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

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Journal of Earth Science 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

Chlorophyll Fluorescence Extraction from Water-Leaving Radiance of Algae-Containing Water Through Polarization

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Journal of Ocean University of China 2017年第6期16卷 1003-1008页

作者： WANG Lin qiu Zhongfeng PANG Huifang LIU yongjian CHEN Yanlong JIANG Lingling Department of Ocean Remote Sensing National Marine Environmental Monitoring Center School of Marine Sciences Nanjing University of Information Science and Technology College of Environmental Science and Engineering Dalian Maritime University

When measuring reflectance spectra, it is very important to accurately extract chlorophyll fluorescence from elastic-scattering light in water-leaving radiance. The elastic scattering of light by water particles produces partially polarized light. In contrast, chlorophyll fluorescence in planktonic algae yields completely unpolarized light. These properties can be used to separate fluorescent signals from the water-leaving radiance and thus to determine chlorophyll concentration. The algal species Aureococcus anophagefferens was used to conduct a laboratory polarization experiment. For the tests, we used a field spectroradiometer and a polarizer; measurements were collected using two different observation modes. The chlorophyll fluorescence curve extracted through polarization shows an excellent match with the results obtained using the fluorospectro photometer for both measurement modes, suggesting that polarization-based chlorophyll fluorescence extraction may be feasible. The extracted fluorescence is more reliable at incident zenith angles ranging from 30? to 60?. For algae-containing water, the results improve with increasing chlorophyll concentration. This method could help improve chlorophyll concentration measurement and the remote-sensing detection of resulting harmful algae blooms.

关键词： chlorophyll fluorescence polarization technique water-leaving radiance extraction red shift

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98卷 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 UniversityYichangHubei 443002China College of Computer and Information Technology China Three Gorges UniversityYichangHubei 443002China Key Laboratory of Geological Survey and Evaluation of Ministry of Education China University of GeosciencesWuhan 430074China School of Computer Science China University of GeosciencesWuhan 430074China Key Laboratory of Quantitative Resource Evaluation and Information Engineering Ministry of Natural ResourcesChina University of GeosciencesWuhan 430074China

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 *** 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 *** 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 *** 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 *** experimental results show that the proposed model for the constructed dataset achieves high performance and is capable of mining the rich geological information.

关键词： geological named entity recognition geological report adversarial training confrontation training global pointer pre-training model

Extracting Named Entity Using Entity Labeling in Geological Text Using Deep Learning Approach

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Journal of Earth Science 2023年第5期34卷 1406-1417页

作者： Qinjun qiu Miao Tian Zhong Xie yongjian Tan Kai Ma Qingfang Wang Shengyong Pan Liufeng Tao State Key Laboratory of Geo-Information Engineering and Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of MNR Chinese Academy of Surveying and MappingBeijing 100036China Key Laboratory of Spatial-Temporal Big Data Analysis and Application of Natural Resources in Megacities MNRShanghai 20063China 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 Chengdu Geological Environment Monitoring Station Chengdu 610036China Wuhan Zondy Cyber Science&Technology Co. Ltd.Wuhan 430074China

Artificial intelligence(AI) is the key to mining and enhancing the value of big data, and knowledge graph is one of the important cornerstones of artificial intelligence, which is the core foundation for the integration of statistical and physical representations. Named entity recognition is a fundamental research task for building knowledge graphs, which needs to be supported by a high-quality corpus, and currently there is a lack of high-quality named entity recognition corpus in the field of geology, especially in Chinese. In this paper, based on the conceptual structure of geological ontology and the analysis of the characteristics of geological texts, a classification system of geological named entity types is designed with the guidance and participation of geological experts, a corresponding annotation specification is formulated, an annotation tool is developed, and the first named entity recognition corpus for the geological domain is annotated based on real geological reports. The total number of words annotated was 698 512 and the number of entities was 23 345. The paper also explores the feasibility of a model pre-annotation strategy and presents a statistical analysis of the distribution of technical and term categories across genres and the consistency of corpus annotation. Based on this corpus, a Lite Bidirectional Encoder Representations from Transformers(ALBERT)-Bi-directional Long Short-Term Memory(BiLSTM)-Conditional Random Fields(CRF) and ALBERT-BiLSTM models are selected for experiments, and the results show that the F1-scores of the recognition performance of the two models reach 0.75 and 0.65 respectively, providing a corpus basis and technical support for information extraction in the field of geology.

关键词： ontology geological reports named entity recognition geological corpus construction semi-automated annotation platforms deep learning

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

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Journal of Earth Science 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

An overview of bearing voltages and currents in rail transportation traction motors

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Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2023年第3期24卷 226-242页

作者： Yao LI Lin qiu yongjian ZHI Zifan GAO Jien MA Jian ZHANG Youtong FANG College of Electrical Engineering Zhejiang UniversityHangzhou 310027China CRRC Zhuzhou Institute Co.Ltd. Zhuzhou 412001China

In modern rail transportation,inverter drive systems have been extensively used due to their excellent speed control ***,in recent years,premature failure problems caused by bearing voltage and current phenomena have been frequently reported in electric motors,with electrical bearing failures making up a considerable *** purpose of this review is to provide a comprehensive overview of facets relating to the electrical erosion of bearings in an electrical environment represented by railway ***,the origins of the phenomenon as well as typical bearing electrical failure modes are ***,we introduce the distinctive features of the electrical environment of railway traction motor bearings,including output voltages with high common-mode components and systems with complex grounding ***,we classify the fundamental mechanisms for generating bearing voltages/currents into four groups,and present their modeling processes,including equivalent circuit establishment and parameter determination ***,we summarize the strategies frequently used to protect bearings,and describe a typical solution to suppress electrical bearing failures in railway ***,we present a case example to illustrate a research procedure for systematic investigation of inverter-induced bearing currents in rail transportation.

