Knowledge graph(KG)serves as a specialized semantic network that encapsulates intricate relationships among real-world entities within a structured framework.This framework facilitates a transformation in information ...Knowledge graph(KG)serves as a specialized semantic network that encapsulates intricate relationships among real-world entities within a structured framework.This framework facilitates a transformation in information retrieval,transitioning it from mere string matching to far more sophisticated entity matching.In this transformative process,the advancement of artificial intelligence and intelligent information services is invigorated.Meanwhile,the role ofmachine learningmethod in the construction of KG is important,and these techniques have already achieved initial success.This article embarks on a comprehensive journey through the last strides in the field of KG via machine learning.With a profound amalgamation of cutting-edge research in machine learning,this article undertakes a systematical exploration of KG construction methods in three distinct phases:entity learning,ontology learning,and knowledge reasoning.Especially,a meticulous dissection of machine learningdriven algorithms is conducted,spotlighting their contributions to critical facets such as entity extraction,relation extraction,entity linking,and link prediction.Moreover,this article also provides an analysis of the unresolved challenges and emerging trajectories that beckon within the expansive application of machine learning-fueled,large-scale KG construction.展开更多
Syndrome differentiation is the core diagnosis method of Traditional Chinese Medicine(TCM).We propose a method that simulates syndrome differentiation through deductive reasoning on a knowledge graph to achieve automa...Syndrome differentiation is the core diagnosis method of Traditional Chinese Medicine(TCM).We propose a method that simulates syndrome differentiation through deductive reasoning on a knowledge graph to achieve automated diagnosis in TCM.We analyze the reasoning path patterns from symptom to syndromes on the knowledge graph.There are two kinds of path patterns in the knowledge graph:one-hop and two-hop.The one-hop path pattern maps the symptom to syndromes immediately.The two-hop path pattern maps the symptom to syndromes through the nature of disease,etiology,and pathomechanism to support the diagnostic reasoning.Considering the different support strengths for the knowledge paths in reasoning,we design a dynamic weight mechanism.We utilize Naïve Bayes and TF-IDF to implement the reasoning method and the weighted score calculation.The proposed method reasons the syndrome results by calculating the possibility according to the weighted score of the path in the knowledge graph based on the reasoning path patterns.We evaluate the method with clinical records and clinical practice in hospitals.The preliminary results suggest that the method achieves high performance and can help TCM doctors make better diagnosis decisions in practice.Meanwhile,the method is robust and explainable under the guide of the knowledge graph.It could help TCM physicians,especially primary physicians in rural areas,and provide clinical decision support in clinical practice.展开更多
Biological inspirations are good design mimicry resources. This paper proposes a function based approach for modeling and transformation of bio-inspiration design knowledge. A general functional modeling method for bi...Biological inspirations are good design mimicry resources. This paper proposes a function based approach for modeling and transformation of bio-inspiration design knowledge. A general functional modeling method for biological domain and engineering domain design knowledge is introduced. Functional similarity based bio-inspiration transformation between biological domain and engineering domain is proposed. The biological function topology transfer and analog solution recomposition are also discussed in this paper.展开更多
As one of the most valuable assets in China,traditional medicine has a long history and contains pieces of knowledge.The diagnosis and treatment of Traditional Chinese Medicine(TCM)has benefited from the natural langu...As one of the most valuable assets in China,traditional medicine has a long history and contains pieces of knowledge.The diagnosis and treatment of Traditional Chinese Medicine(TCM)has benefited from the natural language processing technology.This paper proposes a knowledge-based syndrome reasoning method in computer-assisted diagnosis.This method is based on the established knowledge graph of TCM and this paper introduces the reinforcement learning algorithm to mine the hidden relationship among the entities and obtain the reasoning path.According to this reasoning path,we could infer the path from the symptoms to the syndrome and get all possibilities via the relationship between symptoms and causes.Moreover,this study applies the Term Frequency-Inverse Document Frequency(TF-IDF)idea to the computer-assisted diagnosis of TCM for the score of syndrome calculation.Finally,combined with symptoms,syndrome,and causes,the disease could be confirmed comprehensively by voting,and the experiment shows that the system can help doctors and families to disease diagnosis effectively.展开更多
