ORIGINAL RESEARCH article
Front. Earth Sci.
Sec. Geoinformatics
This article is part of the Research TopicBig Data Mining & Artificial Intelligence in Earth ScienceView all 9 articles
Knowledge Graph-Driven Decision Support for Cross-Regional Solid Mineral Deposits
Provisionally accepted
- 1Guangzhou Huali College, Guangzhou, China
- 2Sun Yat-Sen University, Guangzhou, China
- 3Pondicherry University, Puducherry, India
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To tackle the challenges of fragmented multi-regional data and obscured correlations in solid mineral deposit management, this study proposes a novel knowledge graph construction and application framework centered on a Content-Position Attention (CPA) mechanism. The core CPA model features a content-position fusion encoder and an entity grid decoder, which work in tandem to capture textual context and entity positional information, enabling precise extraction of overlapping and nested entity-relation triples. Based on this model, we built a domain ontology and integrated it with the Neo4j graph database to create a comprehensive knowledge graph for solid mineral deposits in South China. Evaluated on the self-constructed SC-Mineral dataset, the CPA model attained an overall F1-score of 91.8%. In complex scenarios including entity-pair overlap, single-entity overlap, and subject-object nested overlap, the model obtained F1-scores of 92.8%, 92.5%, and 89.8% respectively, demonstrating excellent capability in handling complex relations. The resulting knowledge graph system enables intelligent information retrieval, multi-dimensional correlation analysis, and mineralization potential prediction, demonstrating its practical effectiveness in supporting cross-regional resource management and intelligent decision-making. This work offers a replicable technical pathway for knowledge management and decision analysis in the mineral resources domain.
Keywords: Cross-regional management, EntityRelation Extraction, knowledge graph, Solid mineral deposit, Triple extraction
Received: 16 Dec 2025; Accepted: 03 Feb 2026.
Copyright: © 2026 Gao, Ma, Tang, Gopalakrishnan, Lu, Mai and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Le Gao
Jianhua Ma
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