To deal with a lack of semantic interoperability of traditional knowledge retrieval approaches, a semantic-based networked manufacturing （NM） knowledge retrieval architecture is proposed, which offers a series of to...To deal with a lack of semantic interoperability of traditional knowledge retrieval approaches, a semantic-based networked manufacturing （NM） knowledge retrieval architecture is proposed, which offers a series of tools for supporting the sharing of knowledge and promoting NM collaboration. A 5-tuple based semantic information retrieval model is proposed, which includes the interoperation on the semantic layer, and a test process is given for this model. The recall ratio and the precision ratio of manufacturing knowledge retrieval are proved to be greatly improved by evaluation. Thus, a practical and reliable approach based on the semantic web is provided for solving the correlated concrete problems in regional networked manufacturing.展开更多
In order to improve the utilization ratio of knowledge retrieval, a product-design knowledge retrieval approach based on ontology is proposed. A representation model of product-design knowledge is proposed according t...In order to improve the utilization ratio of knowledge retrieval, a product-design knowledge retrieval approach based on ontology is proposed. A representation model of product-design knowledge is proposed according to its characteristics. Domain ontology of product-design is estab- lished and the semantic annotation technology is used to connect the design knowledge and ontolo- gy. A new semantic annotation format is developed and semantic information of the design knowl- edge is enriched by making use of ontology. On that basis a retrieval algorithm is designed for semantic retrieval. Finally, this approach is used in a knowledge management system for military-vehi- cle design and its effectiveness and feasibility are validated. Results show that the recall ratio and the precision ratio of knowledge retrieval are improved greatly and users＇ requirements in semantic retrieval are satisfied.展开更多
OBJECTIVE:To design a model to capture information on the state and trends of knowledge creation,at both an individual and an organizational level,in order to enhance knowledge management.METHODS:We designed a graph-t...OBJECTIVE:To design a model to capture information on the state and trends of knowledge creation,at both an individual and an organizational level,in order to enhance knowledge management.METHODS:We designed a graph-theoretic knowledge model,the expert knowledge map(EKM),based on literature-based annotation.A case study in the domain of Traditional Chinese Medicine research was used to illustrate the usefulness of the model.RESULTS:The EKM successfully captured various aspects of knowledge and enhanced knowledge management within the case-study organization through the provision of knowledge graphs,expert graphs,and expert-knowledge biography.CONCLUSION:Our model could help to reveal thehot topics,trends,and products of the research done by an organization.It can potentially be used to facilitate knowledge learning,sharing and decision-making among researchers,academicians,students,and administrators of organizations.展开更多
As large language models(LLMs)continue to demonstrate their potential in handling complex tasks,their value in knowledge-intensive industrial scenarios is becoming increasingly evident.Fault diagnosis,a critical domai...As large language models(LLMs)continue to demonstrate their potential in handling complex tasks,their value in knowledge-intensive industrial scenarios is becoming increasingly evident.Fault diagnosis,a critical domain in the industrial sector,has long faced the dual challenges of managing vast amounts of experiential knowledge and improving human-machine collaboration efficiency.Traditional fault diagnosis systems,which are primarily based on expert systems,suffer from three major limitations:(1)ineffective organization of fault diagnosis knowledge,(2)lack of adaptability between static knowledge frameworks and dynamic engineering environments,and(3)difficulties in integrating expert knowledge with real-time data streams.These systemic shortcomings restrict the ability of conventional approaches to handle uncertainty.In this study,we proposed an intelligent computer numerical control(CNC)fault diagnosis system,integrating LLMs with knowledge graph(KG).First,we constructed a comprehensive KG that consolidated multi-source data for structured representation.Second,we designed a retrievalaugmented generation(RAG)framework leveraging the KG to support multi-turn interactive fault diagnosis while incorporating real-time engineering data into the decision-making process.Finally,we introduced a learning mechanism to facilitate dynamic knowledge updates.The experimental results demonstrated that our system significantly improved fault diagnosis accuracy,outperforming engineers with two years of professional experience on our constructed benchmark datasets.By integrating LLMs and KG,our framework surpassed the limitations of traditional expert systems rooted in symbolic reasoning,offering a novel approach to addressing the cognitive paradox of unstructured knowledge modeling and dynamic environment adaptation in industrial settings.展开更多
Research on specific domain question-answering technology has become important with the increasing demand for intelligent question-answering systems. This paper proposes a domain question-answering algorithm based on ...Research on specific domain question-answering technology has become important with the increasing demand for intelligent question-answering systems. This paper proposes a domain question-answering algorithm based on the CLIP mechanism to improve the accuracy and efficiency of interaction. First, this paper reviewed relevant technologies involved in the question-answering field. Then, the question-answering model based on the CLIP mechanism was produced, including its design, implementation, and optimization. It also described the construction process of the specific domain knowledge graph, including graph design, data collection and processing, and graph construction methods. The paper compared the performance of the proposed algorithm with classic question-answering algorithms BiDAF, R-Net, and XLNet models, using a military domain dataset. The experimental results show that the proposed algorithm has advanced performance, with an F1 score of 84.6% on the constructed military knowledge graph test set, which is at least 1.5% higher than other models. We conduct a detailed analysis of the experimental results, which illustrates the algorithm’s advantages in accuracy and efficiency, as well as its potential for further improvement. These findings demonstrate the practical application potential of the proposed algorithm in the military domain.展开更多
基金The National High Technology Research and Devel-opment Program of China (863Program) (No2003AA1Z2560,2002AA414060)the Key Science and Technology Program of Shaanxi Province (No2006K04-G10)
文摘To deal with a lack of semantic interoperability of traditional knowledge retrieval approaches, a semantic-based networked manufacturing （NM） knowledge retrieval architecture is proposed, which offers a series of tools for supporting the sharing of knowledge and promoting NM collaboration. A 5-tuple based semantic information retrieval model is proposed, which includes the interoperation on the semantic layer, and a test process is given for this model. The recall ratio and the precision ratio of manufacturing knowledge retrieval are proved to be greatly improved by evaluation. Thus, a practical and reliable approach based on the semantic web is provided for solving the correlated concrete problems in regional networked manufacturing.
