Traditional Chinese medicine(TCM)serves as a treasure trove of ancient knowledge,holding a crucial position in the medical field.However,the exploration of TCM's extensive information has been hindered by challeng...Traditional Chinese medicine(TCM)serves as a treasure trove of ancient knowledge,holding a crucial position in the medical field.However,the exploration of TCM's extensive information has been hindered by challenges related to data standardization,completeness,and accuracy,primarily due to the decen-tralized distribution of TCM resources.To address these issues,we developed a platform for TCM knowledge discovery(TCMKD,https://cbcb.cdutcm.edu.cn/TCMKD/).Seven types of data,including syndromes,formulas,Chinese patent drugs(CPDs),Chinese medicinal materials(CMMs),ingredients,targets,and diseases,were manually proofread and consolidated within TCMKD.To strengthen the integration of TCM with modern medicine,TCMKD employs analytical methods such as TCM data mining,enrichment analysis,and network localization and separation.These tools help elucidate the molecular-level commonalities between TCM and contemporary scientific insights.In addition to its analytical capabilities,a quick question and answer(Q&A)system is also embedded within TCMKD to query the database efficiently,thereby improving the interactivity of the platform.The platform also provides a TCM text annotation tool,offering a simple and efficient method for TCM text mining.Overall,TCMKD not only has the potential to become a pivotal repository for TCM,delving into the pharmaco-logical foundations of TCM treatments,but its flexible embedded tools and algorithms can also be applied to the study of other traditional medical systems,extending beyond just TCM.展开更多
Enhancing Autonomous Decision-Making (ADM) for unmanned combat aerial vehicle formations in beyond-visual-range air combat is pivotal for future battlefields, whereas the predominant reinforcement learning technique f...Enhancing Autonomous Decision-Making (ADM) for unmanned combat aerial vehicle formations in beyond-visual-range air combat is pivotal for future battlefields, whereas the predominant reinforcement learning technique for ADM has been proven to be inadequately fitting complex tactical Unit Coordination (UC), limiting the integrity of decision-making for formations. This study proposes a knowledge-enhanced ADM method, with a focus on UC, to elevate formation combat effectiveness. The main innovation is integrating data mining technique with tactical knowledge mining and integration. Foremost, based on Frequent Event Arrangement Mining (FEAM) theory, a cross-channel UC knowledge mining method is designed by introducing data flow, which is capable of capturing dynamic coordinative action sequences. Then, a dual-mode knowledge integration method is proposed by employing the Graph Attention Network (GAT) and attenuated structural similarity, bolstering the interplay between autonomous UC tactics fitting and knowledge injection. The experimental results demonstrate that the algorithm surpasses the existing methods, providing more strategic maneuver trajectories and a win rate of more than 90% in different scenarios. The method is promising to augment the autonomous operational capabilities of unmanned formations and drive the evolution of combat effectiveness.展开更多
As a new data management paradigm,knowledge graphs can integrate multiple data sources and achieve quick responses,reasoning and better predictions in drug discovery.Characterized by powerful contagion and a high rate...As a new data management paradigm,knowledge graphs can integrate multiple data sources and achieve quick responses,reasoning and better predictions in drug discovery.Characterized by powerful contagion and a high rate of morbidity and mortality,porcine reproductive and respiratory syndrome(PRRS)is a common infectious disease in the global swine industry that causes economically great losses.Traditional Chinese medicine(TCM)has advantages in low adverse effects and a relatively affordable cost of application,and TCM is therefore conceived as a possibility to treat PRRS under the current circumstance that there is a lack of safe and effective approaches.Here,we constructed a knowledge graph containing common biomedical data from humans and Sus Scrofa as well as information from thousands of TCMs.Subsequently,we validated the effectiveness of the Sus Scrofa knowledge graph by the t-SNE algorithm and selected the optimal model(i.e.,transR)from six typical models,namely,transE,transR,DistMult,ComplEx,RESCAL and RotatE,according to five indicators,namely,MRR,MR,HITS@1,HITS@3 and HITS@10.Based on embedding vectors trained by the optimal model,anti-PRRSV TCMs were predicted by two paths,namely,VHC-Herb and VHPC-Herb,and potential anti-PRRSVTCMs were identified by retrieving the HERB database according to the phar-macological properties corresponding to symptoms of PRRS.Ultimately,Dan Shen's(Salvia miltiorrhiza Bunge)capacity to resist PRRSV infection was validated by a cell experiment in which the inhibition rate of PRRSV exceeded90%when the concentrations of Dan Shen extract were 0.004,0.008,0.016 and 0.032 mg/mL.In summary,this is the first report on the Sus Scrofa knowledge graph including TCM information,and our study reflects the important application values of deep learning on graphs in the swine industry as well as providing accessible TCM resources for PRRS.展开更多
