Construction Method Of Knowledge Graph Research Articles

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.

Knowledge Graph Construction Method for Commercial Aircraft Fault Diagnosis Based on Logic Diagram Model

Knowledge Graph Construction Method for Commercial Aircraft Fault Diagnosis Based on Logic Diagram Model

A task‐centric knowledge graph construction method based on multi‐modal representation learning for industrial maintenance automation

AbstractMaintenance manuals are crucial information sources for maintenance and repair. Prior studies explored factual knowledge extraction from textual documents. However, maintenance knowledge in manuals is more task‐centric rather than factual knowledge and often documented in an unstructured Portable Document Format (PDF), posing challenges for knowledge extraction. Addressing this, this research develops effective methods to extract task‐centric maintenance knowledge from unstructured PDF manuals. A new Task‐centric Knowledge Graph (TCKG) schema centralized on maintenance task components (MTCs) is proposed to address the need for structured knowledge representation. A method (Heterogeneous Graph‐based Method, HGM) for knowledge extraction is then proposed, which is enhanced by incorporating visual and spatial information. In the experiments, the proposed HGM exhibits robust performance in the knowledge extraction process, surpassing the baseline Graph‐based Interaction Model with a Tracker (GIT) method in MTCs extraction by 13.3%, and the baseline Translate Embedding (TransE) method in MTCs' relation extraction by 3.8%. A series of ablation studies also prove that including visual and spatial information through the proposed method can improve the relation extraction performance by over 10%. This research supplies valuable insights for future developments in information extraction from maintenance manuals.

Distant supervision knowledge extraction and knowledge graph construction method for supply chain management domain

As the core competitiveness of the national industry, large-scale equipment such as ships, high-speed rail and nuclear power equipment, their production process involves in-depth personalization. It includes complex processes and long manufacturing cycles. In addition, the equipment’s supply chain management is extremely complex. Therefore, the development of a supply chain management knowledge graph is of significant strategic significance. It not only enhances the synergistic effect of the supply chain management but also upgrades the level of intelligent management. This paper proposes a distant supervision knowledge extraction and knowledge graph construction method in the supply chain management of large equipment manufacturing, which achieves digital and structured management and efficient use of supply chain management knowledge in the industry. This paper presents an approach to extract entity-relation knowledge using limited samples. We achieve this by establishing a distant supervision model. Furthermore, we introduce a fusion gate mechanism and integrate ontology information, thereby enhancing the model’s capability to effectively discern sentence-level semantics. Subsequently, we promptly modify the weights of input features using the gate mechanism to strengthen the model’s resilience and address the issue of vector noise diffusion. Finally, an inter-bag sentence attention mechanism is introduced to integrate different sentence bag information at the sentence bag level, which achieves more accurate entity-relation knowledge extraction. The experimental results prove that compared with the latest distant supervision method, the accuracy of relation extraction is improved by 2.8%, and the AUC value is increased by 3.9%, effectively improving the quality of knowledge graph in supply chain management.

A Semi-Supervised Failure Knowledge Graph Construction Method for Decision Support in Operations and Maintenance

A Semi-Supervised Failure Knowledge Graph Construction Method for Decision Support in Operations and Maintenance

Research on knowledge graph-driven equipment fault diagnosis method for intelligent manufacturing

AbstractIn the process of rotating machinery fault diagnosis (RMFD), the lack of feature conditions leads to the problem of low accuracy of traditional rule-based reasoning methods FD. This paper proposed a knowledge graph (KG)-driven device FD method and applied it to RMFD. First, we proposed a multi-level KG construction method to get multi-source data based on each level and analyzed the levels that affected the system state. A single-level KG was constructed through data features, and a multilevel KG with a stereostructure was built using a multi-source data fusion model as data support for FD. Second, we proposed an approach based on multilevel KG and Bayes theory to detect the system state and located the source of faults by combining the KG reasoning based on relational paths, then used the relationships between the structures of rotating mechanical equipment for fault cause reasoning and used the KG as a knowledge base for a reason using machine learning. Finally, the proposed method was validated using a steelworks motor as an example and compared with other ways, such as rule-based FD. The results show that under the condition of missing input features, the accuracy of the proposed method reaches 91.1%, which is significantly higher than other methods and effectively solves the problem of low diagnostic accuracy.