关键词： Rail transportation Bearing current Common-mode voltage Bearing electrical erosion Grounding configuration

Analysis of composition of gut microbial community in a rat model of functional dyspepsia treated with Simo Tang(四磨汤)

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Journal of Traditional Chinese Medicine 2024年第6期44卷 1168-1176页

作者： WANG Yiying LIU Jianjun XIONG yongjian ZHANG Yongli WEN Yuqi XUE Mengli GUO Huishu qiu Juanjuan Centralab the First Affiliated Hospital of Dalian Medical UniversityDalian 116011China Clinical Laboratory of Integrative Medicine the First Affiliated Hospital of Dalian Medical UniversityDalian 116011China Department of Critical Care Medicine the First Affiliated Hospital of Dalian Medical UniversityDalian 116011China

OBJECTIVE: To investigate composition of gut microbial community in a rat model of functional dyspepsia(FD) and to explore the interventional effects of Simo Tang(四磨汤, SMT). METHODS: A rat model of FD was established through the tail-clamping stimulation method. The rat model of FD was assessed by the state of rats, their weight, sucrose preference rate, and intestinal propulsion rate. The DNA was extracted from stool samples after treatment with SMT. Amplified polymerase chain reaction(PCR) products of the 16S r DNA were sequenced using Novase Q6000 after construction of libraries. Composition of gut microbial community in the stool samples was determined and analyzed by cluster analysis, bioinformatic analysis, and analysis of α-diversity and β-***: The rat model of FD was successfully established using the tail-clamping stimulation method. The statistical results of cluster analysis of operational taxonomic units(OTUs) showed that the relative abundance of OTUs in the FD group was the lowest, while it was the highest in the normal(N) group. The composition of microbiome in the four groups was similar at phyla level. Compared with the FD group, the abundance of Firmicutes was downregulated, and the abundance of Proteobacteria and Bacteroidetes was upregulated in the Simo Tang(SMT) and high-dose Simo Tang(SMT.G) groups. The ratio of Bacteroidetes/Firmicutes was also elevated. According to the analysis of α-diversity and β-diversity, the abundance of flora in FD rats was significantly reduced. The treatment using SMT appeared beneficial to improve the diversity of flora. SMT could improve the intestinal flora in FD rats. The results showed that FD rats had intestinal flora imbalance, and species diversity increased. The results suggested that SMT could regulate the disorders of intestinal flora caused by FD. CONCLUDIONS: SMT could restore gut homeostasis and maintain gut flora diversity by modulating the gut microbiota and its associated metabolites in

关键词： gut microbial community composition functional dyspepsia gastrointestinal microbiome abundance of flora Simo Tang

Application of Probiotics in Pig Industry as a Substitute of Antibiotics

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Animal Husbandry and Feed Science 2015年第1期7卷 15-17,38页

作者： Sha Wenfeng Zhu Juan Gu yongjian Dai Hui qiu Liangwei Jiangsu Yanjiang Institute of Agricultural Sciences

Antibiotics plays a positive role in the development of livestock breeding and feed industry,but has also disadvantages impossible to overcome,so it is an inevitable trend of prohibiting application of antibiotics in *** paper reviewed concept and action mechanism of the antibiotics substitute—probiotics,and its application in piglets,growing and fattening pigs and sows.

关键词： Antibiotics Probiotics Pig industry Research application

虚拟经济与实体经济比率研究——基于金融危机的视角

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维普期刊数据库

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经济问题探索 2020年第1期 156-164页

作者：邱蓉梁永坚贵州财经大学

20世纪70年代以来,金融危机的爆发越来越频繁,对全球经济和人们的生活产生了深远的影响。金融危机爆发的一个重要原因是虚拟经济脱离了实体经济,出现过度膨胀。论文选取了特定指标,比较分析了几次典型的金融危机中虚拟经济与实体经济的... 详细信息

20世纪70年代以来,金融危机的爆发越来越频繁,对全球经济和人们的生活产生了深远的影响。金融危机爆发的一个重要原因是虚拟经济脱离了实体经济,出现过度膨胀。论文选取了特定指标,比较分析了几次典型的金融危机中虚拟经济与实体经济的比率,发现小国爆发金融危机前后虚拟经济与实体经济的比例约在0.5-4之间,大国的比例约在4-7之间。为各国监测预警金融危机提供数据参考,做到“虚拟经济不虚,实体经济更实”,助力经济健康协调发展。

关键词：虚拟经济实体经济金融危机合理比率