In the context of power generation companies, vast amounts of specialized data and expert knowledge have been accumulated. However, challenges such as data silos and fragmented knowledge hinder the effective utilizati...In the context of power generation companies, vast amounts of specialized data and expert knowledge have been accumulated. However, challenges such as data silos and fragmented knowledge hinder the effective utilization of this information. This study proposes a novel framework for intelligent Question-and-Answer (Q&A) systems based on Retrieval-Augmented Generation (RAG) to address these issues. The system efficiently acquires domain-specific knowledge by leveraging external databases, including Relational Databases (RDBs) and graph databases, without additional fine-tuning for Large Language Models (LLMs). Crucially, the framework integrates a Dynamic Knowledge Base Updating Mechanism (DKBUM) and a Weighted Context-Aware Similarity (WCAS) method to enhance retrieval accuracy and mitigate inherent limitations of LLMs, such as hallucinations and lack of specialization. Additionally, the proposed DKBUM dynamically adjusts knowledge weights within the database, ensuring that the most recent and relevant information is utilized, while WCAS refines the alignment between queries and knowledge items by enhanced context understanding. Experimental validation demonstrates that the system can generate timely, accurate, and context-sensitive responses, making it a robust solution for managing complex business logic in specialized industries.展开更多
Search engines have greatly helped us to find the desired information from the Internet. Most search engines use keywords matching technique. This paper discusses a Dynamic Knowledge Base based Search Engine （DKBSE）...Search engines have greatly helped us to find the desired information from the Internet. Most search engines use keywords matching technique. This paper discusses a Dynamic Knowledge Base based Search Engine （DKBSE）, which can expand the user＇s query using the keywords＇ concept or meaning. To do this, the DKBSE needs to construct and maintain the knowledge base dynamically via the system＇s searching results and the user＇s feedback information. The DKBSE expands the user＇s initial query using the knowledge base, and returns the searched information after the expanded query.展开更多
Knowledge graphs often suffer from sparsity and incompleteness.Knowledge graph reasoning is an effective way to address these issues.Unlike static knowledge graph reasoning,which is invariant over time,dynamic knowled...Knowledge graphs often suffer from sparsity and incompleteness.Knowledge graph reasoning is an effective way to address these issues.Unlike static knowledge graph reasoning,which is invariant over time,dynamic knowledge graph reasoning is more challenging due to its temporal nature.In essence,within each time step in a dynamic knowledge graph,there exists structural dependencies among entities and relations,whereas between adjacent time steps,there exists temporal continuity.Based on these structural and temporal characteristics,we propose a model named“DKGR-DR”to learn distributed representations of entities and relations by combining recurrent neural networks and graph neural networks to capture structural dependencies and temporal continuity in DKGs.In addition,we construct a static attribute graph to represent entities’inherent properties.DKGR-DR is capable of modeling both dynamic and static aspects of entities,enabling effective entity prediction and relation prediction.We conduct experiments on ICEWS05-15,ICEWS18,and ICEWS14 to demonstrate that DKGR-DR achieves competitive performance.展开更多
Under the paradigm of Industry 5.0,intelligent manufacturing transcends mere efficiency enhancement by emphasizing human-machine collaboration,where human expertise plays a central role in assembly processes.Despite a...Under the paradigm of Industry 5.0,intelligent manufacturing transcends mere efficiency enhancement by emphasizing human-machine collaboration,where human expertise plays a central role in assembly processes.Despite advancements in intelligent and digital technologies,assembly process design still heavily relies on manual knowledge reuse,and inefficiencies and inconsistent quality in process documentation are caused.To address the aforementioned issues,this paper proposes a knowledge push method of complex product assembly process design based on distillation model-based dynamically enhanced graph and Bayesian network.First,an initial knowledge graph is constructed using a BERT-BiLSTM-CRF model trained with integrated human expertise and a fine-tuned large language model.Then,a confidence-based dynamic weighted fusion strategy is employed to achieve dynamic incremental construction of the knowledge graph with low resource consumption.Subsequently,a Bayesian network model is constructed based on the relationships between assembly components,assembly features,and operations.Bayesian network reasoning is used to push assembly process knowledge under different design requirements.Finally,the feasibility of the Bayesian network construction method and the effectiveness of Bayesian network reasoning are verified through a specific example,significantly improving the utilization of assembly process knowledge and the efficiency of assembly process design.展开更多