Visualization and Analysis of Knowledge Graph for the Integration of Traditional Culture and Rural Tourism Industry

Abstract In order to promote the integrated development of culture and the tourism industry, this paper explores the integration of traditional culture and the rural tourism industry. Firstly, a bottom-up knowledge graph construction method is designed based on the DGCN relational extraction model, Muhead-CU-FL-BE entity extraction model and Neo4j high-performance graph database. Then, based on the domain and characteristics of traditional culture and rural tourism industry integration, the knowledge graph of rural traditional culture and tourism industry integration is constructed from three aspects: line results, ontology model and description of this paper. Finally, the performance of the knowledge graph constructed in this paper is tested on the relevant dataset, and the visualization analysis of industrial integration is carried out through the constructed knowledge graph. The results show that the overall performance of the relationship extraction algorithm in this paper is around 0.7, the entity extraction algorithm has the best performance, and the overall performance is around 0.8, and the ratio of the public ancestor nodes of 5 times linking in the 5th and 6th layers is greater than 0.65. The centrality of culture and tourism industry, cultural and tourism fusion, high-quality development, culture and tourism, and industry fusion are 0.78, 0.60, 0.58, and 0.80. The centrality of the keywords is 0.78, 0.60, 0.58, and 0.80, respectively, 1.00, and the strength of the salient values of each keyword is concentrated around 1~3.

SMAAMA: A named entity alignment method based on Siamese network character feature and multi-attribute importance feature for Chinese civil aviation

With the growth of demand for air-space integrated transportation industry, the civil aviation transportation industry plays an increasingly important role in the national economy and transportation. The diversity and diversified development of civil aviation emergencies have greatly affected the decision-making efficiency for emergencies in civil aviation emergency management systems. Constructing a knowledge graph of civil aviation and improving the reliability and richness of the knowledge graph has become an urgent problem to be solved. There are some problems during the construction of a civil aviation domain knowledge graph such as long entity length, existing hybrid and composite entities, the similarity of the font features of domain entity names, a difference of information between entities, separated coding between entities, and error-prone in the transmission process of coding. So we research the construction method of knowledge graph for civil aviation emergencies to resolve these problems. (1) We propose a font pre-training model and incorporate a character feature layer into the embedding layer. (2) We propose a multi-attribute attention alignment method based on the Siamese network; entities with similar structures in the civil aviation knowledge base are input into the font layer for pre-training; the complete semantic information of entities and the importance of different attributes to entities are learned through two sub-networks respectively to improve the integrity of civil aviation knowledge graph. Experiments were carried out on the self-built aviation emergency and public data sets, respectively. The experimental results show that compared with other models, the F1 value of the proposed model can reach 96.97%, which verifies the feasibility of the model.

A Knowledge Graph Construction Method for Software Project Based on CAJP

<p>In recent years, there has been increasing interest in using knowledge graphs (KGs) to help stakeholders organize and better understand the connections between various artifacts during software development. However, extracting entities and relationships automatically and accurately in open-source projects is still a challenge. Therefore, an efficient method called Concise Annotated JavaParser (CAJP) has been proposed to support these extraction activities, which are vitally important for KG construction. The experimental result shows that CAJP improves the accuracy and type of entity extraction and ensures the accuracy of relationship exaction. Moreover, an intelligent question-and-answer (Q&A) system is designed to visualize and verify the quality of the KGs constructed from six open-source projects. Overall, the software project-oriented KG provides developers a valuable and intuitive way to access and understand project information.</p> <p> </p>