Recently, high-precision trajectory prediction of ballistic missiles in the boost phase has become a research hotspot. This paper proposes a trajectory prediction algorithm driven by data and knowledge(DKTP) to solve ...Recently, high-precision trajectory prediction of ballistic missiles in the boost phase has become a research hotspot. This paper proposes a trajectory prediction algorithm driven by data and knowledge(DKTP) to solve this problem. Firstly, the complex dynamics characteristics of ballistic missile in the boost phase are analyzed in detail. Secondly, combining the missile dynamics model with the target gravity turning model, a knowledge-driven target three-dimensional turning(T3) model is derived. Then, the BP neural network is used to train the boost phase trajectory database in typical scenarios to obtain a datadriven state parameter mapping(SPM) model. On this basis, an online trajectory prediction framework driven by data and knowledge is established. Based on the SPM model, the three-dimensional turning coefficients of the target are predicted by using the current state of the target, and the state of the target at the next moment is obtained by combining the T3 model. Finally, simulation verification is carried out under various conditions. The simulation results show that the DKTP algorithm combines the advantages of data-driven and knowledge-driven, improves the interpretability of the algorithm, reduces the uncertainty, which can achieve high-precision trajectory prediction of ballistic missile in the boost phase.展开更多
Geologic time is an essential dimension in geological research,acting as a pivotal attribute that integrates data across various subdisciplines.The Geologic Time Scale(GTS)provides a formal framework for interpreting ...Geologic time is an essential dimension in geological research,acting as a pivotal attribute that integrates data across various subdisciplines.The Geologic Time Scale(GTS)provides a formal framework for interpreting and communicating geologic time within the field of geological studies,such as macro-geological evolution and regional geologic surveys.展开更多
With the explosive growth of data available, there is an urgent need to develop continuous data mining which reduces manual interaction evidently. A novel model for data mining is proposed in evolving environment. Fir...With the explosive growth of data available, there is an urgent need to develop continuous data mining which reduces manual interaction evidently. A novel model for data mining is proposed in evolving environment. First, some valid mining task schedules are generated, and then au tonomous and local mining are executed periodically, finally, previous results are merged and refined. The framework based on the model creates a communication mechanism to in corporate domain knowledge into continuous process through ontology service. The local and merge mining are transparent to the end user and heterogeneous data ,source by ontology. Experiments suggest that the framework should be useful in guiding the continuous mining process.展开更多
A decision model of knowledge transfer is presented on the basis of the characteristics of knowledge transfer in a big data environment.This model can determine the weight of knowledge transferred from another enterpr...A decision model of knowledge transfer is presented on the basis of the characteristics of knowledge transfer in a big data environment.This model can determine the weight of knowledge transferred from another enterprise or from a big data provider.Numerous simulation experiments are implemented to test the efficiency of the optimization model.Simulation experiment results show that when increasing the weight of knowledge from big data knowledge provider,the total discount expectation of profits will increase,and the transfer cost will be reduced.The calculated results are in accordance with the actual economic situation.The optimization model can provide useful decision support for enterprises in a big data environment.展开更多
In order to realize the intelligent management of data mining （DM） domain knowledge, this paper presents an architecture for DM knowledge management based on ontology. Using ontology database, this architecture can ...In order to realize the intelligent management of data mining （DM） domain knowledge, this paper presents an architecture for DM knowledge management based on ontology. Using ontology database, this architecture can realize intelligent knowledge retrieval and automatic accomplishment of DM tasks by means of ontology services. Its key features include：①Describing DM ontology and meta-data using ontology based on Web ontology language （OWL）.② Ontology reasoning function. Based on the existing concepts and relations, the hidden knowledge in ontology can be obtained using the reasoning engine. This paper mainly focuses on the construction of DM ontology and the reasoning of DM ontology based on OWL DL（s）.展开更多
Using the advantages of web crawlers in data collection and distributed storage technologies,we accessed to a wealth of forestry-related data.Combined with the mature big data technology at its present stage,Hadoop...Using the advantages of web crawlers in data collection and distributed storage technologies,we accessed to a wealth of forestry-related data.Combined with the mature big data technology at its present stage,Hadoop's distributed system was selected to solve the storage problem of massive forestry big data and the memory-based Spark computing framework to realize real-time and fast processing of data.The forestry data contains a wealth of information,and mining this information is of great significance for guiding the development of forestry.We conducts co-word and cluster analyses on the keywords of forestry data,extracts the rules hidden in the data,analyzes the research hotspots more accurately,grasps the evolution trend of subject topics,and plays an important role in promoting the research and development of subject areas.The co-word analysis and clustering algorithm have important practical significance for the topic structure,research hotspot or development trend in the field of forestry research.Distributed storage framework and parallel computing have greatly improved the performance of data mining algorithms.Therefore,the forestry big data mining system by big data technology has important practical significance for promoting the development of intelligent forestry.展开更多
