|
|
基于本体模型的锂矿知识图谱的构建研究
|
Abstract:
地球科学属于典型的数据密集型科学，从非结构化数据中挖掘有效信息已逐渐发展为地球科学的热门研究方向之一。知识图谱作为一种非结构化信息的有效处理方案，为地球科学领域的知识挖掘提供全新的研究思路与技术手段，推动了地球科学知识的整合与共享。本体模型的提出进一步推动了知识图谱构建的规范性。但是，目前尚且缺乏对锂矿本体模型的研究。本文从具体矿床名称出发，构建了基于本体模型的锂矿知识图谱，最终探讨了知识图谱与自然语言处理技术的协同工作方式与大型语言模型在地球科学中的应用前景。本文指出未来的研究工作应(1) 构建基于本体模型的地球科学知识图谱，(2) 考虑推进基于本体模型知识图谱的应用，实现知识推理，(3) 将大型语言模型与知识图谱相结合，以期为进一步推动地球科学知识图谱的发展提供参考。
Earth science is a typical data-intensive science, mining effective information from unstructured data has gradually developed into one of the hot research directions of earth science. As an effective method of processing unstructured information, knowledge graph provides a new research idea and technical means for knowledge mining in the field of earth science, and promotes the integration and sharing of earth science knowledge. The proposal of ontology model further promotes the normalization of knowledge graph construction. However, at present, there is a lack of research on lithium ore ontology model. Based on the name of specific ore deposit, this paper constructs the knowledge graph of lithium ore based on ontology model, and finally discusses the cooperative working mode of knowledge graph and natural language processing technology and the application prospect of large-scale language model on earth science. This paper points out that future research should (1) construct ontology-based knowledge graphs of Earth sciences, (2) consider promoting the application of ontology-based knowledge graphs to realize knowledge reasoning, and (3) combine large-scale language models with knowledge graphs, in order to provide references for further promoting the development of knowledge graphs of earth science.
| [1] | Bergen, K.J., Johnson, P.A., de Hoop, M.V. and Beroza, G.C. (2019) Machine Learning for Data-Driven Discovery in Solid Earth Geoscience. Science, 363, eaau0323. https://doi.org/10.1126/science.aau0323 |
| [2] | Ma, X. (2022) Knowledge Graph Construction and Application in Geosciences: A Review. Computers & Geosciences, 161, Article ID: 105082. https://doi.org/10.1016/j.cageo.2022.105082 |
| [3] | 牛凤桂, 张贝, 陈石. 大数据时代的地球科学知识图谱研究现状与展望[J]. 地震学报, 2024, 46(3): 353-376. |
| [4] | Qiu, Q., Tian, M., Ma, K., Tan, Y.J., Tao, L. and Xie, Z. (2023) A Question Answering System Based on Mineral Exploration Ontology Generation: A Deep Learning Methodology. Ore Geology Reviews, 153, Article ID: 105294. https://doi.org/10.1016/j.oregeorev.2023.105294 |
| [5] | Qiu, Q., Wang, B., Ma, K., Lü, H., Tao, L. and Xie, Z. (2023) A Practical Approach to Constructing a Geological Knowledge Graph: A Case Study of Mineral Exploration Data. Journal of Earth Science, 34, 1374-1389. https://doi.org/10.1007/s12583-023-1809-3 |
| [6] | Singhal, A. (2012) Introducing the Knowledge Graph: Things, Not Strings. https://www.blog.google/products/search/introducing-knowledge-graph-things-not/ |
| [7] | Fensel, D., Şimşek, U., Angele, K., et al. (2020) Knowledge Graphs: Methodology, Tools and Selected Use Cases. Springer. http://link.springer.com/10.1007/978-3-030-37439-6 |
