Proceedings of the 2023 9th International Conference on Advances in Energy Resources and Environment Engineering (ICAESEE 2023)

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Application of Mineral Deposit Knowledge Graph for Jilin Province Utilizing Neo4j

Authors
Xiangjin Ran1, Yao Pei2, *
1College of Earth Sciences, Jilin University, 6 Ximinzhu Street, Changchun, China
2College of Geoexploration Science and Technology, Jilin University, 6 Ximinzhu Street, Changchun, China
*Corresponding author. Email: peiyao@jlu.edu.cn
Corresponding Author
Yao Pei
Available Online 14 May 2024.
DOI
10.2991/978-94-6463-415-0_10How to use a DOI?
Keywords
Mineral Deposit Knowledge Graph; Deep Learning; Natural Language Processing; Neo4j Graph Database
Abstract

A mineral deposit knowledge graph (KG) based on deep learning and natural language processing (NLP) can unveil the association between the Earth system and mineral formation. It automatically extracts the overarching model of mineral genesis and discovers mineralization laws, aiding researchers in expeditiously analyzing mineralization processes. This study constructs a mineral deposit KG for Jilin Province employing Neo4j. Initially, the Bidirectional Long Short-Term Memory (Bi-LSTM) and Conditional Random Field (CRF) model is employed to identify named entities in the text. Subsequently, the Piecewise Convolutional Neural Network (PCNN) model is employed to extract relationships between entities. Finally, the processed knowledge is imported into the Neo4j graph database for knowledge visualization. Experimental outcomes indicate that the Bi-LSTM+CRF model attains an accuracy of 91.6% and an F1 score of 90.1% in the named entity recognition task. The PCNN model reaches an accuracy of 86.4% and an F1 score of 89.3% in the relation extraction task. Through KG visualization, the correlation between controlling geological factors, prospecting indicators, and mineralization geological elements of typical mineral deposits in Jilin Province is analyzed. This provides novel technological means for the development and utilization of mineral resources in this region.

Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2023 9th International Conference on Advances in Energy Resources and Environment Engineering (ICAESEE 2023)
Series
Atlantis Highlights in Engineering
Publication Date
14 May 2024
ISBN
10.2991/978-94-6463-415-0_10
ISSN
2589-4943
DOI
10.2991/978-94-6463-415-0_10How to use a DOI?
Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Xiangjin Ran
AU  - Yao Pei
PY  - 2024
DA  - 2024/05/14
TI  - Application of Mineral Deposit Knowledge Graph for Jilin Province Utilizing Neo4j
BT  - Proceedings of the 2023 9th International Conference on Advances in Energy Resources and Environment Engineering (ICAESEE 2023)
PB  - Atlantis Press
SP  - 77
EP  - 82
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6463-415-0_10
DO  - 10.2991/978-94-6463-415-0_10
ID  - Ran2024
ER  -