With the increasing number of applications and services online, more and more business processes are carried out in a fully integrated digital working environment. To ensure the success of a business and the smart cap...With the increasing number of applications and services online, more and more business processes are carried out in a fully integrated digital working environment. To ensure the success of a business and the smart capture and reuse of organizational knowledge, adequate recordkeeping is essential. A review of the related literature indicates that international trends and future directions of recordkeeping awareness, recordkeeping processes and regimes, recordkeeping systems and technologies are moving towards meta-synthetic support in a digital environment at strategic level. However, little has been discussed about specific strategies of meta-synthetic support for digital recordkeeping, and in turn, organizational knowledge. The purpose of this paper is to propose the adoption and adaptation of the ISO/TC 46/SC 11 series of standards and the integrated use of ISO management systems standards （MSSs） as enablers of meta-synthetic strategies to enhance organizational performance and accountability through sustainable digital recordkeeping. The integrated use of ISO MSSs provides ever increasing opportunities for integrated product, process, service, and compliance control. The mechanisms of the above meta-synthetic strategies are collaboration, optimization, innovation, and compliance. The theoretical foundations are the integrated use of life-cycle, continuum and ecosystem theories with multidisciplinary perspectives. The practical outcomes are the support of varied evidence-based collaborations at multiple levels, such as national strategies and plans for digital continuity, knowledge sharing and reuse, open government initiatives, e-government information architecture and services competence building, and enterprise information governance.展开更多
It is common in industrial construction projects for data to be collected and discarded without being analyzed to extract useful knowledge. A proposed integrated methodology based on a five-step Knowledge Discovery in...It is common in industrial construction projects for data to be collected and discarded without being analyzed to extract useful knowledge. A proposed integrated methodology based on a five-step Knowledge Discovery in Data (KDD) model was developed to address this issue. The framework transfers existing multidimensional historical data from completed projects into useful knowledge for future projects. The model starts by understanding the problem domain, industrial construction projects. The second step is analyzing the problem data and its multiple dimensions. The target dataset is the labour resources data generated while managing industrial construction projects. The next step is developing the data collection model and prototype data ware-house. The data warehouse stores collected data in a ready-for-mining format and produces dynamic On Line Analytical Processing (OLAP) reports and graphs. Data was collected from a large western-Canadian structural steel fabricator to prove the applicability of the developed methodology. The proposed framework was applied to three different case studies to validate the applicability of the developed framework to real projects data.展开更多
Despite of the fact that knowledge management has become the focus of current literature and management practice, the core process of knowledge has. not been identified. After comparing the pl：ocess of knowledge move...Despite of the fact that knowledge management has become the focus of current literature and management practice, the core process of knowledge has. not been identified. After comparing the pl：ocess of knowledge movement and that of fermenting, we put forward a new model-knowledge fermenting model. In this paper, we thoroughly analyze the element of knowledge fermenting model, and show how knowledge increase is realized through that model.展开更多
Background: Tuberculosis (TB) is one of the top 10 causes of death worldwide. India is still the highest TB burden country. There is a scarcity of data on TB knowledge from Rajasthan state of India. Objective: The obj...Background: Tuberculosis (TB) is one of the top 10 causes of death worldwide. India is still the highest TB burden country. There is a scarcity of data on TB knowledge from Rajasthan state of India. Objective: The objective of this study was to estimate the prevalence of knowledge about TB and services of TB control programme and to determine its correlates among rural population of Jaipur, Rajasthan. Methods: Cross-sectional community based study was carried out at Model Rural Health Research Unit, Jaipur, a unit of Department of Health Research, Ministry of Health & Family Welfare, Government of India. Results: Study reports the result from 1993 adult participants from 10 villages of 2 sub-districts of district Jaipur. About 88.9% of studied participants knew that TB is an infectious disease and it spreads from TB patient to healthy person in close contact. Only 22.3% of participants knew “DOTS is the treatment for TB”. While, only 58.9% knew “sputum is used for diagnosis of TB” at health centers. Scheduled castes, scheduled tribes and backward classes social groups knew less than the mainstream “General” social group. The observed difference was statistically significant (p 0.05). Logistic regression analysis estimated the relative contribution to knowledge status. Conclusion: The knowledge of study participants on transmission of tuberculosis was similar to knowledge of population in country wide study. They poorly knew sputum is used for diagnosing tuberculosis disease;socio-demographic inequity exists in this knowledge too. People from older age groups, underprivileged social groups and minority need extra educational activities.展开更多
