A Text Named Entity Recognition Model for Power Knowledge Graph Construction

The accuracy of entity recognition is particularly important for knowledge graph construction. The traditional Named Entity recognition (NER) model mainly includes HMM, CRF, BiLSTM, BiLSTM-CRF, etc. It is difficult to solve the problem of word meaning confusion resulting from the wrong separation at the end of the entity when using the four models for the labeled nuclear technology knowledge data sets. In order to improve the entity recognition effect and address the problem of polysemy in nuclear technology knowledge data set, an improved nuclear technology entity recognition method based on the BERT-BiLSTM-CRF combination model was proposed by comparative experiments. According to the results, it can conclude that the application of the BERT model instead of the Word2Vec algorithm for word vector training is helpful to the model recognition, and the exclusive dictionary word segmentation and part of speech classification of nuclear technology texts contribute to improving the quality of labeled data. The experiment verified that the usage of BERT pre-training model can solve the problem of polysemy in NER to some extent. Meanwhile, it validated the precision in nuclear technology knowledge entity recognition for collected data, which could be used in pre-procedure in nuclear technology knowledge graph construction.

Tomato leaf pests and diseases pose a significant threat to the yield and quality of Q6 tomatoes, highlighting the necessity for comprehensive studies on effective control methods. Current control measures predominantly rely on experience and manual observation, hindering the integration of multi-source data. To address this, we integrated information resources related to tomato leaf pests and diseases from agricultural standards documents, knowledge websites, and relevant literature. Guided by domain experts, we preprocessed this data to construct a sample set. We utilized the Named Entity Recognition (NER) model ALBERT-BiLSTM-CRF to conduct end-to-end knowledge extraction experiments, which outperformed traditional models such as 1DCNN-CRF and BiLSTM-CRF, achieving a recall rate of 95.03%. The extracted knowledge was then stored in the Neo4j graph database, effectively visualizing the internal structure of the knowledge graph. We developed a digital diagnostic system for tomato leaf pests and diseases based on the knowledge graph, enabling graphical management and visualization of pest and disease knowledge. The constructed knowledge graph offers insights for controlling tomato leaf pests and diseases and provides new research directions for pest control in other crops.

Digging rich knowledge from clinical texts becomes a popular topic today. Knowledge graph has been widely used to integrate and manage abundant knowledge. Entity recognition and relation extraction play important roles in constructing knowledge graphs. In this paper, we develop a system to recognize entities and extract their relations from clinical texts in Electronic Medical Records. Our system implements four major functions: manual entity annotation, automatic entity recognition, manual relation annotation and automatic relation extraction. Tools of entity annotation and relation annotation are designed for professionals to help them manually annotate objects given original clinical texts. Moreover, entity recognition and relation recognition, which CRF and CNN are applied in, are accessible for professionals before manual annotation in order to increase the efficiency. Our system has been used in several applications, such as medical knowledge graph construction and health QA system.

The rapid advancement of artificial intelligence and intelligent algorithms has led to the widespread adoption of natural language processing technology. Named entity recognition (NER), an important task in this field, takes a pivotal part in information extraction, machine translation, and knowledge graph construction. However, for a small language such as Lao, traditional methods have obvious deficiencies in recognition effectiveness due to its complex syllable structure, fuzzy word boundaries and scarce corpus resources. To address this problem, the study designs a named entity recognition model for Lao that incorporates a Bi-directional Long Short-Term Memory (BiLSTM) network, an attention mechanism and a conditional random field. The model first vectorizes multi-granularity features such as characters, syllables and lexemes through an embedding layer, then captures contextual dependencies by a BiLSTM network, introduces an Attention to highlight key information, and finally establishes a global label transfer relation through a conditional random field to ensure the consistency of the predicted sequence. The findings denote that the model achieves 91.5%, 91.0%, 90.7%, and 90.9% in accuracy rate, precision rate, recall rate, and F1 score, respectively, and the mean absolute error and mean square error are reduced to 0.062 and 0.011, which are significantly better than the comparison model. The results show that combining multi-level feature modeling and global constraint mechanism can effectively enhance the performance of named entity recognition in Lao, which provides a valuable reference and practical path for natural language processing in small languages.