Deep learning technologies are increasingly used in the fi eld of geophysics,and a variety of algorithms based on shallow convolutional neural networks are more widely used in fault recognition,but these methods are u...Deep learning technologies are increasingly used in the fi eld of geophysics,and a variety of algorithms based on shallow convolutional neural networks are more widely used in fault recognition,but these methods are usually not able to accurately identify complex faults.In this study,using the advantage of deep residual networks to capture strong learning features,we introduce residual blocks to replace all convolutional layers of the three-dimensional(3D)UNet to build a new 3D Res-UNet and select appropriate parameters through experiments to train a large amount of synthesized seismic data.After the training is completed,we introduce the mechanism of knowledge distillation.First,we treat the 3D Res-UNet as a teacher network and then train the 3D Res-UNet as a student network;in this process,the teacher network is in evaluation mode.Finally,we calculate the mixed loss function by combining the teacher model and student network to learn more fault information,improve the performance of the network,and optimize the fault recognition eff ect.The quantitative evaluation result of the synthetic model test proves that the 3D Res-UNet can considerably improve the accuracy of fault recognition from 0.956 to 0.993 after knowledge distillation,and the eff ectiveness and feasibility of our method can be verifi ed based on the application of actual seismic data.展开更多
This study proposes the establishment of a knowledge-system ontology in the nursing field. It uses advanced data mining techniques,digital publishing technologies, and new media concepts to comprehensively integrate a...This study proposes the establishment of a knowledge-system ontology in the nursing field. It uses advanced data mining techniques,digital publishing technologies, and new media concepts to comprehensively integrate and deepen nursing knowledge and to aggregate sources of knowledge in specialized technical fields. This study applies all forms of media and transmission channels, such as personal computers and mobile devices, to establish a knowledge-transmission system that provides knowledge services such as knowledge search, update retrieval, evaluation, questions and answers(Q&As), online viewing, information subscription, expert services, push notifications, review forums, and online learning. In doing so, this study creates an authoritative and foundational knowledge service engine for the nursing field, which provides convenient, flexible, and comprehensive knowledge services to members of the nursing industry in a digital format.展开更多
Quality management is a constant and significant concern in enterprises.Effective determination of correct solutions for comprehensive problems helps avoid increased backtesting costs.This study proposes an intelligen...Quality management is a constant and significant concern in enterprises.Effective determination of correct solutions for comprehensive problems helps avoid increased backtesting costs.This study proposes an intelligent quality control method for manufacturing processes based on a human–cyber–physical(HCP)knowledge graph,which is a systematic method that encompasses the following elements:data management and classification based on HCP ternary data,HCP ontology construction,knowledge extraction for constructing an HCP knowledge graph,and comprehensive application of quality control based on HCP knowledge.The proposed method implements case retrieval,automatic analysis,and assisted decision making based on an HCP knowledge graph,enabling quality monitoring,inspection,diagnosis,and maintenance strategies for quality control.In practical applications,the proposed modular and hierarchical HCP ontology exhibits significant superiority in terms of shareability and reusability of the acquired knowledge.Moreover,the HCP knowledge graph deeply integrates the provided HCP data and effectively supports comprehensive decision making.The proposed method was implemented in cases involving an automotive production line and a gear manufacturing process,and the effectiveness of the method was verified by the application system deployed.Furthermore,the proposed method can be extended to other manufacturing process quality control tasks.展开更多
In the big data environment, enterprises must constantly assimilate big dataknowledge and private knowledge by multiple knowledge transfers to maintain theircompetitive advantage. The optimal time of knowledge transfe...In the big data environment, enterprises must constantly assimilate big dataknowledge and private knowledge by multiple knowledge transfers to maintain theircompetitive advantage. The optimal time of knowledge transfer is one of the mostimportant aspects to improve knowledge transfer efficiency. Based on the analysis of thecomplex characteristics of knowledge transfer in the big data environment, multipleknowledge transfers can be divided into two categories. One is the simultaneous transferof various types of knowledge, and the other one is multiple knowledge transfers atdifferent time points. Taking into consideration the influential factors, such as theknowledge type, knowledge structure, knowledge absorptive capacity, knowledge updaterate, discount rate, market share, profit contributions of each type of knowledge, transfercosts, product life cycle and so on, time optimization models of multiple knowledgetransfers in the big data environment are presented by maximizing the total discountedexpected profits (DEPs) of an enterprise. Some simulation experiments have beenperformed to verify the validity of the models, and the models can help enterprisesdetermine the optimal time of multiple knowledge transfer in the big data environment.展开更多
On the platform of the Deep-time Digital Earth Program(DDE),sedimentary data are essential for achieving its scientific objectives.These data will take stratigraphic units as their core data carrier,for quantitative o...On the platform of the Deep-time Digital Earth Program(DDE),sedimentary data are essential for achieving its scientific objectives.These data will take stratigraphic units as their core data carrier,for quantitative or qualitative data analysis.The DDE Sedimentary Data Group is responsible for the management of the sedimentary data on the DDE platform and has now developed into a group of nearly 40 disciplinary experts.展开更多
