基于提示和度量学习的小样本地质关系抽取

doi:10.13745/j.esf.sf.2025.4.65

地学前缘 ›› 2025, Vol. 32 ›› Issue (4): 250-261.DOI: 10.13745/j.esf.sf.2025.4.65

• 人工智能驱动地学知识发现 • 上一篇下一篇

基于提示和度量学习的小样本地质关系抽取

张志庭¹^,²^,³(), 彭帅¹^,⁴^,^*(), 阙翔⁴^,⁵, 陈麒玉¹^,⁴

1.中国地质大学(武汉) 计算机学院, 湖北武汉 430074
2.自然资源部基岩区矿产资源勘查工程技术创新中心, 贵州贵阳 550081
3.贵州省战略矿产智慧勘查全省重点实验室, 贵州贵阳550081
4.智能地学信息处理湖北省重点实验室, 湖北武汉 430074
5.福建农林大学计算机与信息学院, 福建福州 350002

收稿日期:2025-02-26 修回日期:2025-04-16 出版日期:2025-07-25 发布日期:2025-08-04
通信作者: *彭帅(2002—),男,硕士研究生,研究方向为地学信息化与智能化。E-mail: 2567438963@qq.com
作者简介:张志庭(1975—),男,博士,主要从事地矿工作信息化与智能化、三维地质过程模拟等研究工作。E-mail: zhangzt@cug.edu.cn
基金资助:
贵州省科技厅科技重大专项“基于大数据的贵州西部重要战略矿产成矿规律与高效找矿勘查研究”(2025-2027);贵州省锰矿精确探矿科技创新人才团队(贵州省锰矿探矿顶尖专家团队)项目(黔科合人才CXTD[2025]026);国家重点研发计划项目(2023YFF0718000);贵州省科技计划项目(黔科合平台人才ZDSYS[2023]005)

Few-shot geological relationship extraction based on prompt and metric learning

ZHANG Zhiting¹^,²^,³(), PENG Shuai¹^,⁴^,^*(), QUE Xiang⁴^,⁵, CHEN Qiyu¹^,⁴

1. School of Computer Science, China University of Geosciences (Wuhan), Wuhan 430074, China
2. Technology Innovation Center of Mineral Resources Explorations Engineering in Bedrock Zones, Ministry of Natural Resources, Guiyang 550081, China
3. Guizhou Key Laboratory for Strategic Mineral Intelligent Exploration, Guiyang 550081, China
4. Hubei Key Laboratory of Intelligent Geo-Information Processing, Wuhan 430074, China
5. College of Computer and Information Science, Fujian Agriculture and Forestry University, Fuzhou 350002, China

Received:2025-02-26 Revised:2025-04-16 Online:2025-07-25 Published:2025-08-04

摘要/Abstract

摘要：

地质领域研究正经历以构建新知识体系为核心、大数据为驱动的深刻变革。地质知识图谱的构建能够有效地解决在数据分散状态下的知识发现与推理受限等问题。关系抽取技术作为知识图谱构建的关键技术之一,在地质实体关系识别中发挥关键作用。传统关系抽取技术高度依赖大规模标注数据。然而地质领域中实体关系复杂且专业性强,人工标注数据耗时费力,致使大规模标注数据短缺。因此,传统关系抽取技术在地质领域的有效应用受限。针对上述困境,本研究提出基于原型网络的地质关系抽取小样本学习方法,创新性地引入增强提示学习机制,并通过对比学习优化实例表示和关系描述表示,显著地提升了原型代表性。同时,采用加权损失函数和困难任务辅助训练策略,增强模型对困难任务的关注度,有效地提高了整体准确率。实验结果表明,本文提出的模型在地质小样本关系抽取数据集的5way 1-shot场景下准确率达到82.16%,相比通用领域先进模型SimpleFSRE提升1.94%,相比原型网络Proto-BERT方法提升9.01%,验证了所提方法的有效性。

关键词: 小样本学习, 关系抽取, 地质知识图谱, 原型网络, 提示学习

Abstract:

