016/j.websem.2007.03.004
Topal, S., Keller, E., Bufe, A., et al., 2016. Tectonic Geomorphology of a Large Normal Fault: Akşehir Fault, SW Turkey. Geomorphology, 259: 55–69. https://doi.org/10.1016/j.geomorph.2016.01.014
Tripathi, A., Babaie, H. A., 2008. Develo** a Modular Hydrogeology Ontology by Extending the SWEET Upper-Level Ontologies. Computers & Geosciences, 34(9): 1022–1033. https://doi.org/10.1016/j.cageo.2007.08.009
Tsimi, C., Ganas, A., 2015. Using the ASTER Global DEM to Derive Empirical Relationships among Triangular Facet Slope, Facet Height and Slip Rates along Active Normal Faults. Geomorphology, 234: 171–181. https://doi.org/10.1016/j.geomorph.2015.01.018
Uschold, M., King, M., 1995. Towards a Methodology for Building Ontologies. The IJCAI95 on Basic Ontological Issues in Knowledge Sharing, Montreal
Wang, C. B., Ma, X. G., Chen, J. G., 2018. Ontology-Driven Data Integration and Visualization for Exploring Regional Geologic Time and Paleontological Information. Computers & Geosciences, 115: 12–19. https://doi.org/10.1016/j.cageo.2018.03.004
Wafróbski, J., 2020. Ontology Learning Methods from Text—An Extensive Knowledge-Based Approach. Procedia Computer Science, 176: 3356–3368. https://doi.org/10.1016/j.procs.2020.09.061
Wei, Y. Y., Wang, R. J., Hu, Y. M., et al., 2012. From Web Resources to Agricultural Ontology: A Method for Semi-Automatic Construction. Journal of Integrative Agriculture, 11(5): 775–783. https://doi.org/10.1016/s2095-3119(12)60067-7
Yang, J., Li, Y., 2011. Active Tectonic Geomorphology. Peking University Press, Bei**g (in Chinese)
Yi, R., Chen, J., Deng, M., et al., 2009. An Approach for the Design of Loess Geomorphology Ontology. Geography and Geo-Information Science, 25(2): 46–49 (in Chinese with English Abstract)
Zhong, J., Aydina, A., McGuinness, D. L., 2009. Ontology of Fractures. Journal of Structural Geology, 31(3): 251–259. https://doi.org/10.1016/j.jsg.2009.01.008
Zhou, C. H., Wang, H., Wang, C. S., et al., 2021. Geoscience Knowledge Graph in the Big Data Era. Science China Earth Sciences, 64(7): 1105–1114. https://doi.org/10.1007/s11430-020-9750-4
Zouaq, A., 2011. Shallow and Deep Natural Language Processing for Ontology Learning: A Quick Overview. In: Wong, W., Liu, W., Bennamoun, M., eds., Ontology Learning and Knowledge Discovery Using the Web: Challenges and Recent Advances. Information Science Reference, Hershey. 16–37
Acknowledgments
This work was supported by the National Key Research and Development Program of China (No. 2021YFB3900901). This work was conducted using the Protégé resource, which is supported by the National Institute of General Medical Sciences of the United States National Institutes of Health (No. GM10331601). The final publication is available at Springer via https://doi.org/10.1007/s12583-022-1779-x.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
The authors declare that they have no conflict of interest.
Rights and permissions
About this article
Cite this article
**, J., Wu, J. & Wu, M. Design and Construction of Lightweight Domain Ontology of Tectonic Geomorphology. J. Earth Sci. 34, 1350–1357 (2023). https://doi.org/10.1007/s12583-022-1779-x
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12583-022-1779-x