Joint extraction method for entity relations from mineral resources reports integrating dependency parsing and improved graph convolutional networks

[Display omitted] •Propose a GCN entity relationship joint extraction model based on dependency syntax analysis.•Propose a GCN with stacked pointer networks to address long entity and overlapping entity.•Propose the use of the axial attention mechanism and BiLSTM model to effectively capture context...

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Published inOre geology reviews Vol. 182; p. 106640
Main Authors Qiu, Qinjun, Jin, Xiangguo, Tian, Miao, Wu, Qirui, Tao, Liufeng, Chen, Jianguo, Xie, Zhong
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.07.2025
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Online AccessGet full text
ISSN0169-1368
1872-7360
DOI10.1016/j.oregeorev.2025.106640

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Abstract [Display omitted] •Propose a GCN entity relationship joint extraction model based on dependency syntax analysis.•Propose a GCN with stacked pointer networks to address long entity and overlapping entity.•Propose the use of the axial attention mechanism and BiLSTM model to effectively capture contextual semantic features. Geological reports, as crucial technical documents reflecting the outcomes of geological survey work, encapsulate extensive expert and domain knowledge. Geological knowledge graphs integrate vast amounts of data, facilitating efficient and rapid extraction of knowledge embedded within geoscientific data. The extraction of geological entity relations is a key method in creating these knowledge graphs. Existing techniques for extracting geological entities and their relations encounter difficulties such entity overlap, relation overlap, and the challenge of obtaining deep semantic information because of the vastness and complexity of geological data. Our study suggests a collaborative extraction model for entity relations that integrates dependency syntactic relations with a graph convolutional network (GCN) in order to address these problems. This model learns dependency syntactic structures and deep semantic information by building a GCN that includes dependency syntactic relations. A pointer network decoder is then added to increase entity relation extraction efficiency. Dependencies between words in a phrase, such as subject-verb and verb-object relations, are revealed via dependency syntactic analysis. By structuring these dependencies into a graph, the model captures syntactic structural information. Through operations involving adjacency matrices and feature matrices, the model effectively propagates and aggregates node information, thereby capturing the global dependency syntactic structure and deep semantic information of sentences. The integration of dependency syntactic relations with GCN processing enables the model to more accurately comprehend entity relations within sentences. Results from experiments show that this model successfully tackles problems like overlapping entity relations and the challenge of gleaning deep semantic information from geological texts. It achieves a 79.73% accuracy rate and a 77.98% F1 score on geological text datasets.
AbstractList [Display omitted] •Propose a GCN entity relationship joint extraction model based on dependency syntax analysis.•Propose a GCN with stacked pointer networks to address long entity and overlapping entity.•Propose the use of the axial attention mechanism and BiLSTM model to effectively capture contextual semantic features. Geological reports, as crucial technical documents reflecting the outcomes of geological survey work, encapsulate extensive expert and domain knowledge. Geological knowledge graphs integrate vast amounts of data, facilitating efficient and rapid extraction of knowledge embedded within geoscientific data. The extraction of geological entity relations is a key method in creating these knowledge graphs. Existing techniques for extracting geological entities and their relations encounter difficulties such entity overlap, relation overlap, and the challenge of obtaining deep semantic information because of the vastness and complexity of geological data. Our study suggests a collaborative extraction model for entity relations that integrates dependency syntactic relations with a graph convolutional network (GCN) in order to address these problems. This model learns dependency syntactic structures and deep semantic information by building a GCN that includes dependency syntactic relations. A pointer network decoder is then added to increase entity relation extraction efficiency. Dependencies between words in a phrase, such as subject-verb and verb-object relations, are revealed via dependency syntactic analysis. By structuring these dependencies into a graph, the model captures syntactic structural information. Through operations involving adjacency matrices and feature matrices, the model effectively propagates and aggregates node information, thereby capturing the global dependency syntactic structure and deep semantic information of sentences. The integration of dependency syntactic relations with GCN processing enables the model to more accurately comprehend entity relations within sentences. Results from experiments show that this model successfully tackles problems like overlapping entity relations and the challenge of gleaning deep semantic information from geological texts. It achieves a 79.73% accuracy rate and a 77.98% F1 score on geological text datasets.
ArticleNumber 106640
Author Jin, Xiangguo
Wu, Qirui
Tao, Liufeng
Tian, Miao
Xie, Zhong
Qiu, Qinjun
Chen, Jianguo
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Keywords Geological survey report
Geological knowledge graph
Graph convolutional network
Geological entity relation extraction
Dependent syntax
Language English
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Snippet [Display omitted] •Propose a GCN entity relationship joint extraction model based on dependency syntax analysis.•Propose a GCN with stacked pointer networks to...
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StartPage 106640
SubjectTerms Dependent syntax
Geological entity relation extraction
Geological knowledge graph
Geological survey report
Graph convolutional network
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Title Joint extraction method for entity relations from mineral resources reports integrating dependency parsing and improved graph convolutional networks
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