The research in the field of geology is undergoing profound transformations, with the construction of a new knowledge system as its core and big data serving as the driving force. The construction of geological knowledge graphs can effectively address the challenge of knowledge discovery and limited reasoning in scenarios characterized by fragmented data. As one of the critical technologies for constructing knowledge graphs, relation extraction technology plays a pivotal role in identifying relationships between geological entities. Traditional relation extraction techniques are intrinsically contingent upon extensive large-scale annotated datasets. However, the intricacy and specificity of entity relationships in the geological domain render manual annotation of data laborious and time-consuming, consequently leading to a paucity of large-scale labeled datasets. Therefore, the effective implementation of traditional relation extraction techniques within the geological domain is significantly circumscribed. Given the above dilemmas, this study proposes a few-shot learning method for geological relation extraction based on the prototypical network, which innovatively introduces an enhanced prompt learning mechanism and optimizes the instance representation and relation description representation through contrastive learning, thereby significantly improving the representativeness of the prototype. Meanwhile, the weighted loss function and difficult task-assisted training strategy are adopted to enhance the model’s focus on difficult tasks, which effectively improves the overall accuracy. The experimental findings demonstrate that our approach achieves an accuracy of 82.16% in the 5-way 1-shot scenario of a geological few-shot relation extracted dataset. This represents an enhancement of 1.94% over the advanced general-domain model, SimpleFSRE, and 9.01% over the prototypical network, Proto-BERT method. These results substantiate the efficacy of our method.

Key words: few-shot learning, relation extraction, geological knowledge graph, prototype network, prompt learning

中图分类号:

P209
TP391

张志庭, 彭帅, 阙翔, 陈麒玉. 基于提示和度量学习的小样本地质关系抽取[J]. 地学前缘, 2025, 32(4): 250-261.

ZHANG Zhiting, PENG Shuai, QUE Xiang, CHEN Qiyu. Few-shot geological relationship extraction based on prompt and metric learning[J]. Earth Science Frontiers, 2025, 32(4): 250-261.

图/表 9

参考文献 27

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关系大类	关系类别	关系描述
时空关系	时间地点演变	指某地质事件发生的具体地质年代或时间跨度。指地质事件或地质构造所在的具体位置。表示地质体或地质构造随时间的变化过程。
从属关系	属于拥有	表示某地质现象或地质单元归属于特定的构造系统。指某一地质区域或构造拥有的资源或特性。
因果关系	导致	指某一地质事件或过程引发了另一地质现象。
特征关系	地质构造属性	描述地球表面或地下的结构特征。指地质体或地质单元的固有特征。
功能关系	用途	指某一地质资源或区域的实际应用。
交互关系	响应	指地质系统对外部应力、环境变化或人类活动的反应。
认知关系	认为研究	表达地质学家基于已有的地质证据对某地质现象的解释或假设。指对地质现象、地质构造或地质资源的科学调查和分析。

关系大类	关系类别	关系描述
时空关系	时间地点演变	指某地质事件发生的具体地质年代或时间跨度。指地质事件或地质构造所在的具体位置。表示地质体或地质构造随时间的变化过程。
从属关系	属于拥有	表示某地质现象或地质单元归属于特定的构造系统。指某一地质区域或构造拥有的资源或特性。
因果关系	导致	指某一地质事件或过程引发了另一地质现象。
特征关系	地质构造属性	描述地球表面或地下的结构特征。指地质体或地质单元的固有特征。
功能关系	用途	指某一地质资源或区域的实际应用。
交互关系	响应	指地质系统对外部应力、环境变化或人类活动的反应。
认知关系	认为研究	表达地质学家基于已有的地质证据对某地质现象的解释或假设。指对地质现象、地质构造或地质资源的科学调查和分析。

	地质数据集		Fewrel
模型	5way-1shot	5way-5shot	5way-1shot	5way-5shot	10way-1shot	10way-5shot
Proto-BERT	73.15%	74.96%	84.77%	89.54%	76.85%	83.42%
BERT-PAIR	73.45%	75.02%	85.66%	89.48%	76.84%	81.76%
HCRP	79.17%	80.28%	90.90%	93.22%	84.11%	87.79%
SimpleFSRE	80.22%	81.14%	91.29%	94.05%	86.09%	89.68%
GFSRE	82.16%	82.88%	92.98%	94.39%	86.71%	90.23%

	地质数据集		Fewrel
模型	5way-1shot	5way-5shot	5way-1shot	5way-5shot	10way-1shot	10way-5shot
Proto-BERT	73.15%	74.96%	84.77%	89.54%	76.85%	83.42%
BERT-PAIR	73.45%	75.02%	85.66%	89.48%	76.84%	81.76%
HCRP	79.17%	80.28%	90.90%	93.22%	84.11%	87.79%
SimpleFSRE	80.22%	81.14%	91.29%	94.05%	86.09%	89.68%
GFSRE	82.16%	82.88%	92.98%	94.39%	86.71%	90.23%

Model	5way-5shot
GFSRE	82.88%
去除增强提示学习模板	81.04%
去除对比学习模块	80.48%
去除困难任务的加权损失与辅助训练任务	81.38%

基于提示和度量学习的小样本地质关系抽取

Few-shot geological relationship extraction based on prompt and metric learning

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