Computational techniques have been adopted in medi-cal and biological systems for a long time. There is no doubt that the development and application of computational methods will render great help in better understan...Computational techniques have been adopted in medi-cal and biological systems for a long time. There is no doubt that the development and application of computational methods will render great help in better understanding biomedical and biological functions. Large amounts of datasets have been produced by biomedical and biological experiments and simulations. In order for researchers to gain knowledge from origi- nal data, nontrivial transformation is necessary, which is regarded as a critical link in the chain of knowledge acquisition, sharing, and reuse. Challenges that have been encountered include: how to efficiently and effectively represent human knowledge in formal computing models, how to take advantage of semantic text mining techniques rather than traditional syntactic text mining, and how to handle security issues during the knowledge sharing and reuse. This paper summarizes the state-of-the-art in these research directions. We aim to provide readers with an introduction of major computing themes to be applied to the medical and biological research.展开更多
The rapid increase in the publication of knowledge bases as linked open data （LOD） warrants serious consideration from all concerned, as this phenomenon will potentially scale exponentially. This paper will briefly ...The rapid increase in the publication of knowledge bases as linked open data （LOD） warrants serious consideration from all concerned, as this phenomenon will potentially scale exponentially. This paper will briefly describe the evolution of the LOD, the emerging world-wide semantic web （WWSW）, and explore the scalability and performance features Of the service oriented architecture that forms the foundation of the semantic technology platform developed at MIMOS Bhd., for addressing the challenges posed by the intelligent future internet. This paper＂ concludes with a review of the current status of the agriculture linked open data.展开更多
Multiple efforts have been performed worldwide around diverse aspects of land administra-tion.However,land administration data and systems’notorious heterogeneity remains a longstanding challenge to develop a harmoni...Multiple efforts have been performed worldwide around diverse aspects of land administra-tion.However,land administration data and systems’notorious heterogeneity remains a longstanding challenge to develop a harmonized vision.In this sense,the traditional Spatial Data Infrastructures adoption is not enough to overcome this challenge since data sources’heterogeneity implies needs related to harmonization interoperability,sharing,and integration in land administration development.This paper proposes a graph-based represen-tation of knowledge for integrating multiple and heterogeneous data sources(tables,shape-files,geodatabases,and WFS services)belonging to two Colombian agencies within a decentralized land administration scenario.These knowledge graphs are developed on an ontology-based knowledge representation using national and international standards for land administration.Our approach aims to prevent data isolation,enable cross-datasets integration,accomplish machine-processable data,and facilitate the reuse and exploitation of multi-jurisdictional datasets in a single approach.A real case study demonstrates the applicability of the land administration data cycle deployed.展开更多
Big data knowledge,such as customer demands and consumer preferences,is among the crucial external knowledge that firms need for new product development in the big data environment.Prior research has focused on the pr...Big data knowledge,such as customer demands and consumer preferences,is among the crucial external knowledge that firms need for new product development in the big data environment.Prior research has focused on the profit of big data knowledge providers rather than the profit and pricing schemes of knowledge recipients.This research addresses this theoretical gap and uses theoretical and numerical analysis to compare the profitability of two pricing schemes commonly used by knowledge recipients:subscription pricing and pay-per-use pricing.We find that:(1)the subscription price of big data knowledge has no effect on the optimal time of knowledge transaction in the same pricing scheme,but the usage ratio of the big data knowledge affects the optimal time of knowledge transaction,and the smaller the usage ratio of big data knowledge the earlier the big data knowledge transaction conducts;(2)big data knowledge with a higher update rate can bring greater profits to the firm both in subscription pricing scheme and pay-per-use pricing scheme;(3)a knowledge recipient will choose the knowledge that can bring a higher market share growth rate regardless of what price scheme it adopts,and firms can choose more efficient knowledge in the pay-per-use pricing scheme by adjusting the usage ratio of knowledge usage according to their economic conditions.The model and findings in this paper can help knowledge recipient firms select optimal pricing method and enhance future new product development performance.展开更多
To improve the efficiency of the attribute reduction, we present an attribute reduction algorithm based on background knowledge and information entropy by making use of background knowledge from research fields. Under...To improve the efficiency of the attribute reduction, we present an attribute reduction algorithm based on background knowledge and information entropy by making use of background knowledge from research fields. Under the condition of known background knowledge, the algorithm can not only greatly improve the efficiency of attribute reduction, but also avoid the defection of information entropy partial to attribute with much value. The experimental result verifies that the algorithm is effective. In the end, the algorithm produces better results when applied in the classification of the star spectra data.展开更多
基金supported by Natural Science Foundation of Sichuan,China(Grant No.:2024ZDZX0019).