<p>The traditional Chinese Named Entity Recognition (NER) method is difficult to define the entity category of the word according to the specific language environment, and the category is ambiguous, so it is difficult to accurately identify the entity. Named entity recognition based on deep learning can find entity categories in text, so it has received widespread attention. On the basis of the neural network model, this paper proposes a model based on TextCNN-BiLSTM-CRF and text classification (TextCNN-BiLSTM-TC-CRF) for Chinese NER. First, the TextCNN model is used to extract the word vector information of the text data; secondly, bidirectional LSTM is used the model extracts the contextual features of the text; then the neural network model is used to automatically extract the word features and the global features of the text for text classification; finally, the text sequence labeling and entity recognition are performed. Experiments verify that on a large-scale Chinese NER data set, the entity recognition model proposed in this paper has better evaluation indicators than other algorithms, with an F1 score of 98.7%.</p> <p> </p>

We assessed whether constructing a mathematical knowledge graph for a knowledge question-answering system or a course recommendation system, Named Entity Recognition (NER), is indispensable. The accuracy of its recognition directly affects the actual performance of these subsequent tasks. In order to improve the accuracy of mathematical knowledge entity recognition and provide effective support for subsequent functionalities, this paper adopts the latest pre-trained language model, LERT, combined with a Bidirectional Gated Recurrent Unit (BiGRU), Iterated Dilated Convolutional Neural Networks (IDCNNs), and Conditional Random Fields (CRFs), to construct the LERT-BiGRU-IDCNN-CRF model. First, LERT provides context-related word vectors, and then the BiGRU captures both long-distance and short-distance information, the IDCNN retrieves local information, and finally the CRF is decoded to output the corresponding labels. Experimental results show that the accuracy of this model when recognizing mathematical concepts and theorem entities is 97.22%, the recall score is 97.47%, and the F1 score is 97.34%. This model can accurately recognize the required entities, and, through comparison, this method outperforms the current state-of-the-art entity recognition models.

Background Chinese patent medicines are increasingly used clinically, and the prescription drug monitoring program is an effective tool to promote drug safety and maintain health. Methods We constructed a prescription drug monitoring program for Chinese patent medicines based on knowledge graphs. First, we extracted the key information of Chinese patent medicines, diseases, and symptoms from the domain-specific corpus by the information extraction. Second, based on the extracted entities and relationships, a knowledge graph was constructed to form a rule base for the monitoring of data. Then, the named entity recognition model extracted the key information from the electronic medical record to be monitored and matched the knowledge graph to realize the monitoring of the Chinese patent medicines in the prescription. Results Named entity recognition based on the pretrained model achieved an F1 value of 83.3% on the Chinese patent medicines dataset. On the basis of entity recognition technology and knowledge graph, we implemented a prescription drug monitoring program for Chinese patent medicines. The accuracy rate of combined medication monitoring of three or more drugs of the program increased from 68% to 86.4%. The accuracy rate of drug control monitoring increased from 70% to 97%. The response time for conflicting prescriptions with two drugs was shortened from 1.3S to 0.8S. The response time for conflicting prescriptions with three or more drugs was shortened from 5.2S to 1.4S. Conclusions The program constructed in this study can respond quickly and improve the efficiency of monitoring prescriptions. It is of great significance to ensure the safety of patients' medication.

Traffic accident case named entity recognition, which helps mine key information in traffic accident texts, plays a vital role in downstream tasks such as the construction of knowledge graphs in road traffic and intelligent policing. In this paper, we construct a named entity recognition model based on the EDE (Entity Data Enhancement)-ERNIE-Bidirectional Gated Recurrent Unit Network (BiGRU)-Conditional Random Field (CRF) to address the current situation of low traffic accident case data and poor recognition of long-text entities. First, the amount of accident case data is enhanced using the entity random substitution method. Next, the text data of traffic accident cases are characterized as a dynamic word vector using the ERNIE pretraining model. Then, the BiGRU network learns the long-distance dependency relationship in the text to enhance the effect of the model on long-text entity recognition. Finally, the result sequence is constrained by the CRF layer to realize the named entity recognition model. The experimental part uses data related to real traffic accident cases in a domestic area. The data enhancement method increases the data volume three times compared to the original data volume. Experimental results show that the EDE-ERNIE-BiGRU-CRF model achieves better F1 values, recall and precision achieved better performance than the entity recognition methods of BERT-BiGRU-CRF, ERNIE-BiGRU-CRF, ERNIE-BiLSTM-CRF, ERNIE-CRF, ERNIE, BiGRU-CRF, ROBERTA-wwm-ext-BiGRU-CRF and verify its effectiveness for entity recognition in traffic accident cases.

Background Clinical named entity recognition is the basic task of mining electronic medical records text, which are with some challenges containing the language features of Chinese electronic medical records text with many compound entities, serious missing sentence components, and unclear entity boundary. Moreover, the corpus of Chinese electronic medical records is difficult to obtain. Methods Aiming at these characteristics of Chinese electronic medical records, this study proposed a Chinese clinical entity recognition model based on deep learning pretraining. The model used word embedding from domain corpus and fine-tuning of entity recognition model pretrained by relevant corpus. Then BiLSTM and Transformer are, respectively, used as feature extractors to identify four types of clinical entities including diseases, symptoms, drugs, and operations from the text of Chinese electronic medical records. Results 75.06% Macro-P, 76.40% Macro-R, and 75.72% Macro-F1 aiming at test dataset could be achieved. These experiments show that the Chinese clinical entity recognition model based on deep learning pretraining can effectively improve the recognition effect. Conclusions These experiments show that the proposed Chinese clinical entity recognition model based on deep learning pretraining can effectively improve the recognition performance.