CMKG: Construction Method of Knowledge Graph for Image Recognition

With the continuous development of artificial intelligence technology and the exponential growth in the number of images, image detection and recognition technology is becoming more widely used. Image knowledge management is extremely urgent. The data source of a knowledge graph is not only the text and structured data but also the visual or auditory data such as images, video, and audio. How to use multimodal information to build an information management platform is a difficult problem. In this paper, a method is proposed to construct the result of image recognition as a knowledge graph. First of all, based on the improvement in the BlendMASK algorithm, the hollow convolution kernel is added. Secondly, the effect of image recognition and the relationships between all kinds of information are analyzed. Finally, the image knowledge graph is constructed by using the relationship between the image entities. The contributions of this paper are as follows. (1) The hollow convolution kernel is added to reduce the loss from extracting feature information from high-level feature images. (2) In this paper, a method is proposed to determine the relationship between entities by dividing the recognition results of entities in an image with a threshold, which makes it possible for the relationships between images to be interconnected. The experimental results show that this method improves the accuracy and F1 value of the image recognition algorithm. At the same time, the method achieves integrity in the construction of a multimodal knowledge graph.

Analysis of the dilemma of higher vocational thinking education in China under the background of “Internet+”

Abstract Network culture is a culture based on computer network information technology. With the popularity of “Internet+”, the influence of network culture on college students’ ideological views, behavioral patterns, value orientations and psychological development is also increasing. While Internet culture brings opportunities to higher education ideological and political education, it also makes education work face serious challenges. This paper proposes a collaborative knowledge graph construction method for Civic Education based on Internet + group intelligence. The core of the method is a continuously running loop that contains 3 activities: free exploration, automatic integration, and active feedback. The experimental results show that the knowledge graph fusion algorithm can effectively utilize the structural information of the knowledge graph as well as the semantic information of the nodes to form a high-quality knowledge graph fusion scheme. The collaborative approach based on the “explore-fusion-feedback” loop can improve the scale of group construction of knowledge graphs and the efficiency of individual construction of knowledge graphs, and exhibit better scalability of group scale.

Construction and analysis of knowledge graphs for multi‐source heterogeneous data of soil pollution

AbstractThe decommissioning and overall relocation of urban industrial enterprises have generated a large number of decommissioned contaminated sites, and the leftover soil pollution is gradually becoming a major problem that restricts urban green development and damages human health. Deep mining and efficient management of site soil pollution information through digitization and informatization are needed to solve these problems more accurately and efficiently. Knowledge mapping for visual analysis of relevant pathways is a forward‐looking approach in soil contamination management that does not require complex testing instruments, thus saving research manpower, time and cost. Data associated with contaminated sites come from a wide range of sources and have different structures. Through the natural language processing technology of computer, suitable methods such as entity recognition, relationship recognition and knowledge fusion are selected to extract various types of information from contaminated sites and establish semantic networks for fast targeting of soil contamination sources, thus providing a more convenient solution. In this paper, we propose a knowledge graph construction method for multi‐source heterogeneous data of contaminated sites, find sulphide contamination sources through visual analysis of knowledge graph and explore the application prospects of natural language processing techniques such as knowledge graph in contaminated site management.

Cybersecurity knowledge graph enabled attack chain detection for cyber-physical systems

The Cyber-Physical System covers a wide range of applications, many of which are involved in critical infrastructure, and the cybersecurity attacks on them become more and more threatening. Currently, most of the comprehensive analysis of compound attacks depend on the experience of security analysts. To improve the efficiency and accuracy of compound attack research, this paper introduces a knowledge graph into compound attack detection and constructs a cybersecurity knowledge graph based on the knowledge of known attacks. The cybersecurity knowledge graph can carry out correlation analysis on real-time data to restore the attack process. The main work of this paper is to construct the cybersecurity knowledge graph and to apply mining found compound attacks automatically. Besides, a multi-dimensional data association analysis algorithm based on dynamic clustering mechanism, and an attack chain complementation-pruning method based on optimal reaching path queries are proposed to solve the problem of low efficiency in correlation analysis caused by redundant data and the problem of missing and misunderstandings in the collection data. Experiments show that the cyber security knowledge graph construction method and attack chain optimization-pruning method proposed in this paper improve the accuracy and efficiency of attack chain mining.