During the procedure of fault diagnosis for large-scale complicated equipment, the existence of redundant and fuzzy information results in the difficulty of knowledge access. Aiming at this characteristic, this paper ...During the procedure of fault diagnosis for large-scale complicated equipment, the existence of redundant and fuzzy information results in the difficulty of knowledge access. Aiming at this characteristic, this paper brought forth the Rough Set （RS） theory to the field of fault diagnosis. By means of the RS theory which is predominant in the way of dealing with fuzzy and uncertain information, knowledge access about fault diagnosis was realized. The foundation ideology of the RS theory was exhausted in detail, an amended RS algorithm was proposed, and the process model of knowledge access based on the amended RS algorithm was researched. Finally, we verified the correctness and the practicability of this method during the procedure of knowledge access.展开更多
Knowledge graph completion(KGC)aims to fill in missing entities and relations within knowledge graphs(KGs)to address their incompleteness.Most existing KGC models suffer from knowledge coverage as they are designed to...Knowledge graph completion(KGC)aims to fill in missing entities and relations within knowledge graphs(KGs)to address their incompleteness.Most existing KGC models suffer from knowledge coverage as they are designed to operate within a single KG.In contrast,Multilingual KGC(MKGC)leverages seed pairs from different language KGs to facilitate knowledge transfer and enhance the completion of the target KG.Previous studies on MKGC based on graph neural networks(GNNs)have primarily focused on using relationaware GNNs to capture the combined features of neighboring entities and relations.However,these studies still have some shortcomings,particularly in the context of MKGCs.First,each language’s specific semantics,structures,and expressions contribute to the increased heterogeneity of the KG.Therefore,the completion of MKGCs necessitates a thorough consideration of the heterogeneity of the KG and the effective integration of its heterogeneous features.Second,MKGCs typically have a large graph scale due to the need to store and manage information from multiple languages.However,current relation-aware GNNs often inherit complex GNN operations,resulting in unnecessary complexity.Therefore,it is necessary to simplify GNN operations.To address these limitations,we propose a Simplified Multi-view Graph Neural Network(SMGNN)for MKGC.SM-GNN incorporates two simplified multiview GNNs as components.One GNN is utilized for learning multi-view graph features to complete the KG.The other generates new alignment pairs,facilitating knowledge transfer between different views of the KG.We simplify the two multiview GNNs by retaining feature propagation while discarding linear transformation and nonlinear activation to reduce unnecessary complexity and effectively leverage graph contextual information.Extensive experiments demonstrate that our proposed model outperforms competing baselines.The code and dataset are available at the website of github.com/dbbice/SM-GNN.展开更多
Stress Knowledge Map(SKM;https://skm.nib.si)is a publicly available resource containing two complementary knowledge graphs that describe the current knowledge of biochemical,signaling,and regulatory molecular interact...Stress Knowledge Map(SKM;https://skm.nib.si)is a publicly available resource containing two complementary knowledge graphs that describe the current knowledge of biochemical,signaling,and regulatory molecular interactions in plants:a highly curated model of plant stress signaling(PSS;543 reactions)and a large comprehensive knowledge network(488390 interactions).Both were constructed by domain experts through systematic curation of diverse literature and database resources.SKM provides a single entry point for investigations of plant stress response and related growth trade-offs,as well as interactive explorations of current knowledge.PSS is also formulated as a qualitative and quantitative model for systems biology and thus represents a starting point for a plant digital twin.Here,we describe the features of SKM and show,through two case studies,how it can be used for complex analyses,including systematic hypothesis generation and design of validation experiments,or to gain new insights into experimental observations in plant biology.展开更多
The paper discusses two basic principles derived from results of studies concerning foundations of micro-theories of knowledge creation; these are Multimedia Principle and Emergence Principle. Their epistemic, systemi...The paper discusses two basic principles derived from results of studies concerning foundations of micro-theories of knowledge creation; these are Multimedia Principle and Emergence Principle. Their epistemic, systemic and metaphysical importance is discussed, together with their relations to the episteme of technology treated as a separate cultural sphere. A spiral of evolutionary knowledge creation is presented, in which an extended Falsification Principle plays the role of an objectifying feedback; this spiral is related to an episteme of Evolutionary Constructive Objectivism proposed earlier for the coming knowledge civilisation age.展开更多