文摘Traditional Chinese medicine(TCM)serves as a treasure trove of ancient knowledge,holding a crucial position in the medical field.However,the exploration of TCM's extensive information has been hindered by challenges related to data standardization,completeness,and accuracy,primarily due to the decen-tralized distribution of TCM resources.To address these issues,we developed a platform for TCM knowledge discovery(TCMKD,https://cbcb.cdutcm.edu.cn/TCMKD/).Seven types of data,including syndromes,formulas,Chinese patent drugs(CPDs),Chinese medicinal materials(CMMs),ingredients,targets,and diseases,were manually proofread and consolidated within TCMKD.To strengthen the integration of TCM with modern medicine,TCMKD employs analytical methods such as TCM data mining,enrichment analysis,and network localization and separation.These tools help elucidate the molecular-level commonalities between TCM and contemporary scientific insights.In addition to its analytical capabilities,a quick question and answer(Q&A)system is also embedded within TCMKD to query the database efficiently,thereby improving the interactivity of the platform.The platform also provides a TCM text annotation tool,offering a simple and efficient method for TCM text mining.Overall,TCMKD not only has the potential to become a pivotal repository for TCM,delving into the pharmaco-logical foundations of TCM treatments,but its flexible embedded tools and algorithms can also be applied to the study of other traditional medical systems,extending beyond just TCM.
文摘Enhancing Autonomous Decision-Making (ADM) for unmanned combat aerial vehicle formations in beyond-visual-range air combat is pivotal for future battlefields, whereas the predominant reinforcement learning technique for ADM has been proven to be inadequately fitting complex tactical Unit Coordination (UC), limiting the integrity of decision-making for formations. This study proposes a knowledge-enhanced ADM method, with a focus on UC, to elevate formation combat effectiveness. The main innovation is integrating data mining technique with tactical knowledge mining and integration. Foremost, based on Frequent Event Arrangement Mining (FEAM) theory, a cross-channel UC knowledge mining method is designed by introducing data flow, which is capable of capturing dynamic coordinative action sequences. Then, a dual-mode knowledge integration method is proposed by employing the Graph Attention Network (GAT) and attenuated structural similarity, bolstering the interplay between autonomous UC tactics fitting and knowledge injection. The experimental results demonstrate that the algorithm surpasses the existing methods, providing more strategic maneuver trajectories and a win rate of more than 90% in different scenarios. The method is promising to augment the autonomous operational capabilities of unmanned formations and drive the evolution of combat effectiveness.
基金supported by the China Fundamental Research Funds for the Central Universities(No.2662022XXYJ001,2662022JC004,2662023XXPY005)。
文摘As a new data management paradigm,knowledge graphs can integrate multiple data sources and achieve quick responses,reasoning and better predictions in drug discovery.Characterized by powerful contagion and a high rate of morbidity and mortality,porcine reproductive and respiratory syndrome(PRRS)is a common infectious disease in the global swine industry that causes economically great losses.Traditional Chinese medicine(TCM)has advantages in low adverse effects and a relatively affordable cost of application,and TCM is therefore conceived as a possibility to treat PRRS under the current circumstance that there is a lack of safe and effective approaches.Here,we constructed a knowledge graph containing common biomedical data from humans and Sus Scrofa as well as information from thousands of TCMs.Subsequently,we validated the effectiveness of the Sus Scrofa knowledge graph by the t-SNE algorithm and selected the optimal model(i.e.,transR)from six typical models,namely,transE,transR,DistMult,ComplEx,RESCAL and RotatE,according to five indicators,namely,MRR,MR,HITS@1,HITS@3 and HITS@10.Based on embedding vectors trained by the optimal model,anti-PRRSV TCMs were predicted by two paths,namely,VHC-Herb and VHPC-Herb,and potential anti-PRRSVTCMs were identified by retrieving the HERB database according to the phar-macological properties corresponding to symptoms of PRRS.Ultimately,Dan Shen's(Salvia miltiorrhiza Bunge)capacity to resist PRRSV infection was validated by a cell experiment in which the inhibition rate of PRRSV exceeded90%when the concentrations of Dan Shen extract were 0.004,0.008,0.016 and 0.032 mg/mL.In summary,this is the first report on the Sus Scrofa knowledge graph including TCM information,and our study reflects the important application values of deep learning on graphs in the swine industry as well as providing accessible TCM resources for PRRS.
