Knowledge Graph Technology Research Articles

Progress and prospect of digital transformation of higher education in China: Bibliometric and visualized analysis

SDG4 calls for ensuring inclusive and equitable quality education while promoting lifelong learning opportunities for everyone. To achieve this, there’s a need to advance digital education to support personalized and lifelong learning by broadening access to high-quality educational resources. Higher education models are continually evolving to seize new opportunities that facilitate the deep integration of digital technology with education. Chinese government holds high expectations for digital education, aiming to foster comprehensive individual development and contribute to the United Nations 2030 SDGs. This paper clarifies the distinction between the concepts of digital transformation in higher education and visualized analyses of academic achievements in this area. By utilizing bibliometrics and knowledge graph technology, this paper examines the general trends, evolutionary features, and frontier topics related to digital transformation in Chinese higher education. The analysis is based on relevant literature sourced from the CNKI database, covering the period from 2000 to 2023. Furthermore, this paper combines with the CIPP model to propose a comprehensive theoretical framework and practical guidance for digital transformation in higher education, addressing the aspects of context, input, process, and progress.

A Text Generation Method Based on a Multimodal Knowledge Graph for Fault Diagnosis of Consumer Electronics

As consumer electronics evolve towards greater intelligence, their automation and complexity also increase, making it difficult for users to diagnose faults when they occur. To address the problem where users, relying solely on their own knowledge, struggle to diagnose faults in consumer electronics promptly and accurately, we propose a multimodal knowledge graph-based text generation method. Our method begins by using deep learning models like the Residual Network (ResNet) and Bidirectional Encoder Representations from Transformers (BERT) to extract features from user-provided fault information, which can include images, text, audio, and even olfactory data. These multimodal features are then combined to form a comprehensive representation. The fused features are fed into a graph convolutional network (GCN) for fault inference, identifying potential fault nodes in the electronics. These fault nodes are subsequently fed into a pre-constructed knowledge graph to determine the final diagnosis. Finally, this information is processed through the Bias-term Fine-tuning (BitFit) enhanced Chinese Pre-trained Transformer (CPT) model, which generates the final fault diagnosis text for the user. The experimental results show that our proposed method achieves a 4.4% improvement over baseline methods, reaching a fault diagnosis accuracy of 98.4%. Our approach effectively leverages multimodal fault information, addressing the challenges users face in diagnosing faults through the integration of graph convolutional network and knowledge graph technologies.

SecKG2vec: A Novel Security Knowledge Graph Relational Reasoning Method based on Semantic and Structural Fusion Embedding

Knowledge graph technology is widely used in network security design, analysis, and detection. By collecting, organizing, and mining various security knowledge, it provides scientific support for security decisions. Some public Security Knowledge Repositories (SKRs) are frequently used to construct security knowledge graphs. The quality of SKRs affects the efficiency and effectiveness of security analysis. However, the current situation is that the identification of relational information among security knowledge elements is not sufficient and timely, and a large number of key relational information is missing. In view of this, we propose a security knowledge graph relational reasoning method, based on the fusion embedding of semantic correlation and structure correlation, named SecKG2vec. By SecKG2vec, the embedded vector simultaneously presents both semantic and structural characteristics, and it can exhibit better relational reasoning performance. In qualitative evaluation and quantitative experiments with baseline methods, SecKG2vec has better performance in relationship reasoning task and entity reasoning task, and potential capability of 0-shot scenario prediction.

Construction of Legal Knowledge Graph Based on Knowledge-Enhanced Large Language Models

Legal knowledge involves multidimensional heterogeneous knowledge such as legal provisions, judicial interpretations, judicial cases, and defenses, which requires extremely high relevance and accuracy of knowledge. Meanwhile, the construction of a legal knowledge reasoning system also faces challenges in obtaining, processing, and sharing multisource heterogeneous knowledge. The knowledge graph technology, which is a knowledge organization form with triples as the basic unit, is able to efficiently transform multisource heterogeneous information into a knowledge representation form close to human cognition. Taking the automated construction of the Chinese legal knowledge graph (CLKG) as a case scenario, this paper presents a joint knowledge enhancement model (JKEM), where prior knowledge is embedded into a large language model (LLM), and the LLM is fine-tuned through the prefix of the prior knowledge data. Under the condition of freezing most parameters of the LLM, this fine-tuning scheme adds continuous deep prompts as prefix tokens to the input sequences of different layers, which can significantly improve the accuracy of knowledge extraction. The results show that the knowledge extraction accuracy of the JKEM in this paper reaches 90.92%. Based on the superior performance of this model, the CLKG is further constructed, which contains 3480 knowledge triples composed of 9 entities and 2 relationships, providing strong support for an in-depth understanding of the complex relationships in the legal field.