The paper starts from a discussion of the concepts of knowledge management versus technology management, and the emergence of knowledge sciences. This is followed be a summary of recent results in the theory of knowle...The paper starts from a discussion of the concepts of knowledge management versus technology management, and the emergence of knowledge sciences. This is followed be a summary of recent results in the theory of knowledge creation. Most of them concern diverse spirals of creative interplay between rational （explicit） and intuitive or emotional （tacit） aspects of knowledge. Some of them concentrate on organizational （market or purpose-oriented） knowledge creation, other describe academic （research-oriented） knowledge creation. The problem addressed in this paper is how to integrate diverse spirals of knowledge creation into a prescriptive or exemplar model that would help to overcome the differences between organizational （market-oriented） and normal academic knowledge creation. As such prescriptive approach, the JAIST Nanatsudaki Model of knowledge creation is proposed. It consists of seven spirals, known from other studies, but integrated in a sequence resulting from the experience of authors in practical management of research activities. Not all of these spirals have to be fully utilized, depending on a particular application, but all of them relate to some essential aspects of either academic or organizational knowledge creation. The paper presents Nanatsudaki Model in detail with comments on consecutive spirals. The results of a survey of opinions about creativity conditions at JAIST indicate the importance of many spirals constituting the Nanatsudaki Model. Directions of further testing the Nanatsudaki Model are indicated.展开更多
The early concept of knowledge graph originates from the idea of the semantic Web,which aims at using structured graphs to model the knowledge of the world and record the relationships that exist between things.Curren...The early concept of knowledge graph originates from the idea of the semantic Web,which aims at using structured graphs to model the knowledge of the world and record the relationships that exist between things.Currently publishing knowledge bases as open data on the Web has gained significant attention.In China,Chinese Information Processing Society of China(CIPS)launched the OpenKG in 2015 to foster the development of Chinese Open Knowledge Graphs.Unlike existing open knowledge-based programs,OpenKG chain is envisioned as a blockchain-based open knowledge infrastructure.This article introduces the first attempt at the implementation of sharing knowledge graphs on OpenKG chain,a blockchain-based trust network.We have completed the test of the underlying blockchain platform,and the on-chain test of OpenKG’s data set and tool set sharing as well as fine-grained knowledge crowdsourcing at the triple level.We have also proposed novel definitions:K-Point and OpenKG Token,which can be considered to be a measurement of knowledge value and user value.1,033 knowledge contributors have been involved in two months of testing on the blockchain,and the cumulative number of on-chain recordings triggered by real knowledge consumers has reached 550,000 with an average daily peak value of more than 10,000.For the first time,we have tested and realized on-chain sharing of knowledge at entity/triple granularity level.At present,all operations on the data sets and tool sets at OpenKG.CN,as well as the triplets at OpenBase,are recorded on the chain,and corresponding value will also be generated and assigned in a trusted mode.Via this effort,OpenKG chain looks forward to providing a more credible and traceable knowledge-sharing platform for the knowledge graph community.展开更多
This paper proposes a knowledge-scientific approach to evaluation of community service systems from the viewpoints of knowledge creation, consciousness reform, and value co-creation. A concrete example of the commtmit...This paper proposes a knowledge-scientific approach to evaluation of community service systems from the viewpoints of knowledge creation, consciousness reform, and value co-creation. A concrete example of the commtmity service system treated here is an education program for old men to find their reason for living after the retirement. After introducing this program and the traditional evaluation methods for such a program, the paper emphasizes the necessity of developing new evaluation methods for such a community service system based on knowledge science. The paper proposes a new evaluation framework and reports an actual evaluation result using the interview data from participants in that program.展开更多
In this paper, an application mode and method of knowledge management in remanufacturing engineering management is established based on Nonaka's SECI model. The relationships between knowledge transfer,knowledge s...In this paper, an application mode and method of knowledge management in remanufacturing engineering management is established based on Nonaka's SECI model. The relationships between knowledge transfer,knowledge sharing and remanufacturing engineering management are highlighted. It is noticeable that a great deal of knowledge transfer and sharing activities, which can improve the performance of remanufacturing engineering management constantly, are involved in remanufacturing engineering.展开更多
