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Deep Learning and Network Analysis:Classifying and Visualizing Geologic Hazard Reports

Journal of Earth Science 2024年第4期35卷 1289-1303页

作者： Wenjia Li Liang Wu Xinde Xu Zhong Xie qinjun qiu Hao Liu Zhen Huang Jianguo Chen School of Geography and Information Engineering China University of GeosciencesWuhan430078China Key Laboratory of Geological Survey and Evaluation of Ministry of Education China University of GeosciencesWuhan430078China School of Computer Science China University of GeosciencesWuhan430078China Key Laboratory of Urban Land Resources Monitoring and Simulation Ministry of Natural ResourcesShenzhen518034China Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges UniversityYichang443002China Wuhan Geomatics Institute Wuhan430074China Faculty of Earth Resources China University of GeosciencesWuhan430074China

If progress is to be made toward improving geohazard management and emergency decision-making,then lessons need to be learned from past geohazard information.A geologic hazard report provides a useful and reliable source of information about the occurrence of an event,along with detailed information about the condition or factors of the *** such reports,however,can be a challenging process because these texts are often presented in unstructured long text formats,and contain rich specialized and detailed *** text classification is commonly used to mine disaster text data in open domains(e.g.,news and microblogs).But it has limitations to performing contextual long-distance dependencies and is insensitive to discourse *** deficiencies are most obviously exposed in long text ***,this paper uses the bidirectional encoder representations from Transformers(BERT),to model long ***,utilizing a softmax layer to automatically extract text features and classify geohazards without manual *** latent Dirichlet allocation(LDA)model is used to examine the interdependencies that exist between causal variables to visualize *** proposed method is useful in enabling the machine-assisted interpretation of text-based ***,it can help users visualize causes,processes,and other geohazards and assist decision-makers in emergency responses.

关键词： geologic hazard network analysis latent dirichlet allocation text classification deep learning

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GeoNER:Geological Named Entity Recognition with Enriched Domain Pre-Training Model and Adversarial Training

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Acta Geologica Sinica(English Edition) 2024年第5期 1404-1417页

作者： MA Kai HU Xinxin TIAN Miao TAN Yongjian ZHENG Shuai TAO Liufeng qiu qinjun Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges University College of Computer and Information Technology China Three Gorges University Key Laboratory of Geological Survey and Evaluation of Ministry of Education China University of Geosciences School of Computer Science China University of Geosciences Key Laboratory of Quantitative Resource Evaluation and Information Engineering Ministry of Natural Resources China University of Geosciences

As important geological data, a geological report contains rich expert and geological knowledge, but the challenge facing current research into geological knowledge extraction and mining is how to render accurate understanding of geological reports guided by domain knowledge. While generic named entity recognition models/tools can be utilized for the processing of geoscience reports/documents, their effectiveness is hampered by a dearth of domain-specific knowledge, which in turn leads to a pronounced decline in recognition accuracy. This study summarizes six types of typical geological entities, with reference to the ontological system of geological domains and builds a high quality corpus for the task of geological named entity recognition(GNER). In addition, Geo Wo BERT-adv BGP(Geological Word-base BERTadversarial training Bi-directional Long Short-Term Memory Global Pointer) is proposed to address the issues of ambiguity, diversity and nested entities for the geological entities. The model first uses the fine-tuned word granularitybased pre-training model Geo Wo BERT(Geological Word-base BERT) and combines the text features that are extracted using the Bi LSTM(Bi-directional Long Short-Term Memory), followed by an adversarial training algorithm to improve the robustness of the model and enhance its resistance to interference, the decoding finally being performed using a global association pointer algorithm. The experimental results show that the proposed model for the constructed dataset achieves high performance and is capable of mining the rich geological information.

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地球表层系统开放科学数据目录关联网络构建研究进展

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地球信息科学学报 2024年第4期26卷 866-880页

作者：邱芹军郝孟璂谢忠陶留锋李伟杰王洋刘建东中国地质大学(武汉)计算机学院武汉430074 地理信息系统国家地方联合工程实验室武汉430074 中国地质大学(武汉)地质探测与评估教育部重点实验室武汉430074

快速发现、挖掘并利用海量地球表层系统(以下简称“地表系统”)开放科学数据,是大数据时代下地表系统开放科学数据共享新的发展趋势和前沿研究方向。地表系统开放科学数据具有组织分散、多源异构、多模态、多类型等特性,通常以专题共享... 详细信息