A new structure of ESKD (expert system based on knowledge discovery system KD (D&K)) is first presented on the basis of KD (D&K)-a synthesized knowledge discovery system based on double-base (database and know...A new structure of ESKD (expert system based on knowledge discovery system KD (D&K)) is first presented on the basis of KD (D&K)-a synthesized knowledge discovery system based on double-base (database and knowledge base) cooperating mechanism. With all new features, ESKD may form a new research direction and provide a great probability for solving the wealth of knowledge in the knowledge base. The general structural frame of ESKD and some sub-systems among ESKD have been described, and the dynamic knowledge base based on double-base cooperating mechanism has been emphased on. According to the result of demonstrative experi- ment, the structure of ESKD is effective and feasible.展开更多
In this paper, the authors outline a formal system for reasoning about agents＇ knowledge in knowledge games-a special type of multi-agent system. Knowledge games are card games where the agents＇ actions involve an e...In this paper, the authors outline a formal system for reasoning about agents＇ knowledge in knowledge games-a special type of multi-agent system. Knowledge games are card games where the agents＇ actions involve an exchange of information with other agents in the game. The authors＇ system is modeled using Coq-a formal proof management system. To the best of the authors＂ knowledge, there are no papers in which knowledge games are considered using a Coq proof assistant. The authors use the dynamic logic of common knowledge, where they particularly focus on the epistemic consequences of epistemic actions carried out by agents. The authors observe the changes in the system that result from such actions. Those changes that can occur in such a system that are of interest to the authors take the form of agents＇ knowledge about the state of the system, knowledge about other agents＇ knowledge, higher-order agents＇ knowledge and so on, up to common knowledge. Besides an axiomatic ofepistemic logic, the authors use a known axiomatization of card games that is extended with some new axioms that are required for the authors＇ approach. Due to a deficit in implementations grounded in theory that enable players to compute their knowledge in any state of the game, the authors show how the authors＇ approach can be used for these purposes.展开更多
Based on dynamic capability theory and legitimacy theory,a theoretical model is constructed to examine how big data capability,through the mediation of knowledge dynamic capability,drive business model innovation unde...Based on dynamic capability theory and legitimacy theory,a theoretical model is constructed to examine how big data capability,through the mediation of knowledge dynamic capability,drive business model innovation under the moderation effect of innovation legitimacy.The eanalys is isconducted using regression analysis and fuzzy set qualitative comparative analysis(fsQCA)on survey data from 302 enterprises that have already implemented big data application practices.The study finds the following four conclusions.(1)Big data capability has a significant positive impact on business model innovation.(2)Dynamic knowledge capability partially mediates the relationship between big data capability and business model innovation.(3)Innovation legitimacy positively influences business model innovation and positively moderates the relationship between big data capability and businessmodel innovation.(4)Through further qualitative comparative analysis,two causal paths that influence business model innovation are identified.展开更多
Radiology report generation is of significant importance.Unlike standard image captioning tasks,radiology report generation faces more pronounced visual and textual biases due to constrained data availability,making i...Radiology report generation is of significant importance.Unlike standard image captioning tasks,radiology report generation faces more pronounced visual and textual biases due to constrained data availability,making it increasingly reliant on prior knowledge in this context.In this paper,we introduce a radiology report generation network termed Dynamics Priori Networks(DPN),which leverages a dynamic knowledge graph and prior knowledge.Concretely,we establish an adaptable graph network and harness both medical domain knowledge and expert insights to enhance the model’s intelligence.Notably,we introduce an image-text contrastive module and an image-text matching module to enhance the quality of the generated results.Our method is evaluated on two widely available datasets:X-ray collection from Indiana University(IU X-ray)and Medical Information Mart for Intensive Care,Chest X-Ray(MIMIC-CXR),where it demonstrates superior performance,particularly excelling in critical metrics.展开更多
