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

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.

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.

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.

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.

Since the causes of aviation accidents and risks are complicated, concealed, unpredictable and difficult to be investigated, in order to achieve the efficient organization and knowledge sharing of the historical cases of aviation risk events, this paper put forward the method of constructing vertical knowledge graph for aviation risk field. Firstly, the data-driven incremental construction technology is used to build aviation risk event ontology model. Secondly, the pattern-based knowledge mapping mechanism, which transform structured data into RDF (Resource Description Framework) data for storage, is proposed. And then the application, update and maintenance of the knowledge graph are described. Finally, knowledge graph construction system in aviation risk field is developed; and the data from American Aviation Safety Reporting System (ASRS) is used as an example to verify the rationality and validity of the knowledge graph construction method. Practice has proved that the construction of knowledge graph has a guiding significance for the case information organization and sharing on the field of aviation risk.

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.

Virtual geographic scenes can significantly facilitate the visualization and comprehension of the real world. However, the multitude of object types and complex relationships they contain make it challenging to display embedded geographic knowledge. Knowledge graphs help organize object entities and their relationships within scenes, which makes it easier to store and express geographic knowledge. Current methods for constructing knowledge graphs for virtual geographic scenes suffer from issues such as weak automation, low dimensionality, and poor cognitive efficiency. Thus, this paper presents a method for automatically constructing knowledge graphs by integrating intelligent systems. The method involves designing a multiagent system to extract entities and relationships hidden in virtual geographic scenes. In addition, a three-dimensional visualization approach is proposed for knowledge graphs constrained by spatiotemporal relationships. A virtual scene containing 2,183 objects was selected as the experimental area for constructing and displaying the knowledge graph. The results demonstrate that the method constructs a knowledge graph with 12,958 relationships within 300 s in common network and hardware settings. In addition, a knowledge graph offers better completeness at small scales compared to other geographic knowledge graphs, and it can also enhance the cognitive efficiency of understanding scene relationships.

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.

In order to effectively organize and manage the text knowledge of forest diseases, this paper proposes a knowledge graph construction method based on deep learning, which uses the knowledge graph to describe the complex relationship between entities in a structured form. Firstly, the general method of constructing forest disease knowledge graph is described. Secondly, the characteristics of forest disease text are analyzed, preprocessed and labeled. And the data set is constructed. Then, for the unstructured text in the data set, the innovative deep learning method of Bi-gated Recurrent Unit (BiGRU) and Conditional Random Field(CRF) model is used to identify the named entities of forest disease names and therapeutic agents. Experiments on forest disease attribute text corpus show that the precison is 93.15%, which can be effectively applied to the named entity recognition task of forest disease text. Finally, the extracted knowledge is stored in the neo4j map database to intuitively reflect the internal structure of the knowledge graph and realize the visualization and reasoning based on forest disease knowledge.

An educational knowledge graph provides students and teachers with detailed knowledge organization and a clear concept structure by extracting knowledge points and relationships from textbooks. A high-fidelity knowledge graph is essential for precise teaching and personalized learning. However, as an important step in knowledge graph construction, coreference resolution is often ignored or left to the end. This neglect leads to a loss in the high fidelity of the knowledge graph and may also cause clearly underestimated focuses and fewer associations between knowledge points, although the ratio of the pronouns to the entire corpus is very small (less than 5‰). In this paper, a rule and semantic-based method is proposed to resolve coreference in the knowledge graph constructed from a biology textbook. Compared with the other three algorithms, it has a better precision ratio and recall ratio. By comparing the two knowledge graphs constructed before and after coreference resolution, it can be seen that the focus has changed significantly to better align with the text, and the associations between the knowledge points are more consistent with intuition. This outcome suggests that coreference resolution improves the high fidelity of an educational 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.

Facing the network security problem of power monitoring system, this paper studies the knowledge graph-based network security knowledge extraction and management method. In the entity recognition module, an entity recognition algorithm based on the idea of multi task learning is adopted, which decomposes the entity recognition process into entity boundary perception and entity classification to reduce the impact of error accumulation. Meanwhile, the introduction of comprehensive description information of entity categories into entity classification tasks can not only be widely used in general fields, but also be targeted to specific scenes in the field of power monitoring system. In the relation extraction module, based on the idea of multi-instance learning, the method of piecewise convolution neural network is employed, which reduces the impact of some instance annotation errors. The relation extraction method can extract text feature expression and inter entity structure information, which has a good effect on specialized field knowledge. Finally, based on the established knowledge graph, this paper studies the application problems of network security knowledge management and intelligent question answering of power monitoring system, which shows the effectiveness of knowledge graph technology in the field of network security management.

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.

As a new generation technology representative of knowledge engineering, knowledge graph is a large-scale semantic network, which has natural advantages in extracting knowledge from multi-source heterogeneous information, complex and highly correlated knowledge representation and reasoning. Based on the analysis of the application status of the grid day-ahead scheduling optimization decision-making technology and the demand for improving the intelligent processing capability, the key technologies and methods for the application of knowledge graph in the grid day-ahead scheduling optimization decision-making are proposed. The key technologies, application ideas and process methods of knowledge graphs are analyzed from the aspects of the design of knowledge ontology framework in the field of day-to-day dispatching of power grids, high-quality phrase mining and entity recognition, relation extraction and knowledge fusion, and scene-based dynamic knowledge graph update. Finally, the application and challenges of knowledge graph in the field of grid day-ahead scheduling in the future are summarized and prospected.