基金the National Natural Science Foundation of China (Grants No. 12072090 and No.12302056) to provide fund for conducting experiments。
文摘Recently, high-precision trajectory prediction of ballistic missiles in the boost phase has become a research hotspot. This paper proposes a trajectory prediction algorithm driven by data and knowledge(DKTP) to solve this problem. Firstly, the complex dynamics characteristics of ballistic missile in the boost phase are analyzed in detail. Secondly, combining the missile dynamics model with the target gravity turning model, a knowledge-driven target three-dimensional turning(T3) model is derived. Then, the BP neural network is used to train the boost phase trajectory database in typical scenarios to obtain a datadriven state parameter mapping(SPM) model. On this basis, an online trajectory prediction framework driven by data and knowledge is established. Based on the SPM model, the three-dimensional turning coefficients of the target are predicted by using the current state of the target, and the state of the target at the next moment is obtained by combining the T3 model. Finally, simulation verification is carried out under various conditions. The simulation results show that the DKTP algorithm combines the advantages of data-driven and knowledge-driven, improves the interpretability of the algorithm, reduces the uncertainty, which can achieve high-precision trajectory prediction of ballistic missile in the boost phase.
基金supported by the Deep Earth Probe and Mineral Resources Exploration-National Science and Technology Major Project of China(Grant number:2024ZD1001105)National Natural Science Foundation of China(Grant number:42488201).
文摘Geologic time is an essential dimension in geological research,acting as a pivotal attribute that integrates data across various subdisciplines.The Geologic Time Scale(GTS)provides a formal framework for interpreting and communicating geologic time within the field of geological studies,such as macro-geological evolution and regional geologic surveys.
基金Supported by the National Natural Science Foun-dation of China (60173058 ,70372024)
文摘With the explosive growth of data available, there is an urgent need to develop continuous data mining which reduces manual interaction evidently. A novel model for data mining is proposed in evolving environment. First, some valid mining task schedules are generated, and then au tonomous and local mining are executed periodically, finally, previous results are merged and refined. The framework based on the model creates a communication mechanism to in corporate domain knowledge into continuous process through ontology service. The local and merge mining are transparent to the end user and heterogeneous data ,source by ontology. Experiments suggest that the framework should be useful in guiding the continuous mining process.
基金supported by NSFC(Grant No.71373032)the Natural Science Foundation of Hunan Province(Grant No.12JJ4073)+3 种基金the Scientific Research Fund of Hunan Provincial Education Department(Grant No.11C0029)the Educational Economy and Financial Research Base of Hunan Province(Grant No.13JCJA2)the Project of China Scholarship Council for Overseas Studies(201208430233201508430121)
文摘A decision model of knowledge transfer is presented on the basis of the characteristics of knowledge transfer in a big data environment.This model can determine the weight of knowledge transferred from another enterprise or from a big data provider.Numerous simulation experiments are implemented to test the efficiency of the optimization model.Simulation experiment results show that when increasing the weight of knowledge from big data knowledge provider,the total discount expectation of profits will increase,and the transfer cost will be reduced.The calculated results are in accordance with the actual economic situation.The optimization model can provide useful decision support for enterprises in a big data environment.
基金the Natural Science Foundation of Chongqing (CSTC2005BB2190)
文摘In order to realize the intelligent management of data mining （DM） domain knowledge, this paper presents an architecture for DM knowledge management based on ontology. Using ontology database, this architecture can realize intelligent knowledge retrieval and automatic accomplishment of DM tasks by means of ontology services. Its key features include：①Describing DM ontology and meta-data using ontology based on Web ontology language （OWL）.② Ontology reasoning function. Based on the existing concepts and relations, the hidden knowledge in ontology can be obtained using the reasoning engine. This paper mainly focuses on the construction of DM ontology and the reasoning of DM ontology based on OWL DL（s）.
基金grants from the Fundamental Research Funds for the Central Universities(Grant No.2572018BH02)Special Funds for Scientific Research in the Forestry Public Welfare Industry(Grant Nos.201504307-03)。
文摘Using the advantages of web crawlers in data collection and distributed storage technologies,we accessed to a wealth of forestry-related data.Combined with the mature big data technology at its present stage,Hadoop's distributed system was selected to solve the storage problem of massive forestry big data and the memory-based Spark computing framework to realize real-time and fast processing of data.The forestry data contains a wealth of information,and mining this information is of great significance for guiding the development of forestry.We conducts co-word and cluster analyses on the keywords of forestry data,extracts the rules hidden in the data,analyzes the research hotspots more accurately,grasps the evolution trend of subject topics,and plays an important role in promoting the research and development of subject areas.The co-word analysis and clustering algorithm have important practical significance for the topic structure,research hotspot or development trend in the field of forestry research.Distributed storage framework and parallel computing have greatly improved the performance of data mining algorithms.Therefore,the forestry big data mining system by big data technology has important practical significance for promoting the development of intelligent forestry.