快速发现、挖掘并利用海量地球表层系统(以下简称“地表系统”)开放科学数据,是大数据时代下地表系统开放科学数据共享新的发展趋势和前沿研究方向。地表系统开放科学数据具有组织分散、多源异构、多模态、多类型等特性,通常以专题共享网站、数据服务、元数据、期刊论文(特别是数据论文)等形式存在,研究发展适应不同模态的地表系统开放数据挖掘方法、分析其共享质量是充分利用这些数据的关键科学问题。关联网络为地表系统开放科学数据的语义互联和知识发现提供了有力的支撑,其以元数据URI(Uniform Resource Identifier)为节点,元数据间的语义关系为边,节点间关联性的强弱作为边的值。本文从地表系统开放科学数据关联网络构建角度出发,对其发展现状、基本特征和构建技术进行了调研与分析。首先选取国内外典型关联网络和相关文献作为研究对象,根据所选取的9个主流关联网络和200余篇相关文献,从关联网络的基本特征和构建技术2个方面进行比较分析。在基本特征方面,分析了关联网络的数据来源、自动化程度和更新方式;在构建技术方面,介绍了关联指标的选择,讨论了地表系统开放科学数据特征的提取、表示和计算方法。最后提出了未来地表系统关联网络构建的建议,包括构建高质量、全覆盖的地表系统开放科学数据本体、考虑顾及“时间-空间-内容”地学知识复杂关系及推理、建立面向多语言的地表系统开放科学数据关联网络方法以及提升地表系统开放科学数据关联网络应用成效等。

关键词：地球表层系统关联网络数据目录特征计算元数据共享程度评价数据本体关联指标

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面向地质图的知识图谱构建及智能问答应用

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地质科学 2024年第2期59卷 588-602页

作者：段雨希邱芹军田苗马凯谢忠陶留锋刘俊杰国家地理信息系统工程技术研究中心武汉430074 中国地质大学(武汉)计算机与信息学院武汉430074 地理信息系统国家地方联合工程实验室武汉430074 中国地质大学(武汉)地质探测与评估教育部重点实验室武汉430074 三峡大学计算机与信息学院湖北宜昌430002 湖北省水电工程智能视觉监测重点实验室湖北宜昌443002

海量地质图件蕴含着丰富的地学基础知识及专家经验知识。地质图主要表达了通过区域地质调查、矿产地质调查所获取的地球表面的地质知识(如地层单元、岩体、断裂等)。如何快速地从矢量地质图件中抽取地质知识并形成知识服务是目前地学知... 详细信息

海量地质图件蕴含着丰富的地学基础知识及专家经验知识。地质图主要表达了通过区域地质调查、矿产地质调查所获取的地球表面的地质知识(如地层单元、岩体、断裂等)。如何快速地从矢量地质图件中抽取地质知识并形成知识服务是目前地学知识图谱及知识服务研究的前沿。由于传统的地质图知识抽取主要依赖人工方式进行综合分析,本文聚焦于矢量地质图件知识表达与抽取研究,提出了一种地质图知识表达框架,提取地质图中所包含的地质实体及关系,将地质图信息以知识图谱的形式表达,并开展了基于地质矢量知识图谱的智能问答应用。最后以江西省于都县银坑幅矢量数据集为例开展实验验证分析,结果表明,本文方法能够较为全面地获取地质图中各个地质对象的信息,提高了地质图语义表达的效果,同时也可以提高地质学习人员对地质图的理解和认识,让计算机能够大规模获取地质图的知识内容。

关键词：地质图知识表达模型地质知识图谱地质矢量图件智能问答空间认知

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多模态数据的地质图关联网络构建及知识服务

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地质论评 2024年第4期70卷 1469-1482页

作者：邱芹军段雨希田苗吴麒瑞马凯陶留锋谢忠自然资源部城市国土资源监测与仿真重点实验室广东深圳518034 中国地质大学(武汉)计算机与信息学院武汉430074 地理信息系统国家地方联合工程实验室武汉430074 国家地理信息系统工程技术研究中心武汉430074 中国地质大学(武汉)地质探测与评估教育部重点实验室武汉430074 中国地质大学(武汉)未来技术学院武汉430074 湖北省水电工程智能视觉监测重点实验室湖北宜昌443002 三峡大学计算机与信息学院湖北宜昌443002

地质图件及其附属资源(如描述文本)作为地质知识的重要存储媒介,蕴含着大量的地学知识及专家经验知识及隐式知识。快速精准地对多模态地质数据进行知识规范化定义、形式化表达、语义化关联和精准化推理是目前地学知识抽取及挖掘的前沿... 详细信息

地质图件及其附属资源(如描述文本)作为地质知识的重要存储媒介,蕴含着大量的地学知识及专家经验知识及隐式知识。快速精准地对多模态地质数据进行知识规范化定义、形式化表达、语义化关联和精准化推理是目前地学知识抽取及挖掘的前沿。当前,海量的地学数据中图件及附属描述信息知识分布零散,图件中的信息无法有效地与描述信息进行关联并提供知识服务。笔者等以多模态地质数据为数据源,建立了多源数据驱动下的地质图知识表达模型,通过地质基础知识、规则知识及决策知识来表达图件中蕴含的显隐式地质知识,从语义、空间、属性3个维度表示地质对象间的关系;并基于领域知识针对地质图件及描述信息开展了地质实体及关系的精准抽取及存储研究,最后以江西省于都县银坑幅G50E011007图幅1∶50000矿产地质调查矢量数据为例进行了验证与分析,实验结果验证了笔者等所提出的知识表达模型及知识抽取方法,能够较好地解决地质知识建模中图文关联弱、知识挖掘不充分等问题。

