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Design and Construction of Lightweight Domain Ontology of Tectonic Geomorphology

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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.

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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.

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**, 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

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