Improve on Entity Recognition Method Based on BiLSTM-CRF Model for the Nuclear Technology Knowledge Graph

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.

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

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.

<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>

In order to extract named entities from power equipment text, an entity recognition model based on character pair link is proposed in this paper. This model decodes links based on characters and incorporates lexical information to improve the model prediction effect. In order to extract entities from the text of power equipment, the original corpus is constructed by power equipment-related texts and actual fault cases, and the entity recognition data set of power equipment is constructed based on entity annotation. Finally, the entity recognition model is used to conduct experiments on the data set. The experimental results show that the proposed model improves the entity recognition rate by 29.2% and 13.9%, respectively, compared with the existing algorithms, and thus improves the construction efficiency of knowledge graph.

MKG-GC: A multi-task learning-based knowledge graph construction framework with personalized application to gastric cancer

In the rapidly evolving field of cybersecurity, the integration of multi-source, heterogeneous, and fragmented data into a coherent knowledge graph has garnered considerable attention. Such a graph elucidates semantic interconnections, thereby facilitating sophisticated analytical decision support. Central to the construction of a cybersecurity knowledge graph is Named Entity Recognition (NER), a critical technology that converts unstructured text into structured data. The efficacy of NER is pivotal, as it directly influences the integrity of the knowledge graph. The task of NER in cybersecurity, particularly within the Chinese linguistic context, presents distinct challenges. Chinese text lacks explicit space delimiters and features complex contextual dependencies, exacerbating the difficulty in discerning and categorizing named entities. These linguistic characteristics contribute to errors in word segmentation and semantic ambiguities, impeding NER accuracy. This paper introduces a novel NER methodology tailored for the Chinese cybersecurity corpus, termed CSBERT-IDCNN-BiLSTM-CRF. This approach harnesses Iterative Dilated Convolutional Neural Networks (IDCNN) for extracting local features, and Bi-directional Long Short-Term Memory networks (BiLSTM) for contextual understanding. It incorporates CSBERT, a pre-trained model adept at processing few-shot data, to derive input feature representations. The process culminates with Conditional Random Fields (CRF) for precise sequence labeling. To compensate for the scarcity of publicly accessible Chinese cybersecurity datasets, this paper synthesizes a bespoke dataset, authenticated by data from the China National Vulnerability Database, processed via the YEDDA annotation tool. Empirical analysis affirms that the proposed CSBERT-IDCNN-BiLSTM-CRF model surpasses existing Chinese NER frameworks, with an F1-score of 87.30% and a precision rate of 85.89%. This marks a significant advancement in the accurate identification of cybersecurity entities in Chinese text, reflecting the model’s robust capability to address the unique challenges presented by the language’s structural intricacies.

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.

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.

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.

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.

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.

With the rapid development of intelligent coal mine, the field of coal mine also ushered in a large number of heterogeneous data information. Aiming at the data information existing in the field of coal mine, a coal mine safety knowledge atlas centered on coal mine accident cases is constructed. The construction of knowledge graph mainly uses crawler technology to obtain text data of coal mine, Albert-BilSTM-CRF model to realize entity recognition, Albert-BilSTM-ATT to complete relation extraction, and obtain triplet form in the field of coal mine safety. Finally, the obtained triplet form is imported into Neo4J graph database. Form a structured knowledge graph.

As an important infrastructure in the era of big data, the knowledge graph can integrate and manage data resources. Therefore, the construction of tourism knowledge graphs with wide coverage and of high quality in terms of information from the perspective of tourists’ needs is an effective solution to the problem of information clutter in the tourism field. This paper first analyzes the current state of domestic and international research on constructing tourism knowledge graphs and highlights the problems associated with constructing knowledge graphs, which are that they are time-consuming, laborious and have a single function. In order to make up for these shortcomings, this paper proposes a set of systematic methods to build a tourism knowledge graph. This method integrates the BiLSTM and BERT models and combines these with the attention mechanism. The steps of this methods are as follows: First, data preprocessing is carried out by word segmentation and removing stop words; second, after extracting the features and vectorization of the words, the cosine similarity method is used to classify the tourism text, with the text classification based on naive Bayes being compared through experiments; third, the popular tourism words are obtained through the popularity analysis model. This paper proposes two models to obtain popular words: One is a multi-dimensional tourism product popularity analysis model based on principal component analysis; the other is a popularity analysis model based on emotion analysis; fourth, this paper uses the BiLSTM-CRF model to identify entities and the cosine similarity method to predict the relationship between entities so as to extract high-quality tourism knowledge triplets. In order to improve the effect of entity recognition, this paper proposes entity recognition based on the BiLSTM-LPT and BiLSTM-Hanlp models. The experimental results show that the model can effectively improve the efficiency of entity recognition; finally, a high-quality tourism knowledge was imported into the Neo4j graphic database to build a tourism knowledge graph.

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.