基金supported by the National Natural Science Foundation of China(No.42072169)。
文摘Deep learning technologies are increasingly used in the fi eld of geophysics,and a variety of algorithms based on shallow convolutional neural networks are more widely used in fault recognition,but these methods are usually not able to accurately identify complex faults.In this study,using the advantage of deep residual networks to capture strong learning features,we introduce residual blocks to replace all convolutional layers of the three-dimensional(3D)UNet to build a new 3D Res-UNet and select appropriate parameters through experiments to train a large amount of synthesized seismic data.After the training is completed,we introduce the mechanism of knowledge distillation.First,we treat the 3D Res-UNet as a teacher network and then train the 3D Res-UNet as a student network;in this process,the teacher network is in evaluation mode.Finally,we calculate the mixed loss function by combining the teacher model and student network to learn more fault information,improve the performance of the network,and optimize the fault recognition eff ect.The quantitative evaluation result of the synthetic model test proves that the 3D Res-UNet can considerably improve the accuracy of fault recognition from 0.956 to 0.993 after knowledge distillation,and the eff ectiveness and feasibility of our method can be verifi ed based on the application of actual seismic data.
基金supported by National Natural Science Foundation of China(No.71573162)Shanxi Province Soft Science Research Program(No.2018041029-3)
文摘This study proposes the establishment of a knowledge-system ontology in the nursing field. It uses advanced data mining techniques,digital publishing technologies, and new media concepts to comprehensively integrate and deepen nursing knowledge and to aggregate sources of knowledge in specialized technical fields. This study applies all forms of media and transmission channels, such as personal computers and mobile devices, to establish a knowledge-transmission system that provides knowledge services such as knowledge search, update retrieval, evaluation, questions and answers(Q&As), online viewing, information subscription, expert services, push notifications, review forums, and online learning. In doing so, this study creates an authoritative and foundational knowledge service engine for the nursing field, which provides convenient, flexible, and comprehensive knowledge services to members of the nursing industry in a digital format.
基金supported by the National Science and Technology Innovation 2030 of China Next-Generation Artificial Intelligence Major Project(2018AAA0101800)the National Natural Science Foundation of China(52375482)the Regional Innovation Cooperation Project of Sichuan Province(2023YFQ0019).
文摘Quality management is a constant and significant concern in enterprises.Effective determination of correct solutions for comprehensive problems helps avoid increased backtesting costs.This study proposes an intelligent quality control method for manufacturing processes based on a human–cyber–physical(HCP)knowledge graph,which is a systematic method that encompasses the following elements:data management and classification based on HCP ternary data,HCP ontology construction,knowledge extraction for constructing an HCP knowledge graph,and comprehensive application of quality control based on HCP knowledge.The proposed method implements case retrieval,automatic analysis,and assisted decision making based on an HCP knowledge graph,enabling quality monitoring,inspection,diagnosis,and maintenance strategies for quality control.In practical applications,the proposed modular and hierarchical HCP ontology exhibits significant superiority in terms of shareability and reusability of the acquired knowledge.Moreover,the HCP knowledge graph deeply integrates the provided HCP data and effectively supports comprehensive decision making.The proposed method was implemented in cases involving an automotive production line and a gear manufacturing process,and the effectiveness of the method was verified by the application system deployed.Furthermore,the proposed method can be extended to other manufacturing process quality control tasks.
基金supported by the National Natural Science Foundation ofChina (Grant No. 71704016,71331008, 71402010)the Natural Science Foundation of HunanProvince (Grant No. 2017JJ2267)+1 种基金the Educational Economy and Financial Research Base ofHunan Province (Grant No. 13JCJA2)the Project of China Scholarship Council forOverseas Studies (201508430121, 201208430233).
文摘In the big data environment, enterprises must constantly assimilate big dataknowledge and private knowledge by multiple knowledge transfers to maintain theircompetitive advantage. The optimal time of knowledge transfer is one of the mostimportant aspects to improve knowledge transfer efficiency. Based on the analysis of thecomplex characteristics of knowledge transfer in the big data environment, multipleknowledge transfers can be divided into two categories. One is the simultaneous transferof various types of knowledge, and the other one is multiple knowledge transfers atdifferent time points. Taking into consideration the influential factors, such as theknowledge type, knowledge structure, knowledge absorptive capacity, knowledge updaterate, discount rate, market share, profit contributions of each type of knowledge, transfercosts, product life cycle and so on, time optimization models of multiple knowledgetransfers in the big data environment are presented by maximizing the total discountedexpected profits (DEPs) of an enterprise. Some simulation experiments have beenperformed to verify the validity of the models, and the models can help enterprisesdetermine the optimal time of multiple knowledge transfer in the big data environment.
文摘On the platform of the Deep-time Digital Earth Program(DDE),sedimentary data are essential for achieving its scientific objectives.These data will take stratigraphic units as their core data carrier,for quantitative or qualitative data analysis.The DDE Sedimentary Data Group is responsible for the management of the sedimentary data on the DDE platform and has now developed into a group of nearly 40 disciplinary experts.