Intelligent Fault Diagnosis for Aero-Engine Lubrication Systems Using Knowledge Graph and Deep Learning Techniques

Due to the complex structure and function of aero-engine lubrication system, fault diagnosis based on the existing health management system is not explicable and highly dependent on experts' experience. A method for constructing aeroengine lubrication system fault knowledge graph was proposed in this paper. Based on the experts' knowledge, the concept of lubrication system fault knowledge graph ontology was designed. With the help of deep learning techniques such as BiLSTM and CRF, we achieved the automatic extraction of unstructured knowledge. Next, based on the Cosine Distance and Jaccard coefficient, multi-source heterogeneous fault knowledge fusion was realized. In the end, intelligent fault knowledge question answering and fault attribution analysis are realized based on the building of areo-engine lubrication system fault knowledge graph. The application results show that the knowledge graph technology can realize the utilization of prior knowledge of lubrication system faults and the explanation of fault causes. And the knowledge graph technology has a good application prospect in the field of intelligent fault diagnosis.

A Novel Kind of Knowledge Graph Construction Method for Intelligent Machine as a Service Modeling

With the development of Intelligent Machine as a Service (IMaaS), devices increasingly require personalization, intelligence, and service orientation, making resource modeling a key challenge. Knowledge graph (KG) technology, known for unifying heterogeneous data, has become an essential tool for modeling and analyzing manufacturing resources. On this basis, this study proposes a novel resource KG construction method for IMaaS. First, an E-R diagram is used to divide the constant and variable entities and set the constant attributes and the constant relationships. Then, the triplets are named, the value space is set, and the schema layer is constructed. Finally, the related information about devices is used to fill the data layer, and then, the knowledge graph is generated. Meanwhile, this study utilizes desktop FDM 3D printing devices as a case example for validation. The method proposed in this study can enhance the accuracy and maintainability of equipment resource management in the manufacturing sector, effectively promoting subsequent activities such as management, analysis, and decision-making.

Quality inspection method of BIM model for power grid engineering based on knowledge graph

With the continuous updating of national specifications and industry standards, the traditional manual compliance review method is difficult to meet the needs of the development of the construction industry. This study aims to improve the quality review method of BIM models by using knowledge graph technology. The BIM model of power grid engineering is selected as the research object, and a BIM model inspection system based on knowledge graph is developed to verify whether it complies with the relevant specifications and standards of power grid engineering. By taking the BIM model of an electrical engineering project as a case, an inspection report was generated, and the results verified the feasibility and practicality of this method. This technology provides a new direction for the deep integration of architectural design and information technology.

Research on Construction of Knowledge Base for MRI Equipment Maintenance Based on Knowledge Graph

To improve the level of modern hospital management and establish a scientific and effective medical equipment maintenance guarantee system, this paper proposes the construction of a dynamic interactive fault repair knowledge base system for magnetic resonance imaging (MRI) equipment based on knowledge graph technology. This innovative application of intelligent management for large medical equipment involves collecting and organizing actual maintenance data. The paper constructs the medical equipment maintenance knowledge base, with MRI equipment as a case study, and initially implements functions such as fault information retrieval, technical guidance, and training and learning for MRI equipment. These functions aim to provide an objective decision-making basis for the maintenance and management of large medical equipment, enhance the efficiency of fault repair and maintenance, and elevate the construction level of intelligent hospitals.

Research on the Design of Academic Evaluation Standards of E-commerce Professional Group in Secondary Vocational Schools based on the Integration of Post-course Competition Certificate: Taking Short Video Courses as an Example

Given the subjective and one-sided problems existing in the current evaluation system of e-commerce courses in secondary vocational schools, this paper puts forward the implementation path of “four in one.” Through the integration of big data technology, knowledge graph technology and the PGSD ability analysis model, the evaluation graph is constructed, aiming at achieving comprehensiveness, adaptability, professionalism, scientific and objectivity of the evaluation. The evaluation graph includes a three-tier structure of evaluation system, knowledge domain and skill dimension, and systematically distributes weights to realize the development of multi-dimensional evaluation and personalized learning path.

Research on the Management Model of University Teaching Materials based on Knowledge Graph

Colleges and universities will produce a large number of teaching materials in their teaching work. It is of great significance to collect and manage teaching materials. In this study, knowledge graph technology is applied to the management of teaching materials in colleges and universities, and an ontology model of teaching materials in colleges and universities based on knowledge graph is proposed. Based on the relevant norms of teaching resources ontology in China, the ontology model of teaching materials in colleges and universities is constructed by seven-step method, and it is transformed into the attribute graph model of teaching materials in colleges and universities, which is stored in the Neo4 j graph database. The model can define the entity type and the relationship between entities, and construct the subject knowledge framework.