关键词：地质知识模型地质图件知识图谱决策知识智能问答

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

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地球科学学刊:英文版 2023年第5期34卷 1374-1389页

作者： qinjun qiu Bin Wang Kai Ma Hairong Lü Liufeng Tao Zhong Xie Collaborative Innovation Center of Geo-Information Technology for Smart Central Plains Zhengzhou 450046China Key Laboratory of Spatiotemporal Perception and Intelligent Processing Ministry of Natural ResourcesZhengzhou 450046China Key Laboratory of Geological Survey and Evaluation of Ministry of Education China University of GeosciencesWuhan 430074China Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges UniversityYichang 443002China College of Computer and Information Technology China Three Gorges UniversityYichang 443002China Department of Automation Tsinghua UniversityBeijing 100084China

Open data initiatives have promoted governmental agencies and scientific organizations to publish data online for *** of geoscience focuses on processing georeferenced quantitative data(e.g.,rock parameters,geochemical tests,geophysical surveys and satellite imagery)for discovering new *** knowledge is the cognitive result of human knowledge of the spatial distribution,evolution and interaction patterns of geological objects or *** graphs(KGs)can formalize unstructured knowledge into structured form and have been used in supporting decision-making *** this paper,we propose a novel framework that can extract the geological knowledge graph(GKG)from public reports relating to a modelling *** 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 conceptsgeological entities-geological relations”to describe semantic units of geological knowledge and their logic ***,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 *** research in this paper can provide some implications for the formalization and structured representation of geological knowledge graphs.

关键词： mineral resource report geological knowledge knowledge graph ontology hierarchical knowledge representation model

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Ontology-Based BERT Model for Automated Information Extraction from Geological Hazard Reports

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地球科学学刊:英文版 2023年第5期34卷 1390-1405页

作者： Kai Ma Miao Tian Yongjian Tan qinjun qiu Zhong Xie Rong Huang Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges UniversityYichang 443002China College of Computer and Information Technology China Three Gorges UniversityYichang 443002China Hubei Engineering Technology Research Center for Farmland Environment Monitoring China Three Gorges UniversityYichang 443002China School of Computer Science China University of GeosciencesWuhan 430074China Key Laboratory of Urban Land Resources Monitoring and Simulation Ministry of Natural ResourcesShenzhen 518034China Beijing Key Laboratory of Urban Spatial Information Engineering Beijing 100045China College of Economics and Management China Three Gorges UniversityYichang 443002China

Geological knowledge can provide support for knowledge discovery, knowledge inference and mineralization predictions of geological big data. Entity identification and relationship extraction from geological data description text are the key links for constructing knowledge graphs. Given the lack of publicly annotated datasets in the geology domain, this paper illustrates the construction process of geological entity datasets, defines the types of entities and interconceptual relationships by using the geological entity concept system, and completes the construction of the geological corpus. To address the shortcomings of existing language models(such as Word2vec and Glove) that cannot solve polysemous words and have a poor ability to fuse contexts, we propose a geological named entity recognition and relationship extraction model jointly with Bidirectional Encoder Representation from Transformers(BERT) pretrained language model. To effectively represent the text features, we construct a BERT-bidirectional gated recurrent unit network(BiGRU)-conditional random field(CRF)-based architecture to extract the named entities and the BERT-BiGRU-Attention-based architecture to extract the entity relations. The results show that the F1-score of the BERT-BiGRU-CRF named entity recognition model is 0.91 and the F1-score of the BERT-BiGRU-Attention relationship extraction model is 0.84, which are significant performance improvements when compared to classic language models(e.g., word2vec and Embedding from Language Models(ELMo)).