文摘Computational techniques have been adopted in medi-cal and biological systems for a long time. There is no doubt that the development and application of computational methods will render great help in better understanding biomedical and biological functions. Large amounts of datasets have been produced by biomedical and biological experiments and simulations. In order for researchers to gain knowledge from origi- nal data, nontrivial transformation is necessary, which is regarded as a critical link in the chain of knowledge acquisition, sharing, and reuse. Challenges that have been encountered include: how to efficiently and effectively represent human knowledge in formal computing models, how to take advantage of semantic text mining techniques rather than traditional syntactic text mining, and how to handle security issues during the knowledge sharing and reuse. This paper summarizes the state-of-the-art in these research directions. We aim to provide readers with an introduction of major computing themes to be applied to the medical and biological research.
文摘The rapid increase in the publication of knowledge bases as linked open data （LOD） warrants serious consideration from all concerned, as this phenomenon will potentially scale exponentially. This paper will briefly describe the evolution of the LOD, the emerging world-wide semantic web （WWSW）, and explore the scalability and performance features Of the service oriented architecture that forms the foundation of the semantic technology platform developed at MIMOS Bhd., for addressing the challenges posed by the intelligent future internet. This paper＂ concludes with a review of the current status of the agriculture linked open data.
基金supported by Colfuturo and Ministerio de Tecnologías de la Información y las Comunicaciones de Colombia,CYTED program-520RT0010[Red GeoLIBERO-Consolidación de una red de geomática libre aplicada a las necesidades de Iberoamérica],and SIP-IPN 20210677[Generación de grafos de conocimiento sobre eventos meteorológicos urbanos].
文摘Multiple efforts have been performed worldwide around diverse aspects of land administra-tion.However,land administration data and systems’notorious heterogeneity remains a longstanding challenge to develop a harmonized vision.In this sense,the traditional Spatial Data Infrastructures adoption is not enough to overcome this challenge since data sources’heterogeneity implies needs related to harmonization interoperability,sharing,and integration in land administration development.This paper proposes a graph-based represen-tation of knowledge for integrating multiple and heterogeneous data sources(tables,shape-files,geodatabases,and WFS services)belonging to two Colombian agencies within a decentralized land administration scenario.These knowledge graphs are developed on an ontology-based knowledge representation using national and international standards for land administration.Our approach aims to prevent data isolation,enable cross-datasets integration,accomplish machine-processable data,and facilitate the reuse and exploitation of multi-jurisdictional datasets in a single approach.A real case study demonstrates the applicability of the land administration data cycle deployed.
基金This research was funded by(the National Natural Science Foundation of China)Grant Number(71704016),(the Key Scientific Research Fund of Hunan Provincial Education Department of China)Grant Number(19A006),and(the Enterprise Strategic Management and Investment Decision Research Base of Hunan Province)Grant Number(19qyzd03).
文摘Big data knowledge,such as customer demands and consumer preferences,is among the crucial external knowledge that firms need for new product development in the big data environment.Prior research has focused on the profit of big data knowledge providers rather than the profit and pricing schemes of knowledge recipients.This research addresses this theoretical gap and uses theoretical and numerical analysis to compare the profitability of two pricing schemes commonly used by knowledge recipients:subscription pricing and pay-per-use pricing.We find that:(1)the subscription price of big data knowledge has no effect on the optimal time of knowledge transaction in the same pricing scheme,but the usage ratio of the big data knowledge affects the optimal time of knowledge transaction,and the smaller the usage ratio of big data knowledge the earlier the big data knowledge transaction conducts;(2)big data knowledge with a higher update rate can bring greater profits to the firm both in subscription pricing scheme and pay-per-use pricing scheme;(3)a knowledge recipient will choose the knowledge that can bring a higher market share growth rate regardless of what price scheme it adopts,and firms can choose more efficient knowledge in the pay-per-use pricing scheme by adjusting the usage ratio of knowledge usage according to their economic conditions.The model and findings in this paper can help knowledge recipient firms select optimal pricing method and enhance future new product development performance.
基金Supported by the National Natural Science Foundation of China（No. 60573075）, the National High Technology Research and Development Program of China （No. 2003AA133060） and the Natural Science Foundation of Shanxi Province （No. 200601104）.
文摘To improve the efficiency of the attribute reduction, we present an attribute reduction algorithm based on background knowledge and information entropy by making use of background knowledge from research fields. Under the condition of known background knowledge, the algorithm can not only greatly improve the efficiency of attribute reduction, but also avoid the defection of information entropy partial to attribute with much value. The experimental result verifies that the algorithm is effective. In the end, the algorithm produces better results when applied in the classification of the star spectra data.