An Intelligent Hand-Assisted Diagnosis System Based on Information Fusion.

This research proposes an innovative, intelligent hand-assisted diagnostic system aiming to achieve a comprehensive assessment of hand function through information fusion technology. Based on the single-vision algorithm we designed, the system can perceive and analyze the morphology and motion posture of the patient's hands in real time. This visual perception can provide an objective data foundation and capture the continuous changes in the patient's hand movement, thereby providing more detailed information for the assessment and providing a scientific basis for subsequent treatment plans. By introducing medical knowledge graph technology, the system integrates and analyzes medical knowledge information and combines it with a voice question-answering system, allowing patients to communicate and obtain information effectively even with limited hand function. Voice question-answering, as a subjective and convenient interaction method, greatly improves the interactivity and communication efficiency between patients and the system. In conclusion, this system holds immense potential as a highly efficient and accurate hand-assisted assessment tool, delivering enhanced diagnostic services and rehabilitation support for patients.

Optimizing Tourism Accommodation Offers by Integrating Language Models and Knowledge Graph Technologies

Online platforms have become the primary means for travellers to search, compare, and book accommodations for their trips. Consequently, online platforms and revenue managers must acquire a comprehensive comprehension of these dynamics to formulate a competitive and appealing offerings. Recent advancements in natural language processing, specifically through the development of large language models, have demonstrated significant progress in capturing the intricate nuances of human language. On the other hand, knowledge graphs have emerged as potent instruments for representing and organizing structured information. Nevertheless, effectively integrating these two powerful technologies remains an ongoing challenge. This paper presents an innovative deep learning methodology that combines large language models with domain-specific knowledge graphs for classification of tourism offers. The main objective of our system is to assist revenue managers in the following two fundamental dimensions: (i) comprehending the market positioning of their accommodation offerings, taking into consideration factors such as accommodation price and availability, together with user reviews and demand, and (ii) optimizing presentations and characteristics of the offerings themselves, with the intention of improving their overall appeal. For this purpose, we developed a domain knowledge graph covering a variety of information about accommodations and implemented targeted feature engineering techniques to enhance the information representation within a large language model. To evaluate the effectiveness of our approach, we conducted a comparative analysis against alternative methods on four datasets about accommodation offers in London. The proposed solution obtained excellent results, significantly outperforming alternative methods.

Knowledge Management Model for Urban Flood Emergency Response Based on Multimodal Knowledge Graphs

Recently, frequent flood disasters in China have seriously threatened economic development and public safety. This paper addresses the need for a dynamic urban flood emergency knowledge management system in emergency management departments and the lack of systematic knowledge among emergency managers regarding urban flood control. A multimodal knowledge graph-based urban flood emergency knowledge management model was constructed to enhance the decision-making capabilities of emergency management departments, improve the efficiency of public emergency evacuation, and reduce losses from urban flood disasters by analyzing the shortcomings of the existing emergency management system. An intelligent and dynamic flood emergency knowledge management model was built. This paper integrates multimodal knowledge graph technology to establish a multimodal emergency knowledge management framework for urban flood control. It develops and simulates the proposed model’s application scenarios for urban flood emergency evacuation using the Flocking algorithm on the NetLogo platform. Through simulation experiments, the practicality and effectiveness of the model in real flood disaster situations were examined, particularly in simulating crowd evacuation behavior. The study found that the model significantly improves the accuracy of information and decision-making speed during emergency responses and supports emergency management departments in conducting targeted and personalized emergency decisions. This research provides a scientific basis for emergency management departments to optimize their emergency response strategies to flood disasters and serves as a reference and example for the application of multimodal knowledge graph technology in emergency management.

Construction of a large-scale maritime element semantic schema based on knowledge graph models for unmanned automated decision-making