关键词： ontology BERT model name entity recognition relation extraction knowledge graph

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基于XLNet的多数据源中文地名匹配方法

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地理空间信息 2024年第8期22卷 59-63,88页

作者：郑诗语邱芹军谢忠陶留锋李伟杰中国地质大学(武汉)计算机学院湖北武汉430074 地理信息系统国家地方联合工程实验室湖北武汉430074 自然资源部城市国土资源监测与仿真重点实验室广东深圳518000

地址作为社会发展中重要的基础性数据资源,已成为城市地理空间数据化建设的重要组成部分。地名匹配旨在比较表示相同真实世界位置的配对字符串。当前地名匹配方法依赖于字符串相似性独立或多种混合相似性度量方法,这些方法无法有效地捕... 详细信息

地址作为社会发展中重要的基础性数据资源,已成为城市地理空间数据化建设的重要组成部分。地名匹配旨在比较表示相同真实世界位置的配对字符串。当前地名匹配方法依赖于字符串相似性独立或多种混合相似性度量方法,这些方法无法有效地捕捉长句子上下文信息,不能充分理解地址含义。因此,提出一种基于XLNet算法的地名匹配方法,利用深度神经网络将一对地名分类为匹配或不匹配。该方法利用长程记忆并使用双信息流注意力掩码对事件序列进行重构,以利用其双向信息建立表征。实验结果表明,该方法可解决长地址匹配问题,模型能较好地理解上下文语义信息,优于先前研究的单个相似度量及基于监督机器学习的方法。

关键词：地名匹配地名实体 XLNet Softmax 回归模型

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An adaptive representation model for geoscience knowledge graphs considering complex spatiotemporal features and relationships

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Science China Earth Sciences 2023年第11期66卷 2563-2578页

作者： Yunqiang ZHU Kai SUN Shu WANG Chenghu ZHOU Feng LU Hairong LV qinjun qiu Xinbing WANG Yanmin QI State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing 210023China Department of Automation Tsinghua UniversityBeijing 100084China School of Computer Science China University of GeosciencesWuhan 430074China Department of Electronic Engineering Shanghai Jiao Tong UniversityShanghai 200240China School of Computer Science University of Nottingham Ningbo ChinaNingbo 315100China

Geoscience knowledge graph(GKG)can organize various geoscience knowledge into a machine understandable and computable semantic network and is an effective way to organize geoscience knowledge and provide knowledge-related *** a result,it has gained significant attention and become a frontier in *** knowledge is derived from many disciplines and has complex spatiotemporal features and relationships of multiple scales,granularities,and ***,establishing a GKG representation model conforming to the characteristics of geoscience knowledge is the basis and premise for the construction and application of ***,existing knowledge graph representation models leverage fixed tuples that are limited in fully representing complex spatiotemporal features and *** address this issue,this paper first systematically analyzes the categorization and spatiotemporal features and relationships of geoscience *** this basis,an adaptive representation model for GKG is proposed by considering the complex spatiotemporal features and *** the constraint of a unified spatiotemporal ontology,this model adopts different tuples to adaptively represent different types of geoscience knowledge according to their spatiotemporal *** model can efficiently represent geoscience knowledge,thereby avoiding the isolation of the spatiotemporal feature representation and improving the accuracy and efficiency of geoscience knowledge *** can further enable the alignment,transformation,computation,and reasoning of spatiotemporal information through a spatiotemporal ontology.

关键词： Geoscience Knowledge graph Representation model Spatiotemporal features Spatiotemporal relationships

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Knowledge Graph for Identifying Geological Disasters by Integrating Computer Vision with Ontology

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地球科学学刊:英文版 2023年第5期34卷 1418-1432页

作者： qinjun qiu Zhong Xie Die Zhang Kai Ma Liufeng Tao Yongjian Tan Zhipeng Zhang Baode Jiang School of Computer Science China University of GeosciencesWuhan 430078China Key Laboratory of Geological Survey and Evaluation of Ministry of Education China University of GeosciencesWuhan 430074China National Engineering Research Center of Geographic Information System Wuhan 430074China School of Resource and Environmental Sciences Wuhan UniversityWuhan 430074China College of Computer and Information Technology China Three Gorges UniversityYichang 443002China Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges UniversityYichang 443002China

The occurrence of geological disasters can have a large impact on urban safety. Protecting people’s safety is the most important concern when disasters occur. Safety improvement requires a large amount of comprehensive and representative risk analysis and a large collection of information related to geological hazards, including unstructured knowledge and experience. To address the relevant information and support safety risk analysis, a geological hazard knowledge graph is developed automatically based on computer vision and domain-geoscience ontology to identify geological hazards from input images while obeying safety rules and regulations, even when affected by changes. In the implementation of the knowledge graph, we design an ontology schema of geological disasters based on a top-down approach, and by organizing knowledge as a logical semantic expression, it can be shared using ontology technologies and therefore enable semantic interoperability. Computer vision approaches are then used to automatically detect a set of entities and attributes, using the data from input images, and object types and their attributes are identified so that they can be stored in Neo4j for reasoning and searching. Finally, a reasoning model for geological hazard identification was developed using the Neo4j database to create nodes, relationships, and their properties for modeling, and geological hazards in the images can be automatically identified by searching the Neo4j database. An application on geological hazard is presented. The results show the effectiveness of the proposed approach in terms of identifying possible potential hazards in geological hazards and assisting in formulating targeted preventive measures.

关键词： geological hazard computer vision knowledge graph city safety ontology

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