An Information Extraction Method for Sedimentology Literature with Semantic Rules

Hu, Zhichen; Hu, Xiangben; Qi, Lianyong; Xue, She‐Sheng; Xu, Xiaolong

doi:10.1109/dasc-picom-cbdcom-cyberscitech52372.2021.00085

Cited by 2 publications

(2 citation statements)

References 23 publications

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“…Knowledge graphs, as a form of structured semantic knowledge repository, are designed to store information about entities (such as individuals, locations, organizations, etc.) and their relations in a graphical format from various forms of data [92][93][94]. This approach significantly simplifies the process of knowledge comprehension and retrieval for both machines and humans.…”

Section: Application Of Knowledge Graphs In Intelligent Auditmentioning

confidence: 99%

From data to insights: the application and challenges of knowledge graphs in intelligent audit

Zhong,

Yang,

Shi

et al. 2024

J Cloud Comp

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In recent years, knowledge graph technology has been widely applied in various fields such as intelligent auditing, urban transportation planning, legal research, and financial analysis. In traditional auditing methods, there are inefficiencies in data integration and analysis, making it difficult to achieve deep correlation analysis and risk identification among data. Additionally, decision support systems in the auditing process may face issues of insufficient information interpretability and limited predictive capability, thus affecting the quality of auditing and the scientificity of decision-making. However, knowledge graphs, by constructing rich networks of entity relationships, provide deep knowledge support for areas such as intelligent search, recommendation systems, and semantic understanding, significantly improving the accuracy and efficiency of information processing. This presents new opportunities to address the challenges of traditional auditing techniques. In this paper, we investigate the integration of intelligent auditing and knowledge graphs, focusing on the application of knowledge graph technology in auditing work for power engineering projects. We particularly emphasize mainstream key technologies of knowledge graphs, such as data extraction, knowledge fusion, and knowledge graph reasoning. We also introduce the application of knowledge graph technology in intelligent auditing, such as improving auditing efficiency and identifying auditing risks. Furthermore, considering the environment of cloud-edge collaboration to reduce computing latency, knowledge graphs can also play an important role in intelligent auditing. By integrating knowledge graph technology with cloud-edge collaboration, distributed computing and data processing can be achieved, reducing computing latency and improving the response speed and efficiency of intelligent auditing systems. Finally, we summarize the current research status, outlining the challenges faced by knowledge graph technology in the field of intelligent auditing, such as scalability and security. At the same time, we elaborate on the future development trends and opportunities of knowledge graphs in intelligent auditing.

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Section: Application Of Knowledge Graphs In Intelligent Auditmentioning

confidence: 99%

From data to insights: the application and challenges of knowledge graphs in intelligent audit

Zhong,

Yang,

Shi

et al. 2024

J Cloud Comp

View full text Add to dashboard Cite

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“…Wang also added an attention mechanism to BiLSTM-CRF for named entity recognition in the Chinese hypertension treatment literature and obtained an F1_score of 86.2% [24]. Hu et al [25] combined BiLSTM-CRF in the field of sedimentology to identify specific sentence components to achieve sedimentological information extraction. Zhang et al [26] proposed BiLSTM-CRF-based cross-domain migration to improve Chinese clinical named entity recognition for example (e.g., disease, symptom, drug, anatomy), and obtained an F1_score of 85.43% in comparison with other models.…”

Section: Deep Learning-based Nermentioning

confidence: 99%

Data Masking for Chinese Electronic Medical Records with Named Entity Recognition

He¹,

Xu²,

Hu³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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With the rapid development of information technology, the electronification of medical records has gradually become a trend. In China, the population base is huge and the supporting medical institutions are numerous, so this reality drives the conversion of paper medical records to electronic medical records. Electronic medical records are the basis for establishing a smart hospital and an important guarantee for achieving medical intelligence, and the massive amount of electronic medical record data is also an important data set for conducting research in the medical field. However, electronic medical records contain a large amount of private patient information, which must be desensitized before they are used as open resources. Therefore, to solve the above problems, data masking for Chinese electronic medical records with named entity recognition is proposed in this paper. Firstly, the text is vectorized to satisfy the required format of the model input. Secondly, since the input sentences may have a long or short length and the relationship between sentences in context is not negligible. To this end, a neural network model for named entity recognition based on bidirectional long shortterm memory (BiLSTM) with conditional random fields (CRF) is constructed. Finally, the data masking operation is performed based on the named entity recognition results, mainly using regular expression filtering encryption and principal component analysis (PCA) word vector compression and replacement. In addition, comparison experiments with the hidden markov model (HMM) model, LSTM-CRF model, and BiLSTM model are conducted in this paper. The experimental results show that the method used in this paper achieves 92.72% Accuracy, 92.30% Recall, and 92.51% F1_score, which has higher accuracy compared with other models.

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An Information Extraction Method for Sedimentology Literature with Semantic Rules

Cited by 2 publications

References 23 publications

From data to insights: the application and challenges of knowledge graphs in intelligent audit

From data to insights: the application and challenges of knowledge graphs in intelligent audit

Data Masking for Chinese Electronic Medical Records with Named Entity Recognition

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