地质领域实体关系抽取是构建地质知识图谱的基础,对地质领域文本信息抽取与知识库构建具有重要的作用。针对地质领域实体关系复杂、缺少人工标注语料库等特点,提出了面向地质领域实体关系联合抽取模型,着重对多地质文本中存在的复杂重...地质领域实体关系抽取是构建地质知识图谱的基础,对地质领域文本信息抽取与知识库构建具有重要的作用。针对地质领域实体关系复杂、缺少人工标注语料库等特点,提出了面向地质领域实体关系联合抽取模型,着重对多地质文本中存在的复杂重叠关系进行识别,避免传统流水线模型中由于实体识别错误造成级联误差。文章构建了高质量地质领域实体关系语料库,提出了基于预训练语言模型BERT(Bidirectional Encoder Representations from Transformers)和双向门控循环单元BiGRU(Bidirectional Gated Recurrent Units)与条件随机场CRF(Conditional Random Field)的序列标注模型,实现对实体关系的联合抽取。在构建数据集上进行了实验,结果表明,本文提出的联合抽取模型在实体关系抽取上的F1值达到0.671,验证了本文模型在地质实体关系抽取的有效性。展开更多
Open data initiatives have promoted governmental agencies and scientific organizations to publish data online for reuse.Research of geoscience focuses on processing georeferenced quantitative data(e.g.,rock parameters...Open data initiatives have promoted governmental agencies and scientific organizations to publish data online for reuse.Research of geoscience focuses on processing georeferenced quantitative data(e.g.,rock parameters,geochemical tests,geophysical surveys and satellite imagery)for discovering new knowledge.Geological knowledge is the cognitive result of human knowledge of the spatial distribution,evolution and interaction patterns of geological objects or processes.Knowledge graphs(KGs)can formalize unstructured knowledge into structured form and have been used in supporting decision-making recently.In this paper,we propose a novel framework that can extract the geological knowledge graph(GKG)from public reports relating to a modelling study.Based 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 relations.Finally,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 representation.The research in this paper can provide some implications for the formalization and structured representation of geological knowledge graphs.展开更多
Mineral prospectivity mapping(MPM)is designed to reduce the exploration search space by combining and analyzing geological prospecting big data.Such geological big data are too large and complex for humans to effectiv...Mineral prospectivity mapping(MPM)is designed to reduce the exploration search space by combining and analyzing geological prospecting big data.Such geological big data are too large and complex for humans to effectively handle and interpret.Artificial intelligence(AI)algorithms,which are powerful tools for mining nonlinear mineralization patterns in big data obtained from mineral exploration,have demonstrated excellent performance in MPM.However,AI-driven MPM faces several challenges,including difficult interpretability,poor generalizability,and physical inconsistencies.In this study,based on previous studies,we devised a novel workflow that aims to constructing more transparent and explainable artificial intelligence(XAI)models for MPM by embedding domain knowledge throughout the AI-driven MPM,from input data to model design and model output.This newly proposed approach provides strong geological and conceptual leads that guide the entire AI-driven MPM model training process,thereby improving model interpretability and performance.Overall,the development of XAI models for MPM is capable of embedding prior and expert knowledge throughout the modeling process,presenting a valuable and promising area for future research designed to improve MPM.展开更多
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 comprehensi...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.展开更多
文摘地质领域实体关系抽取是构建地质知识图谱的基础,对地质领域文本信息抽取与知识库构建具有重要的作用。针对地质领域实体关系复杂、缺少人工标注语料库等特点,提出了面向地质领域实体关系联合抽取模型,着重对多地质文本中存在的复杂重叠关系进行识别,避免传统流水线模型中由于实体识别错误造成级联误差。文章构建了高质量地质领域实体关系语料库,提出了基于预训练语言模型BERT(Bidirectional Encoder Representations from Transformers)和双向门控循环单元BiGRU(Bidirectional Gated Recurrent Units)与条件随机场CRF(Conditional Random Field)的序列标注模型,实现对实体关系的联合抽取。在构建数据集上进行了实验,结果表明,本文提出的联合抽取模型在实体关系抽取上的F1值达到0.671,验证了本文模型在地质实体关系抽取的有效性。
基金the IUGS Deep-time Digital Earth(DDE)Big Science Programfinancially supported by the National Key R&D Program of China(No.2022YFF0711601)+4 种基金the Natural Science Foundation of Hubei Province of China(No.2022CFB640)the Opening Fund of Hubei Key Laboratory of Intelligent Vision-Based Monitoring for Hydroelectric Engineering(No.2022SDSJ04)the Opening Fund of Key Laboratory of Geological Survey and Evaluation of Ministry of Education(No.GLAB 2023ZR01)the Fundamental Research Funds for the Central UniversitiesFunded by Joint Fund of Collaborative Innovation Center of Geo-Information Technology for Smart Central Plains,Henan Province and Key Laboratory of Spatiotemporal Perception and Intelligent processing,Ministry of Natural Resources(No.212205)。
文摘Open data initiatives have promoted governmental agencies and scientific organizations to publish data online for reuse.Research of geoscience focuses on processing georeferenced quantitative data(e.g.,rock parameters,geochemical tests,geophysical surveys and satellite imagery)for discovering new knowledge.Geological knowledge is the cognitive result of human knowledge of the spatial distribution,evolution and interaction patterns of geological objects or processes.Knowledge graphs(KGs)can formalize unstructured knowledge into structured form and have been used in supporting decision-making recently.In this paper,we propose a novel framework that can extract the geological knowledge graph(GKG)from public reports relating to a modelling study.Based 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 relations.Finally,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 representation.The research in this paper can provide some implications for the formalization and structured representation of geological knowledge graphs.
基金supported by the National Natural Science Foundation of China(Grant Nos.42321001,42172326)the Natural Science Foundation of Hubei Province(China)(Grant No.2023AFA001)。
文摘Mineral prospectivity mapping(MPM)is designed to reduce the exploration search space by combining and analyzing geological prospecting big data.Such geological big data are too large and complex for humans to effectively handle and interpret.Artificial intelligence(AI)algorithms,which are powerful tools for mining nonlinear mineralization patterns in big data obtained from mineral exploration,have demonstrated excellent performance in MPM.However,AI-driven MPM faces several challenges,including difficult interpretability,poor generalizability,and physical inconsistencies.In this study,based on previous studies,we devised a novel workflow that aims to constructing more transparent and explainable artificial intelligence(XAI)models for MPM by embedding domain knowledge throughout the AI-driven MPM,from input data to model design and model output.This newly proposed approach provides strong geological and conceptual leads that guide the entire AI-driven MPM model training process,thereby improving model interpretability and performance.Overall,the development of XAI models for MPM is capable of embedding prior and expert knowledge throughout the modeling process,presenting a valuable and promising area for future research designed to improve MPM.
基金the IUGS Deep-time Digital Earth (DDE) Big Science Programfinancially supported by the National Key R & D Program of China (No.2022YFF0711601)+3 种基金the Natural Science Foundation of Hubei Province of China (No.2022CFB640)the Opening Fund of Hubei Key Laboratory of Intelligent Vision-Based Monitoring for Hydroelectric Engineering (No.2022SDSJ04)the Opening Fund of Key Laboratory of Geological Survey and Evaluation of Ministry of Education (No.GLAB 2023ZR01)the Fundamental Research Funds for the Central Universities。
文摘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.