Machine reading comprehension has been a research focus in natural language processing and intelligence engineering.However,there is a lack of models and datasets for the MRC tasks in the anti-terrorism domain.Moreove...Machine reading comprehension has been a research focus in natural language processing and intelligence engineering.However,there is a lack of models and datasets for the MRC tasks in the anti-terrorism domain.Moreover,current research lacks the ability to embed accurate background knowledge and provide precise answers.To address these two problems,this paper first builds a text corpus and testbed that focuses on the anti-terrorism domain in a semi-automatic manner.Then,it proposes a knowledge-based machine reading comprehension model that fuses domain-related triples from a large-scale encyclopedic knowledge base to enhance the semantics of the text.To eliminate knowledge noise that could lead to semantic deviation,this paper uses a mixed mutual ttention mechanism among questions,passages,and knowledge triples to select the most relevant triples before embedding their semantics into the sentences.Experiment results indicate that the proposed approach can achieve a 70.70%EM value and an 87.91%F1 score,with a 4.23%and 3.35%improvement over existing methods,respectively.展开更多
Knowledge graphs(KGs)express relationships between entity pairs,and many real-life problems can be formulated as knowledge graph reasoning(KGR).Conventional approaches to KGR have achieved promising performance but st...Knowledge graphs(KGs)express relationships between entity pairs,and many real-life problems can be formulated as knowledge graph reasoning(KGR).Conventional approaches to KGR have achieved promising performance but still have some drawbacks.On the one hand,most KGR methods focus only on one phase of the KG lifecycle,such as KG completion or refinement,while ignoring reasoning over other stages,such as KG extraction.On the other hand,traditional KGR methods,broadly categorized as symbolic and neural,are unable to balance both scalability and interpretability.To resolve these two problems,we take a more comprehensive perspective of KGR with regard to the whole KG lifecycle,including KG extraction,completion,and refinement,which correspond to three subtasks:knowledge extraction,relational reasoning,and inconsistency checking.In addition,we propose the implementation of KGR using a novel neural symbolic framework,with regard to both scalability and interpretability.Experimental results demonstrate that our proposed methods outperform traditional neural symbolic models.展开更多
This paper considers the regional vitalization problem and discusses the methodology to create regional vitalization plans, which include activating the local economy, enriching people＇s lives, and activating the fee...This paper considers the regional vitalization problem and discusses the methodology to create regional vitalization plans, which include activating the local economy, enriching people＇s lives, and activating the feelings of people, by new initiatives. Activily underlying the methodology is the experience of implementing several actual projects with the local residents, and theory underlying the methodology is the knowledge construction and justification theory based on knowledge management and systems thinking. Introducing an actual vitalization project as an illustrative example, the paper proposes a knowledge reconstruction and justification procedure for regional vitalization.展开更多
基金supported in part by the Beijing Natural Science Foundation under Grants L211020 and M21032in part by the National Natural Science Foundation of China under Grants U1836106 and 62271045in part by the Scientific and Technological Innovation Foundation of Foshan under Grants BK21BF001 and BK20BF010。
文摘Knowledge graph(KG)serves as a specialized semantic network that encapsulates intricate relationships among real-world entities within a structured framework.This framework facilitates a transformation in information retrieval,transitioning it from mere string matching to far more sophisticated entity matching.In this transformative process,the advancement of artificial intelligence and intelligent information services is invigorated.Meanwhile,the role ofmachine learningmethod in the construction of KG is important,and these techniques have already achieved initial success.This article embarks on a comprehensive journey through the last strides in the field of KG via machine learning.With a profound amalgamation of cutting-edge research in machine learning,this article undertakes a systematical exploration of KG construction methods in three distinct phases:entity learning,ontology learning,and knowledge reasoning.Especially,a meticulous dissection of machine learningdriven algorithms is conducted,spotlighting their contributions to critical facets such as entity extraction,relation extraction,entity linking,and link prediction.Moreover,this article also provides an analysis of the unresolved challenges and emerging trajectories that beckon within the expansive application of machine learning-fueled,large-scale KG construction.
基金This work is supported by the National Key Research and Development Program of China under Grant 2017YFB1002304the China Scholarship Council under Grant 201906465021.
