A Practical Approach to Constructing a Geological Knowledge Graph: A Case Study of Mineral Exploration Data

Abstract

Open data initiatives have promoted governmental agencies and scientific organizations to publish data online for reuse. Research of geoscience focuses on processing georeferenced quantitative data (e.g., rock parameters, geochemical tests, geophysical surveys and satellite imagery) for discovering new knowledge. Geological knowledge is the cognitive result of human knowledge of the spatial distribution, evolution and interaction patterns of geological objects or processes. Knowledge graphs (KGs) can formalize unstructured knowledge into structured form and have been used in supporting decision-making recently. In this paper, we propose a novel framework that can extract the geological knowledge graph (GKG) from public reports relating to a modelling study. Based on the analysis of basic questions answered by geology, we summarize and abstract geological knowledge elements and then explore a geological knowledge representation model with three levels of “geological concepts-geological entities-geological relations” to describe semantic units of geological knowledge and their logic relations. Finally, based on the characteristics of mineral resource reports, the geological knowledge representation model oriented to “object relationships” and the hierarchical geological knowledge representation model oriented to “process relationships” are proposed with reference to the commonly used geological knowledge graph representation. The research in this paper can provide some implications for the formalization and structured representation of geological knowledge graphs.

Keywords

mineral resource report / geological knowledge / knowledge graph / ontology / hierarchical knowledge representation model

References

4D Initiative Team, 2018. White Paper of the 4D Initiative: Deep-Time Data Driven Discovery. https://4d.carnegiescience.edu/sites/default/files/4D_materials/4D_WhitePaper.pdf. (Accessed 4 March 2020)

Alzaidy, R., Caragea, C., Giles, C. L., 2019. Bi-LSTM-CRF Sequence Labeling for Keyphrase Extraction from Scholarly Documents. WWW’19: The World Wide Web Conference. May 13–17, 2019, San Francisco. https://doi.org/10.1145/3308558.3313642

Ballatore A, Bertolotto M, Wilson D. A Structural-Lexical Measure of Semantic Similarity for Geo-Knowledge Graphs. ISPRS Int. J. Geo-Inform., 2015, 4: 471-492.

Bauer, F., Kaltenböck, M., 2011. Linked Open Data: The Essentials. Mono/Monochrom. Vienna, Austria

Bharambe U, Durbha S S. Adaptive Pareto-Based Approach for Geo-Ontology Matching. Computers & Geosciences, 2018, 119: 92-108.

Chen, Y., Goldberg, S., Wang, D. Z., et al., 2016. Ontological Pathfinding. The 2016 International Conference on Management of Data. 26 June 2016, San Francisco. https://doi.org/10.1145/2882903.2882954

Daraio C, Lenzerini M, Leporelli C, . The Advantages of an Ontology-Based Data Management Approach: Openness, Interoperability and Data Quality. Scientometrics, 2016, 108(1): 441-455.

Deng, C., Jia, Y. T., Xu, H., et al., 2021. GAKG: A Multimodal Geoscience Academic Knowledge Graph. Proceedings of the 30th ACM International Conference on Information & Knowledge Management. November 1–5, 2021, Virtual Event, Queensland. https://doi.org/10.1145/3459637.3482003

Dong, X., Gabrilovich, E., Heitz, G., et al., 2014. Knowledge Vault: A Web-Scale Approach to Probabilistic Knowledge Fusion. The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. August 24–27, 2014, New York. https://doi.org/10.1145/2623330.2623623

Enkhsaikhan M, Holden E J, Duuring P, . Understanding Ore-Forming Conditions Using Machine Reading of Text. Ore Geology Reviews, 2021, 135: 104200

Fan R Y, Wang L Z, Yan J N, . Deep Learning-Based Named Entity Recognition and Knowledge Graph Construction for Geological Hazards. ISPRS International Journal of Geo-Information, 2019, 9(1): 15

Holden E J, Liu W, Horrocks T, . GeoDocA-Fast Analysis of Geological Content in Mineral Exploration Reports: A Text Mining Approach. Ore Geology Reviews, 2019, 111 102919

Jia Y, Qi Y L, Shang H J, . A Practical Approach to Constructing a Knowledge Graph for Cybersecurity. Engineering, 2018, 4 1 53-60.