文摘In the context of power generation companies, vast amounts of specialized data and expert knowledge have been accumulated. However, challenges such as data silos and fragmented knowledge hinder the effective utilization of this information. This study proposes a novel framework for intelligent Question-and-Answer (Q&A) systems based on Retrieval-Augmented Generation (RAG) to address these issues. The system efficiently acquires domain-specific knowledge by leveraging external databases, including Relational Databases (RDBs) and graph databases, without additional fine-tuning for Large Language Models (LLMs). Crucially, the framework integrates a Dynamic Knowledge Base Updating Mechanism (DKBUM) and a Weighted Context-Aware Similarity (WCAS) method to enhance retrieval accuracy and mitigate inherent limitations of LLMs, such as hallucinations and lack of specialization. Additionally, the proposed DKBUM dynamically adjusts knowledge weights within the database, ensuring that the most recent and relevant information is utilized, while WCAS refines the alignment between queries and knowledge items by enhanced context understanding. Experimental validation demonstrates that the system can generate timely, accurate, and context-sensitive responses, making it a robust solution for managing complex business logic in specialized industries.
文摘Search engines have greatly helped us to find the desired information from the Internet. Most search engines use keywords matching technique. This paper discusses a Dynamic Knowledge Base based Search Engine （DKBSE）, which can expand the user＇s query using the keywords＇ concept or meaning. To do this, the DKBSE needs to construct and maintain the knowledge base dynamically via the system＇s searching results and the user＇s feedback information. The DKBSE expands the user＇s initial query using the knowledge base, and returns the searched information after the expanded query.
基金supported byNationalNatural Science Foundation of China(GrantNos.62071098,U24B20128)Sichuan Science and Technology Program(Grant No.2022YFG0319).
文摘Knowledge graphs often suffer from sparsity and incompleteness.Knowledge graph reasoning is an effective way to address these issues.Unlike static knowledge graph reasoning,which is invariant over time,dynamic knowledge graph reasoning is more challenging due to its temporal nature.In essence,within each time step in a dynamic knowledge graph,there exists structural dependencies among entities and relations,whereas between adjacent time steps,there exists temporal continuity.Based on these structural and temporal characteristics,we propose a model named“DKGR-DR”to learn distributed representations of entities and relations by combining recurrent neural networks and graph neural networks to capture structural dependencies and temporal continuity in DKGs.In addition,we construct a static attribute graph to represent entities’inherent properties.DKGR-DR is capable of modeling both dynamic and static aspects of entities,enabling effective entity prediction and relation prediction.We conduct experiments on ICEWS05-15,ICEWS18,and ICEWS14 to demonstrate that DKGR-DR achieves competitive performance.
基金Supported by National Key Research and Development Program(Grant No.2024YFB3312700)National Natural Science Foundation of China(Grant No.52405541)the Changzhou Municipal Sci&Tech Program(Grant No.CJ20241131)。
文摘Under the paradigm of Industry 5.0,intelligent manufacturing transcends mere efficiency enhancement by emphasizing human-machine collaboration,where human expertise plays a central role in assembly processes.Despite advancements in intelligent and digital technologies,assembly process design still heavily relies on manual knowledge reuse,and inefficiencies and inconsistent quality in process documentation are caused.To address the aforementioned issues,this paper proposes a knowledge push method of complex product assembly process design based on distillation model-based dynamically enhanced graph and Bayesian network.First,an initial knowledge graph is constructed using a BERT-BiLSTM-CRF model trained with integrated human expertise and a fine-tuned large language model.Then,a confidence-based dynamic weighted fusion strategy is employed to achieve dynamic incremental construction of the knowledge graph with low resource consumption.Subsequently,a Bayesian network model is constructed based on the relationships between assembly components,assembly features,and operations.Bayesian network reasoning is used to push assembly process knowledge under different design requirements.Finally,the feasibility of the Bayesian network construction method and the effectiveness of Bayesian network reasoning are verified through a specific example,significantly improving the utilization of assembly process knowledge and the efficiency of assembly process design.