文摘Syndrome differentiation is the core diagnosis method of Traditional Chinese Medicine(TCM).We propose a method that simulates syndrome differentiation through deductive reasoning on a knowledge graph to achieve automated diagnosis in TCM.We analyze the reasoning path patterns from symptom to syndromes on the knowledge graph.There are two kinds of path patterns in the knowledge graph:one-hop and two-hop.The one-hop path pattern maps the symptom to syndromes immediately.The two-hop path pattern maps the symptom to syndromes through the nature of disease,etiology,and pathomechanism to support the diagnostic reasoning.Considering the different support strengths for the knowledge paths in reasoning,we design a dynamic weight mechanism.We utilize Naïve Bayes and TF-IDF to implement the reasoning method and the weighted score calculation.The proposed method reasons the syndrome results by calculating the possibility according to the weighted score of the path in the knowledge graph based on the reasoning path patterns.We evaluate the method with clinical records and clinical practice in hospitals.The preliminary results suggest that the method achieves high performance and can help TCM doctors make better diagnosis decisions in practice.Meanwhile,the method is robust and explainable under the guide of the knowledge graph.It could help TCM physicians,especially primary physicians in rural areas,and provide clinical decision support in clinical practice.
基金the National Basic Research Program(973)of China(Nos.2011CB707503 and2011CB013305)the National Natural Science Foundation of China(Nos.51075262,51305260,51275293,51121063,50575142 and 51005148)+4 种基金the"ShuGuang"Project of Shanghai Municipal Education Commissionand Shanghai Education Development Foundation(No.12SG14)the Project of Shanghai Committee of Science and Technology(Nos.11JC1406100,13111102800 and 11BA1405300)the National KeyScientific Instruments and Equipment Development Program of China(Nos.2013YQ03065105 and2011YQ030114)the Program for New Century Excellent Talents in University(No.NCET-08-0361)the National High Technology Research and DevelopmentProgram(863)of China(No.2008AA04Z113)
文摘Biological inspirations are good design mimicry resources. This paper proposes a function based approach for modeling and transformation of bio-inspiration design knowledge. A general functional modeling method for biological domain and engineering domain design knowledge is introduced. Functional similarity based bio-inspiration transformation between biological domain and engineering domain is proposed. The biological function topology transfer and analog solution recomposition are also discussed in this paper.
基金Supported by the National Key Research and Development Program of China under Grant 2017YFB1002304 and the National Natural Science Foundation of China(No.61672178)The author who received the grant is Azguri,and the official website of the funder is http://www.most.gov.cn/.
文摘As one of the most valuable assets in China,traditional medicine has a long history and contains pieces of knowledge.The diagnosis and treatment of Traditional Chinese Medicine(TCM)has benefited from the natural language processing technology.This paper proposes a knowledge-based syndrome reasoning method in computer-assisted diagnosis.This method is based on the established knowledge graph of TCM and this paper introduces the reinforcement learning algorithm to mine the hidden relationship among the entities and obtain the reasoning path.According to this reasoning path,we could infer the path from the symptoms to the syndrome and get all possibilities via the relationship between symptoms and causes.Moreover,this study applies the Term Frequency-Inverse Document Frequency(TF-IDF)idea to the computer-assisted diagnosis of TCM for the score of syndrome calculation.Finally,combined with symptoms,syndrome,and causes,the disease could be confirmed comprehensively by voting,and the experiment shows that the system can help doctors and families to disease diagnosis effectively.
基金This work is partly supported by the National Natural Science Foundation of China Key Program （Project Number： 71133006/G0314 Project Name： Developments of the Information Resources Industry in China： Strategies and Policies） and the National Social Science Foundation of China Major Program （Project Number： 13 ＆ ZD 184 Project Name： Novel Mechanisms for the Integration of National Digital Archival Resources and Their Utilization）.
文摘With the increasing number of applications and services online, more and more business processes are carried out in a fully integrated digital working environment. To ensure the success of a business and the smart capture and reuse of organizational knowledge, adequate recordkeeping is essential. A review of the related literature indicates that international trends and future directions of recordkeeping awareness, recordkeeping processes and regimes, recordkeeping systems and technologies are moving towards meta-synthetic support in a digital environment at strategic level. However, little has been discussed about specific strategies of meta-synthetic support for digital recordkeeping, and in turn, organizational knowledge. The purpose of this paper is to propose the adoption and adaptation of the ISO/TC 46/SC 11 series of standards and the integrated use of ISO management systems standards （MSSs） as enablers of meta-synthetic strategies to enhance organizational performance and accountability through sustainable digital recordkeeping. The integrated use of ISO MSSs provides ever increasing opportunities for integrated product, process, service, and compliance control. The mechanisms of the above meta-synthetic strategies are collaboration, optimization, innovation, and compliance. The theoretical foundations are the integrated use of life-cycle, continuum and ecosystem theories with multidisciplinary perspectives. The practical outcomes are the support of varied evidence-based collaborations at multiple levels, such as national strategies and plans for digital continuity, knowledge sharing and reuse, open government initiatives, e-government information architecture and services competence building, and enterprise information governance.