Lafferty J, McCallum A, Pereira F C. Brodley C E, Danyluk A P. Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data. Proceedings of the Eighteenth International Conference on Machine Learning, 2001, San Francisco: Morgan Kaufmann Publishers Inc.

Li L, Liu Y, Zhu H H, . A Bibliometric and Visual Analysis of Global Geo-Ontology Research. Computers & Geosciences, 2017, 99 1-8.

Lin Y K, Shen S Q, Liu Z Y, . Erk k, Smith N A, . Neural Relation Extraction with Selective Attention over Instances. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2016, Berlin: Association for Computational Linguistics

Ma K, Tan Y J, Xie Z, . Chinese Toponym Recognition with Variant Neural Structures from Social Media Messages Based on BERT Methods. Journal of Geographical Systems, 2022, 24(2): 143-169.

Ma K, Tan Y J, Tian M, . Extraction of Temporal Information from Social Media Messages Using the BERT Model. Earth Science Informatics, 2022, 15(1): 573-584.

Ma K, Tian M, Tan Y J, . What is this Article About? Generative Summarization with the BERT Model in the Geosciences Domain.. Earth Science Informatics, 2022, 15(1): 21-36.

Ma X G. Knowledge Graph Construction and Application in Geosciences: A Review. Computers & Geosciences, 2022, 161: 105082

Ma X G, Ma C, Wang C B. A New Structure for Representing and Tracking Version Information in a Deep Time Knowledge Graph. Computers & Geosciences, 2020, 145 104620

Ma Y, Xie Z, Li G, . Text Visualization for Geological Hazard Documents via Text Mining and Natural Language Processing. Earth Science Informatics, 2022, 15(1): 439-454.

Nguyen H L, Vu D T, Jung J J. Knowledge Graph Fusion for Smart Systems: A Survey. Information Fusion, 2020, 61 56-70.

Nickel, M., Tresp, V., Kriegel, H. P., 2011. A Three-Way Model for Collective Learning on Multi-Relational Data. Proceedings of the 28th International Conference on Machine Learning, Bellevue

Normile D. Earth Scientists Plan a ‘Geological Google’. Science, 2019, 363(6430): 917

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

Powers, D. M. W., 1998. Applications and Explanations of Zipf’s lawProceedings of the Joint Conferences on New Methods in Language Processing and Computational Natural Language Learning-NeMLaP3/CoNLL’ 98. January 11–17, 1998. Sydney, Australia. Morristown, NJ, USA: Association for Computational Linguistics, Stroudsburg, PA, USA, 1998: 151–160

Qiu Q J, Xie Z, Wu L A. A Cyclic Self-Learning Chinese Word Segmentation for the Geoscience Domain. Geomatica, 2018, 72 1 16-26.

Qiu Q J, Xie Z, Wu L A, . GNER: A Generative Model for Geological Named Entity Recognition without Labeled Data Using Deep Learning. Earth and Space Science, 2019, 6(6): 931-946.

Qiu Q J, Xie Z, Wu L, . DGeoSegmenter: A Dictionary-Based Chinese Word Segmenter for the Geoscience Domain. Computers & Geosciences, 2018, 121: 1-11.

Qiu Q J, Xie Z, Wu L, . BiLSTM-CRF for Geological Named Entity Recognition from the Geoscience Literature. Earth Science Informatics, 2019, 12(4): 565-579.

Qiu Q J, Xie Z, Wu L, . Geoscience Keyphrase Extraction Algorithm Using Enhanced Word Embedding. Expert Systems with Applications, 2019, 125: 157-169.

Qiu Q J, Xie Z, Zhang D, . Knowledge Graph for Identifying Geological Disasters by Integrating Computer Vision with Ontology. Journal of Earth Science, 2023, 34(5): 1418-1432

Ramos J. Using Tf-Idf to Determine Word Relevance in Document Queries. Proceedings of the First Instructional Conference on Machine Learning, 2003, 242(1): 29-48

Schoenmackers S, Etzioni O, Weld D S, . Learning First-Order Horn Clauses from Web Text, 2010, New York: ACM, 1088-1098

Shi L, Jianping C, Jie X. Prospecting Information Extraction by Text Mining Based on Convolutional Neural Networks—A Case Study of the Lala Copper Deposit, China. IEEE Access, 2018, 6: 52286-52297.