In maritime logistics optimization, considerable research efforts are focused on the extraction of deep behavioral characteristics from comprehensive shipping data to discern patterns in maritime vessel behavior. The effective linkage of these characteristics with maritime infrastructure, such as berths, is critical for the enhancement of ship navigation systems. This endeavor is paramount not only as a research focus within maritime information science but also for the progression of intelligent maritime systems. Traditional methodologies have primarily emphasized the analysis of navigational paths of vessels without an extensive consideration of the geographical dynamics between ships and port infrastructure. However, the introduction of knowledge graphs has enabled the integration of disparate data sources, facilitating new insights that propel the development of intelligent maritime systems. This manuscript presents a novel framework using knowledge graph technology for profound analysis of maritime data. Utilizing automatic identification system (AIS) data alongside spatial information from port facilities, the framework forms semantic triplet connections among ships, anchorages, berths, and waterways. This enables the semantic modeling of maritime behaviors, offering precise identification of ships through their diverse semantic information. Moreover, by exploiting the semantic relations between ships and berths, a reverse semantic knowledge graph for berths is constructed, which is specifically tailored to ship type, size, and category. The manuscript critically evaluates a range of graph embedding techniques, dimensionality reduction methods, and classification strategies through experimental frameworks to determine the most efficacious methodologies. The findings reveal that the maritime knowledge graph significantly enhances the semantic understanding of unmanned maritime equipment, thereby improving decision-making capabilities. Additionally, it establishes a semantic foundation for the development of expansive maritime models, illustrating the potential of knowledge graph technology in advancing intelligent maritime systems.

Construction Method and Practical Application of Oil and Gas Field Surface Engineering Case Database Based on Knowledge Graph

To address the challenge of quickly and efficiently accessing relevant management experience for a wide range of ground engineering construction projects, supporting project management with information technology is crucial. This includes the establishment of a case database and an application platform for intelligent search and recommendations. The article leverages Optical Character Recognition (OCR) technology, knowledge graph technology, and Natural Language Processing (NLP) technology. It explores the mechanisms for classifying construction cases, methods for constructing a case database, structuring case data, intelligently retrieving and matching cases, and intelligent recommendation methods. This research forms a complete, feasible, and scalable method for deconstructing, storing, intelligently retrieving, and recommending construction cases, providing a theoretical basis for the establishment of a construction case database. It aims to meet the needs of digital project management and intelligent decision-making support in the oil and gas sector, thereby enhancing the efficiency and accuracy of project construction. This work offers a theoretical foundation for the development of an intelligent management platform for ground engineering projects in the oil and gas industry, supporting the sector’s digital transformation and intelligent development.

A case-based knowledge graph with reinforcement learning for intelligent design approach of complex product

Amidst the challenge of striving for rapid production and cost reduction, enterprises exhibit a growing demand for inventive, efficient, and high-caliber solutions in product design. The design field has followed the integration of Knowledge Graph and Artificial Intelligence technologies, and attempts have been made to address the demands of rapidity and intelligence. However, effectively leveraging knowledge to achieve rapid product generation remains a challenge. Thus, this paper proposes a case-based knowledge graph with reinforcement learning for the intelligent design approach of complex products, intending to tackle the difficulty of efficiently representing case knowledge of complex products and identifying key modification points in case reconstruction. Knowledge graph technology integrates and defines the features and component data from historical cases intuitively and structuredly. Similarity calculation is employed to facilitate case retrieval and the acquisition of configurable components. Combining the component relationships of the case graphs provides reward values for further adopted reinforcement learning, thereby aiding designers in identifying critical components to reconstruct the case further and achieving a balance between configuration efficiency and product quality. Finally, radar design is utilised as an exemplar to validate the efficacy of the proposed method.

A bibliometric review on research progress, interest evolution and future trend in the field of recycled concrete by using CiteSpace (2004–2023)

The popularization of recycled concrete (RC) is an important way to achieve efficient resource utilization of construction solid waste (CSW), which can coordinate the sustainable economic development with environmental protection. In this paper, CiteSpace software is used to quantitatively analyze the literatures on RC field, identifying its research hotspots and recent developments. Based on the Web of Science database, we screened published literatures in the field of RC from 2004 to 2023, and obtained 2011 articles. CiteSpace software and knowledge graph technology are adopted to visually analyze the literature information, including the collaborators, keywords and citation status. The results show that, Xiao JZ from Tongji University, Poon CS from Hong Kong Polytechnic University and Tam VWY from University of Western Sydney publish a large number of articles and are frequently cited. It indicates that their researches are widely recognized in academic circles. There is a research team with a large size formed in the Chinese mainland, and the core members include Xiao JZ, Li WG, Chen ZP. At present, the research interest gradually shifts from recycled coarse aggregate to recycled fine aggregate, mainly focusing on microscale research of pore structure and interfacial transition zone. In addition, 8 co-citation clusters of selected references and the potential development trends are detailly analyzed. This study is helpful for researchers to fully understand the current hotspots and future research trends in the field of RC.