| [8] | Hao, X., Ji, Z., Li, X., Yin, L., Liu, L., Sun, M., et al. (2021) Construction and Application of a Knowledge Graph. Remote Sensing, 13, Article 2511. https://doi.org/10.3390/rs13132511 |
| [9] | 王益鹏, 张雪英, 党玉龙, 等. 顾及时空过程的台风灾害事件知识图谱表示方法[J]. 地球信息科学学报, 2023, 25(6): 1228-1239. |
| [10] | 冉一早, 董少春, 王汝成, 等. 铌钽矿床知识图谱的构建及应用实践[J]. 高校地质学报, 2023, 29(3): 359-371. |
| [11] | Gruber, T.R. (1993) A Translation Approach to Portable Ontology Specifications. Knowledge Acquisition, 5, 199-220. https://doi.org/10.1006/knac.1993.1008 |
| [12] | AL-Aswadi, F.N., Chan, H.Y. and Gan, K.H. (2022) From Ontology to Knowledge Graph Trend: Ontology as Foundation Layer for Knowledge Graph. In: Villazón-Terrazas, B., Ortiz-Rodriguez, F., Tiwari, S., Sicilia, M.A. and Martín-Moncunill, D., Eds., Knowledge Graphs and Semantic Web, Springer, 330-340. https://doi.org/10.1007/978-3-031-21422-6_25 |
| [13] | 邱芹军, 吴亮, 马凯, 等. 面向灾害应急响应的地质灾害链知识图谱构建方法[J]. 地球科学, 2023, 48(5): 1875-1891. |
| [14] | Noy, N.F., Fergerson, R.W. and Musen, M.A. (2000) The Knowledge Model of Protégé-2000: Combining Interoperability and Flexibility. In: Dieng, R. and Corby, O., Eds., Knowledge Engineering and Knowledge Management Methods, Models, and Tools, Springer, 17-32. https://doi.org/10.1007/3-540-39967-4_2 |
| [15] | 翟裕生, 姚书振, 蔡克勤. 矿床学[M]. 北京: 地质出版社, 2011. https://book.douban.com/subject/20315635/ |
| [16] | 薛春纪. 基础矿床学[M]. 北京: 地质出版社, 2006. https://product.dangdang.com/20057755.html |
| [17] | 张前龙, 周永章, 虞鹏鹏, 等. 多层次矿床本体的构建及在知识图谱中的应用[J]. 矿物岩石地球化学通报, 2024, 43(1): 211-217. |
| [18] | 廖振鑫. 面向地质领域的知识图谱构建研究及应用[D]: [硕士学位论文]. 成都: 电子科技大学, 2021. |
| [19] | 中国标准化研究院. GB/T 9649.16-2009地质矿产术语分类代码第16部分: 矿床学[S]. 北京: 中国标准出版社, 2009. https://std.samr.gov.cn/gb/search/gbDetailed?id=71F772D7D343D3A7E05397BE0A0AB82A |
| [20] | Wang, B., Ma, K., Wu, L., Qiu, Q., Xie, Z. and Tao, L. (2022) Visual Analytics and Information Extraction of Geological Content for Text-Based Mineral Exploration Reports. Ore Geology Reviews, 144, Article ID: 104818. https://doi.org/10.1016/j.oregeorev.2022.104818 |
| [21] | Wang, B., Wu, L., Xie, Z., Qiu, Q., Zhou, Y., Ma, K., et al. (2022) Understanding Geological Reports Based on Knowledge Graphs Using a Deep Learning Approach. Computers & Geosciences, 168, Article ID: 105229. https://doi.org/10.1016/j.cageo.2022.105229 |
| [22] | Qiu, Q., Huang, Z., Xu, D., Ma, K., Tao, L., Wang, R., et al. (2023) Integrating NLP and Ontology Matching into a Unified System for Automated Information Extraction from Geological Hazard Reports. Journal of Earth Science, 34, 1433-1446. https://doi.org/10.1007/s12583-022-1716-z |
| [23] | Enkhsaikhan, M., Liu, W., Holden, E. and Duuring, P. (2018) Towards Geological Knowledge Discovery Using Vector-Based Semantic Similarity. In: Gan, G., Li, B., Li, X. and Wang, S., Eds., Advanced Data Mining and Applications, Springer, 224-237. https://doi.org/10.1007/978-3-030-05090-0_20 |