Singhal A. 2012. Introducing the Knowledge Graph: Things, not Strings. Google Blog. https://www.blog.google/products/search/introducing-knowledge-graph-things-not/

Socher, R., Chen, D. Q., Manning, C. D., et al., 2013. Reasoning with Neural Tensor Networks for Knowledge Base Completion. Proceedings of the 26th International Conference on Neural Information Processing Systems-Volume 1. New York

Sun, Z. Q., Deng, Z. H., Nie, J. Y., et al., 2019. RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space. arXiv: 1902.10197. https://arxiv.org/abs/1902.10197

Wang B, Wu L, Li W J, . A Semi-Automatic Approach for Generating Geological Profiles by Integrating Multi-Source Data. Ore Geology Reviews, 2021, 134: 104190

Wang C B, Ma X G, Chen J G. Ontology-Driven Data Integration and Visualization for Exploring Regional Geologic Time and Paleontological Information. Computers & Geosciences, 2018, 115 12-19.

Wang, C. C., Cheng, P. J., 2018. Translating Representations of Knowledge Graphs with Neighbors. SIGIR’ 18: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. July 8–12, 2018, Ann Arbor. https://doi.org/10.1145/3209978.3210085

Wang D, Zou L, Feng Y S, . S-Store: An Engine for Large RDF Graph Integrating Spatial Information. Database Systems for Advanced Applications, 2013, Berlin, Heidelberg: Springer Berlin Heidelberg

Wang S, Zhang X Y, Ye P, . Geographic Knowledge Graph (GeoKG): A Formalized Geographic Knowledge Representation. ISPRS International Journal of Geo-Information, 2019, 8(4): 184

Wei, Z. P., Su, J. L., Wang, Y., et al., 2019. A Novel Cascade Binary Tagging Framework for Relational Triple Extraction. arXiv: 1909.03227. https://arxiv.org/abs/1909.03227

Wu L A, Xue L, Li C L, . A Knowledge-Driven Geospatially Enabled Framework for Geological Big Data. ISPRS International Journal of Geo-Information, 2017, 6 6 166

Xu H, Stenner S P, Doan S, . MedEx: A Medication Information Extraction System for Clinical Narratives. Journal of the American Medical Informatics Association, 2010, 17 1 19-24.

Yang C W, Huang Q Y, Li Z L, . Big Data and Cloud Computing: Innovation Opportunities and Challenges. International Journal of Digital Earth, 2017, 10(1): 13-53.

Zaslavsky, I., Valentine, D., Richard, S., et al., 2017. EarthCube Data Discovery Hub: Enhancing, Curating and Finding Data across Multiple Geoscience Data Sources. AGU Fall Meeting, New Orleans

Zhang S J, Boukamp F, Teizer J. Ontology-Based Semantic Modeling of Construction Safety Knowledge: Towards Automated Safety Planning for Job Hazard Analysis (JHA). Automation in Construction, 2015, 52: 29-41.

Zhang X Y, Huang Y, Zhang C J, . Geoscience Knowledge Graph (GeoKG): Development, Construction and Challenges. Transactions in GIS, 2022, 26(6): 2480-2494.

Zhang X Y, Zhang C J, Wu M G, . Spatiotemporal Features Based Geographical Knowledge Graph Construction. Scientia Sinica (Informationis), 2020, 50(7): 1019-1032. in Chinese with English Abstract)

Zheng K, Xie M, Zhang J, . A Knowledge Representation Model Based on the Geographic Spatiotemporal Process. International Journal of Geographical Information Science, 2022, 36(4): 674-691.

Zhou C H, Wang H, Wang C S, . Geoscience Knowledge Graph in the Big Data Era. Science China Earth Sciences, 2021, 64(7): 1105-1114.

Zhou P, Shi W, Tian J, . Erk K, Smith N A, . Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), 2016, Berlin: Association for Computational Linguistics

Zhu Y Q, Zhou W W, Xu Y, . Intelligent Learning for Knowledge Graph towards Geological Data. Scientific Programming, 2017, 2017: 1-13.