文摘A new structure of ESKD (expert system based on knowledge discovery system KD (D&K)) is first presented on the basis of KD (D&K)-a synthesized knowledge discovery system based on double-base (database and knowledge base) cooperating mechanism. With all new features, ESKD may form a new research direction and provide a great probability for solving the wealth of knowledge in the knowledge base. The general structural frame of ESKD and some sub-systems among ESKD have been described, and the dynamic knowledge base based on double-base cooperating mechanism has been emphased on. According to the result of demonstrative experi- ment, the structure of ESKD is effective and feasible.
文摘In this paper, the authors outline a formal system for reasoning about agents＇ knowledge in knowledge games-a special type of multi-agent system. Knowledge games are card games where the agents＇ actions involve an exchange of information with other agents in the game. The authors＇ system is modeled using Coq-a formal proof management system. To the best of the authors＂ knowledge, there are no papers in which knowledge games are considered using a Coq proof assistant. The authors use the dynamic logic of common knowledge, where they particularly focus on the epistemic consequences of epistemic actions carried out by agents. The authors observe the changes in the system that result from such actions. Those changes that can occur in such a system that are of interest to the authors take the form of agents＇ knowledge about the state of the system, knowledge about other agents＇ knowledge, higher-order agents＇ knowledge and so on, up to common knowledge. Besides an axiomatic ofepistemic logic, the authors use a known axiomatization of card games that is extended with some new axioms that are required for the authors＇ approach. Due to a deficit in implementations grounded in theory that enable players to compute their knowledge in any state of the game, the authors show how the authors＇ approach can be used for these purposes.
基金general project(No.71672080,72072086)of the National Natural ScienceFoundation of China.
文摘Based on dynamic capability theory and legitimacy theory,a theoretical model is constructed to examine how big data capability,through the mediation of knowledge dynamic capability,drive business model innovation under the moderation effect of innovation legitimacy.The eanalys is isconducted using regression analysis and fuzzy set qualitative comparative analysis(fsQCA)on survey data from 302 enterprises that have already implemented big data application practices.The study finds the following four conclusions.(1)Big data capability has a significant positive impact on business model innovation.(2)Dynamic knowledge capability partially mediates the relationship between big data capability and business model innovation.(3)Innovation legitimacy positively influences business model innovation and positively moderates the relationship between big data capability and businessmodel innovation.(4)Through further qualitative comparative analysis,two causal paths that influence business model innovation are identified.
基金supported by the Strategic Priority Research Program of Chinese Academy of Sciences(No.XDB38050100)the Shenzhen Science and Technology Program(No.SGDX20201103095603009)the Shenzhen Polytechnic Research Fund(No.6023310009K).
文摘Radiology report generation is of significant importance.Unlike standard image captioning tasks,radiology report generation faces more pronounced visual and textual biases due to constrained data availability,making it increasingly reliant on prior knowledge in this context.In this paper,we introduce a radiology report generation network termed Dynamics Priori Networks(DPN),which leverages a dynamic knowledge graph and prior knowledge.Concretely,we establish an adaptable graph network and harness both medical domain knowledge and expert insights to enhance the model’s intelligence.Notably,we introduce an image-text contrastive module and an image-text matching module to enhance the quality of the generated results.Our method is evaluated on two widely available datasets:X-ray collection from Indiana University(IU X-ray)and Medical Information Mart for Intensive Care,Chest X-Ray(MIMIC-CXR),where it demonstrates superior performance,particularly excelling in critical metrics.