基金Supported by the National Defence Research Foundation(41234)
文摘In order to improve the utilization ratio of knowledge retrieval, a product-design knowledge retrieval approach based on ontology is proposed. A representation model of product-design knowledge is proposed according to its characteristics. Domain ontology of product-design is estab- lished and the semantic annotation technology is used to connect the design knowledge and ontolo- gy. A new semantic annotation format is developed and semantic information of the design knowl- edge is enriched by making use of ontology. On that basis a retrieval algorithm is designed for semantic retrieval. Finally, this approach is used in a knowledge management system for military-vehi- cle design and its effectiveness and feasibility are validated. Results show that the recall ratio and the precision ratio of knowledge retrieval are improved greatly and users＇ requirements in semantic retrieval are satisfied.
基金Supported by the Ministry of Science and Technology Support Projects(No.12116BAI14A21)
文摘OBJECTIVE:To design a model to capture information on the state and trends of knowledge creation,at both an individual and an organizational level,in order to enhance knowledge management.METHODS:We designed a graph-theoretic knowledge model,the expert knowledge map(EKM),based on literature-based annotation.A case study in the domain of Traditional Chinese Medicine research was used to illustrate the usefulness of the model.RESULTS:The EKM successfully captured various aspects of knowledge and enhanced knowledge management within the case-study organization through the provision of knowledge graphs,expert graphs,and expert-knowledge biography.CONCLUSION:Our model could help to reveal thehot topics,trends,and products of the research done by an organization.It can potentially be used to facilitate knowledge learning,sharing and decision-making among researchers,academicians,students,and administrators of organizations.
基金funded by the National Natural Science Foundation of China(72104224,L2424237,71974107,L2224059,L2124002,and 91646102)the Beijing Natural Science Foundation(9232015)+4 种基金the Beijing Social Science Foundation(24GLC058)the Construction Project of China Knowledge Center for Engineering Sciences and Technology(CKCEST-2023-1-7)the MOE(Ministry of Education in China)Project of Humanities and Social Sciences(16JDGC011)the Tsinghua University Initiative Scientific Research Program(2019Z02CAU)the Tsinghua University Project of Volvo-Supported Green Economy and Sustainable Development(20183910020)。
文摘As large language models(LLMs)continue to demonstrate their potential in handling complex tasks,their value in knowledge-intensive industrial scenarios is becoming increasingly evident.Fault diagnosis,a critical domain in the industrial sector,has long faced the dual challenges of managing vast amounts of experiential knowledge and improving human-machine collaboration efficiency.Traditional fault diagnosis systems,which are primarily based on expert systems,suffer from three major limitations:(1)ineffective organization of fault diagnosis knowledge,(2)lack of adaptability between static knowledge frameworks and dynamic engineering environments,and(3)difficulties in integrating expert knowledge with real-time data streams.These systemic shortcomings restrict the ability of conventional approaches to handle uncertainty.In this study,we proposed an intelligent computer numerical control(CNC)fault diagnosis system,integrating LLMs with knowledge graph(KG).First,we constructed a comprehensive KG that consolidated multi-source data for structured representation.Second,we designed a retrievalaugmented generation(RAG)framework leveraging the KG to support multi-turn interactive fault diagnosis while incorporating real-time engineering data into the decision-making process.Finally,we introduced a learning mechanism to facilitate dynamic knowledge updates.The experimental results demonstrated that our system significantly improved fault diagnosis accuracy,outperforming engineers with two years of professional experience on our constructed benchmark datasets.By integrating LLMs and KG,our framework surpassed the limitations of traditional expert systems rooted in symbolic reasoning,offering a novel approach to addressing the cognitive paradox of unstructured knowledge modeling and dynamic environment adaptation in industrial settings.
文摘Research on specific domain question-answering technology has become important with the increasing demand for intelligent question-answering systems. This paper proposes a domain question-answering algorithm based on the CLIP mechanism to improve the accuracy and efficiency of interaction. First, this paper reviewed relevant technologies involved in the question-answering field. Then, the question-answering model based on the CLIP mechanism was produced, including its design, implementation, and optimization. It also described the construction process of the specific domain knowledge graph, including graph design, data collection and processing, and graph construction methods. The paper compared the performance of the proposed algorithm with classic question-answering algorithms BiDAF, R-Net, and XLNet models, using a military domain dataset. The experimental results show that the proposed algorithm has advanced performance, with an F1 score of 84.6% on the constructed military knowledge graph test set, which is at least 1.5% higher than other models. We conduct a detailed analysis of the experimental results, which illustrates the algorithm’s advantages in accuracy and efficiency, as well as its potential for further improvement. These findings demonstrate the practical application potential of the proposed algorithm in the military domain.