Research on the construction and implementation of power grid fault handling knowledge graphs

Power grid fault disposal has important guiding significance for the efficient and orderly emergency work of power grid accidents. The knowledge graph technology is used to extract, express and manage the fault disposal information. It is also used to assist dispatchers in fault handling, which can effectively improve the power grid emergency handling capacity and dispatching intelligence level. The unstructured content in the form of a large number of texts in the power grid dispatching operation link is condensed into a structured knowledge network that can be expressed, operated and reasoned. For example, operating procedures, disposal plans, dispatching rules, etc. A top-down and bottom-up knowledge graph construction method for power grid fault handling is proposed. The knowledge graphs are divided into three categories: entity graph of power grid equipment, logic graph of fault handling and case graph. The data and technical means required for graph construction are described. In the process of practice, the knowledge organization structure, notion types, and relationships between notions of the knowledge graphs are defined to form the model layer of knowledge graph. Finally, according to the function realization of each link, the solution ideas and key technologies are analyzed. So as to realize fault information analysis and discrimination based on knowledge atlas, intelligent aided decision-making and multi-dimensional human–computer interaction.

A Study on a Knowledge Graph Construction Method of Safety Reports for Process Industries

There are some representative reports in industrial safety engineering, such as the Hazard and Operability Analysis and Pre-Hazard Analysis; however, a large amount of industrial safety knowledge in the report has not been fully explored. In order to reuse and release the value of industrial safety knowledge, this paper constructs a new industrial safety knowledge extraction framework. The framework combines the asset management shell to summarize the knowledge concept entities of machine description language and model description language. According to the safety report template, the framework also constructs a new industrial safety knowledge-mapping standard structure. Specifically, firstly, considering that the knowledge structure of safety reports is different in different processes of the process industry, this paper innovatively proposes a general industrial safety knowledge-mapping standard structure, which provides a practical solution for the integration of industrial knowledge representation problems in different processes. Secondly, based on the research progress of named entities, this paper presents an industrial named entity extraction method (INERM) for the process industry. This method designs an entity weight model to calculate the entity weight of each sentence, and adds part-of-speech weight to improve the entity extraction algorithm, which alleviates the problem that the existing entity extraction methods cannot reasonably use the semantic information and context of word. Finally, we construct a triple of industrial safety knowledge based on the rules and store it in Neo4j. In this paper, four semantic-type templates and five semantic relation templates are constructed based on the new industrial safety knowledge map standardization construction process of the process industry. The comparative experiments show that the accuracy of the INERM on the test set is improved by 17 percentage points on average compared with other key entity extraction algorithms. A total of 1329 entities are constructed in the directional application example of the fluid transportation process, which provides a large number of references for the safety of the fluid transportation process and is more conducive to improving the safety guarantee of the fluid transport process.

Dynamic knowledge modeling and fusion method for custom apparel production process based on knowledge graph

Compared with ordinary mass-produced apparel products, custom apparel products will generate more data at each stage of their life cycle. Such data is in a highly dynamic state, while the relationship between the data is more complex. However, the current use of traditional relational data to store the whole life cycle data of custom apparel products has several problems of high redundancy, weak correlation, discrete distribution, and certain limitation of storage capacity. Therefore, based on knowledge graph, a dynamic knowledge modeling and fusion method is proposed for the production process of custom apparel. Firstly, an ontology-based knowledge modeling method is designed for custom apparel, which defined three types of ontology modeling methods for the process, resources, and features. On this basis, a knowledge graph construction method based on bi-directional fusion for the custom apparel production system is proposed. With one order as a unit, a knowledge graph facet (KGF) model, as well as the derived knowledge representation, generation and fusion method, is established to realize dynamic knowledge fusion of the custom apparel production process. Finally, taking the suit production process of a custom apparel factory as an example, the corresponding knowledge graph is constructed based on the ontology knowledge model, and the effectiveness of the proposed knowledge fusion method is verified.