Extracting fine-grained event ontology knowledge based on supply chain management (SCM) related corpus and constructing knowledge graph (KG) has important guiding significance and knowledge support for the efficient implementation and development of SCM in manufacturing enterprises. Recently, research on the KG of SCM has not gained sufficient attention. This paper aims to propose an event logical KG construction approach for SCM. Specifically, a stacked BiLSTM entity recognition model based on the binocular attention mechanism is proposed, called the SBBAN model. Firstly, the character feature attention mechanism is used to infer the key information that contributes greatly to entity recognition in the text sequence. Character weighted features and character features splicing are used as new character input features. Then the deep semantic abstract features of text sequence are obtained by stacked BiLSTM. In addition, a self-attention mechanism is added to obtain the deep context relevant features. Experimental results show that the model shows better performance in in comparison with the state-of-the-art algorithms to complete the matching of event argument entities and offer knowledge support for SCM.

Recent advances in deep neural networks (DNNs) have enabled us to achieve reliable named entity recognition (NER) models without handcrafting features. However, these are also some obstacles imposed by using those machine learning methods, in need of a large amount of manually labeled data. To avoid such limitations, we could replace human annotation with distant supervision, however there remain a technical challenge on the error label issue caused by ignoring the entities that are not included in the vocabulary, which should be addressed to achieve the effective NER model. Then, we propose a novel back-labeling approach and integrate it into a tagging scheme, especially, we apply this scheme to handle the NER task in traditional Chinese medicine (TCM) field. In addition, we discuss how to use distant supervision methods to achieve better performance of the NER model. We conduct some experiments and verify that our scheme can effectively improve the entity recognition on the basis of distant supervision.

During the operation and maintenance of power equipment, a large amount of text data is accumulated, and it is of great importance to mine valuable information and evaluate the operation status of the equipment. Among them, named entity recognition technology is a key prerequisite for downstream tasks. However, with the development of natural language processing technology, while improving the accuracy of entity recognition, the existing models are gradually unable to meet the requirements of time and equipment cost for model training in practice. In this paper, we propose a low-cost ALBERT-BiLSTM-CRF-based named entity recognition model applicable to power equipment defective text. The model achieves an F1 score of 92.47% in entity recognition in the power domain, outperforming the benchmark BERT model performance in terms of time cost and effect.

BDBRC: A Chinese military entity recognition model combining context contribution and residual dilatation convolutional networks

Bridge maintenance decision-making is challenged by the “data-rich but knowledge-poor” nature of unstructured inspection and maintenance reports. A bridge maintenance knowledge graph (BMKG) construction framework is proposed, developed from a corpus of 275 inspection reports, to enable structured representation of engineering knowledge and decision support. A standards-aligned domain ontology provides semantic constraints for downstream information extraction and organization. Building on this ontology, a RoBERTa–BiGRU–CRF named entity recognition (NER) model is developed, achieving a precision of 90.8%, recall of 93.8%, and a micro-averaged F1-score (micro-F1) of 92.3%. Inter-annotator agreement for the NER annotations was quantified using Cohen’s kappa, yielding κ = 0.86. To avoid the cost of large-scale relation annotation, relations are constructed using interpretable, rule-based constraints. Through manual verification audit of randomly sampled relationship instances under a strict exact-match criterion (i.e., requiring exact matches for entity boundaries, entity types, and relationship types), an overall manual verification rate of 93.67% was obtained. Unlike existing KG methods that rely heavily on annotated data, the BMKG framework integrates ontological constraints with a rule-driven approach, prioritizing interpretability and reducing dependency on large-scale relation labeling. Consequently, the resulting knowledge graph supports semantic retrieval and visual exploration, enabling efficient disease-to-recommendation queries for refined bridge maintenance management.

Knowledge graph, as a semantic network, provides a method to model knowledge from the perspective of entities and their relationships. Since a large quantity of material knowledge is widely distributed, using the structure of the knowledge graph is an effective approach to clarify the internal connections between related knowledge. Named entity recognition is a crucial step to construct a knowledge graph. However, most of the professional domain datasets are label-free and collected from multiple heterogeneous sources. To identify the entities from these datasets, a rule-based and deep learning hybrid method is proposed. The knowledge extracted by regular expression was used to semi-automatically label the corpus, and then trained by deep learning model, which achieved good results. The constructed knowledge graph regarding to the commonly used steels is of great significance to the integration and management of related knowledge, and it lays a foundation for future engineering application.KeywordsCommonly used steelKnowledge graphSemi-automatic labelingNamed entity recognition