Research hotspots and frontiers of Realgar based on CiteSpace knowledge graph analysis

This study employed knowledge graph technology to analyze the research status and hot spots of Realgar and provide guidance for clinical application and further research of this drug. The research articles both in English and Chinese involving Realgar were retrieved from five databases including CNKI, Wanfang, VIP, SinoMed, and Web of Science. And NoteExpress, a literature management software was used to screen literature. CiteSpace was utilized for visualized analysis and presentations of the authors, institutions, and keywords. 2 879 articles in Chinese and 194 articles in English were included. China Journal of Chinese Materia Medica and Journal of Ethnopharmacology were the top Chinese and English journals in terms of publication volume. Realgar is widely used in the treatment of skin diseases, blood diseases, and cancer. JIANG Hong was the author who have published more articles in Chinese and English working with teams. School of Public Health of China Medical University and China Academy of Chinese Medical Sciences published the most articles in Chinese and English. The research on Realgar mainly focuses on clinical application, mechanism of action, reduction of toxicity, and enhancement of efficacy. The authors and institutions of Realgar research are mainly concentrated in China. The study on the mechanism of treating hematological diseases and cancer with Realgar, as well as the research on its effects of reducing toxicity and enhancing efficacy, are the current research hotspots. The mechanism of "same treatment for different diseases" in Realgar needs to be further explored. It is urgent to carry out interdisciplinary research on Realgar. This study can provide a refe-rence for the clinical application of Realgar and provide ideas for further research on Realgar.

Intelligent Financial Risk Warning for Enterprises Through Knowledge Graph-Based Deep Learning

The financial risk warning methods for enterprises have always been a practical concern. In digital society, the computational intelligence has brought more spirit to this demand. This paper first introduces the current situation of the development of Knowledge graph technology, describes the deep learning fusion method based on Knowledge graph, and expresses the feasibility of this study. Then, according to the requirements of Knowledge graph, it completes the method fusion of core data training and extraction, and completes the adaptive deep learning design for the Beautiful SCOP database, and establishes a STDE-FG financial risk early warning model. Through empirical analysis, the shortcomings of this model were identified, and a comparison of optimized and optimized results was completed. Two aspects of phenomenon can be found from experimental results. For one thing, the accuracy of the unoptimized STDE-FG early warning model has been improved by 37.5–55.3% compared to traditional prediction models, but the prediction value during legal person changes has a greater error than traditional prediction values. For another, the optimized STDE-FG early warning model has also improved its accuracy in predicting new investments and equity changes, with improvements of 17–32% and 16–28%, respectively, with significant changes. This model will have a positive impact on improving enterprise risk management capabilities and reducing financial risk costs.

Visual analysis on the study status and trends of acupuncture and moxibustion for Alzheimer's disease.

The research history, hot spots and frontier trends of acupuncture and moxibustion for Alzheimer's disease (AD) were explored using knowledge graph technology. The articles on acupuncture and moxibustion for AD were searched from 6 databases, i.e. CNKI, VIP, Wanfang, SinoMed, Pubmed and Web of Science, from January 1st, 1993 to January 1st, 2023. Using CiteSpace6.2.R2 Advance and VOSviewer V1.6.19 softwares, the knowledge map was graphed and the visual analysis was performed. A total of 1 228 Chinese and 309 English articles were included. The high-frequency keywords were generally divided into the keywords of clinical diseases (AD, dementia), those of therapeutic methods (electroacupuncture, acupuncture-moxibustion and acupuncture) and those of mechanism study (β-amyloid, mice). Thirteen keyword clusters were formed among the articles of Chinese version, e.g. acupuncture-moxibustion, dementia, acupuncture and electroacupuncture; and 8 clusters were obtained among English articles, e.g. electroacupuncture, drug therapy and hippocampus. The high-frequency keywords of acupoints included Baihui (GV 20), Dazhui (GV 14), Yintang (GV 24+), Zusanli (ST 36), Fenglong (ST 40), etc. Six clusters of "acupuncture techniques → acupoints" were obtained for the treatment of AD with acupuncture and moxibustion. The therapeutic methods and modes of AD with acupuncture and moxibustion are constantly progressed, the development of clinical research tends to the evaluation of novel therapeutic mode and clinical effect, and the mechanism of acupuncture and moxibustion for the effect on AD are more deeply explored. Among the various therapeutic methods, acupuncture-moxibustion, acupuncture and electroacupuncture have been early predominant; while, many novel methods are gradually displayed later, such as music electroacupuncture and hydro-acupuncture. In recent 30 years, among Chinese and English articles for the studies of AD treated with acupuncture and moxibustion, the theme of them focuses on the two aspects, the observation of clinical effect and the mechanism research. It is found that the clinical therapeutic methods have been gradually improved and the mechanism exploration been deepened.