Zero-shot surface defect recognition with class knowledge graph

Convolutional neural networks (CNNs)-based methods achieve excellent performance on surface defects that belong to the same base classes from a training set, which plays an essential role in ensuring product quality in manufacturing systems. However, in the real world, novel classes of defects always exit due to the complexity and changes of the manufacturing environment. These novel classes are never seen during the training stage but are required to be correctly classified. This paper proposes a class knowledge graph (ZS-CKG) method to address the zero-shot problem in real-world surface defect recognition. In the proposed ZS-CKG method, the class knowledge graph construction method (CKGC) is proposed to construct a class knowledge graph to establish the relationship between base and novel defect classes. Then learns class features by using a graph convolutional neural network. The ZS-CKG utilizes the transformer encoder with capturing long-range dependencies to extract features of defect samples to obtain discriminative defect image features, due to the fact that industrial defects have different shapes and sizes. The experimental results on the public NEU-CLS dataset and real engineering dataset printed circuit boards (PCB) surface defects collected from an actual manufacturing factory demonstrate that the proposed method can effectively address zero-shot surface defect recognition. The ZS-CKG achieve an accuracy of 60.91% and 50.53% on the NEU-CLS and PCB datasets, respectively, which increase by 33.82% and 2.36% compared to the best competing method.

Combining knowledge graph into metro passenger flow prediction: A split-attention relational graph convolutional network

With the rapid development of intelligent operation and management in metro systems, accurate network-scale passenger flow prediction has become an essential component in real-time metro management. Although numerous novel methods have been applied in this field, critical barriers still exist in integrating travel behaviors and comprehensive spatiotemporal dependencies into prediction. This study constructs the metro system as a knowledge graph and proposes a split-attention relational graph convolutional network (SARGCN) to address these challenges. Breaking the limitations of physical metro networks, we develop a metro topological graph construction method based on the historical origin–destination (OD) matrix to involve travel behaviors. Then, we design a metro knowledge graph construction method to incorporate land-use features. To adapt prior knowledge of metro systems, we subsequently propose the SARGCN model for network-scale metro passenger flow prediction. This model integrates the relational graph convolutional network (R-GCN), split-attention mechanism, and long short-term memory (LSTM) to explore the spatiotemporal correlations and dependence between passenger inflow and outflow. According to the model validation conducted on the metro systems in Shenzhen and Hangzhou, China, the SARGCN model outperforms the advanced baselines. Furthermore, quantitative experiments also reveal the effectiveness of its component and the constructed metro knowledge graph.

Correlation between the Dissemination of Classic English Literary Works and Cultural Cognition in the New Media Era

With the continuous development of new media technology, the spiritual needs of the masses have been greatly satisfied and the aesthetic ability has also been significantly improved compared with the past. From the current point of view, “literary works,” as the spiritual food of contemporary people, are promoting social spirit. The use of natural language processing and knowledge graph technology can improve cultural cognition to promote the dissemination and development of classic English literature, which has become a necessary means of dissemination of classic English literature. Most of the existing classic English literary works are appreciated based on modern literature datasets. Nowadays, with the continuous development of new media technology, there are fewer studies on the dissemination and cultural cognition of classic English literary works. This makes it impossible for readers to obtain cultural cognition from classic English literary works, making it difficult for the dissemination and development of classic English literary works. In view of the above problems, using natural language processing and knowledge graph technology, taking Shakespeare's play “Hamlet” represented by classic English literary works as an example, the research on the construction method of knowledge graph is carried out and the cultural characteristics in literary works are extracted and analyzed. In parsing, a bidirectional gated recurrent unit network model based on hybrid character embedding is proposed. Based on n-gram embedding, by combining pretraining embedding and radical embedding, it can fully consider the rich semantic information in English literature works to extract. Feature: in terms of named entity recognition, based on the existing iterative atrous convolutional network model, an iterative atrous convolutional network model is proposed. To get the best sequence label and get the last labeled entity information, in terms of knowledge graph construction and visual query, a workflow method for building knowledge graph from unstructured text is proposed and a flask-based knowledge graph visual query system is designed, which applies the best model of the above two tasks. We decode the complete “Hamlet” text, extract entities and their semantic links as nodes and relationships in the knowledge graph, store knowledge through the graph database, and finally form a visual query system that combines the front and back end.

Construction method of knowledge graph under machine learning

Construction method of knowledge graph under machine learning