文摘It is common in industrial construction projects for data to be collected and discarded without being analyzed to extract useful knowledge. A proposed integrated methodology based on a five-step Knowledge Discovery in Data (KDD) model was developed to address this issue. The framework transfers existing multidimensional historical data from completed projects into useful knowledge for future projects. The model starts by understanding the problem domain, industrial construction projects. The second step is analyzing the problem data and its multiple dimensions. The target dataset is the labour resources data generated while managing industrial construction projects. The next step is developing the data collection model and prototype data ware-house. The data warehouse stores collected data in a ready-for-mining format and produces dynamic On Line Analytical Processing (OLAP) reports and graphs. Data was collected from a large western-Canadian structural steel fabricator to prove the applicability of the developed methodology. The proposed framework was applied to three different case studies to validate the applicability of the developed framework to real projects data.
基金This paper is sponsored by National Nature Science Foundation of China（NSFC）.
文摘Despite of the fact that knowledge management has become the focus of current literature and management practice, the core process of knowledge has. not been identified. After comparing the pl：ocess of knowledge movement and that of fermenting, we put forward a new model-knowledge fermenting model. In this paper, we thoroughly analyze the element of knowledge fermenting model, and show how knowledge increase is realized through that model.
文摘Background: Tuberculosis (TB) is one of the top 10 causes of death worldwide. India is still the highest TB burden country. There is a scarcity of data on TB knowledge from Rajasthan state of India. Objective: The objective of this study was to estimate the prevalence of knowledge about TB and services of TB control programme and to determine its correlates among rural population of Jaipur, Rajasthan. Methods: Cross-sectional community based study was carried out at Model Rural Health Research Unit, Jaipur, a unit of Department of Health Research, Ministry of Health & Family Welfare, Government of India. Results: Study reports the result from 1993 adult participants from 10 villages of 2 sub-districts of district Jaipur. About 88.9% of studied participants knew that TB is an infectious disease and it spreads from TB patient to healthy person in close contact. Only 22.3% of participants knew “DOTS is the treatment for TB”. While, only 58.9% knew “sputum is used for diagnosis of TB” at health centers. Scheduled castes, scheduled tribes and backward classes social groups knew less than the mainstream “General” social group. The observed difference was statistically significant (p 0.05). Logistic regression analysis estimated the relative contribution to knowledge status. Conclusion: The knowledge of study participants on transmission of tuberculosis was similar to knowledge of population in country wide study. They poorly knew sputum is used for diagnosing tuberculosis disease;socio-demographic inequity exists in this knowledge too. People from older age groups, underprivileged social groups and minority need extra educational activities.
基金supported by the Shanghai Science and Technology Development Foundation(No.005111070)
文摘During the procedure of fault diagnosis for large-scale complicated equipment, the existence of redundant and fuzzy information results in the difficulty of knowledge access. Aiming at this characteristic, this paper brought forth the Rough Set （RS） theory to the field of fault diagnosis. By means of the RS theory which is predominant in the way of dealing with fuzzy and uncertain information, knowledge access about fault diagnosis was realized. The foundation ideology of the RS theory was exhausted in detail, an amended RS algorithm was proposed, and the process model of knowledge access based on the amended RS algorithm was researched. Finally, we verified the correctness and the practicability of this method during the procedure of knowledge access.
基金supported by the National Natural Science Foundation of China(Grant Nos.62120106008,61976077,61806065,62076085,and 91746209)the Fundamental Research Funds for the Central Universities(JZ2022HGTB0239).
文摘Knowledge graph completion(KGC)aims to fill in missing entities and relations within knowledge graphs(KGs)to address their incompleteness.Most existing KGC models suffer from knowledge coverage as they are designed to operate within a single KG.In contrast,Multilingual KGC(MKGC)leverages seed pairs from different language KGs to facilitate knowledge transfer and enhance the completion of the target KG.Previous studies on MKGC based on graph neural networks(GNNs)have primarily focused on using relationaware GNNs to capture the combined features of neighboring entities and relations.However,these studies still have some shortcomings,particularly in the context of MKGCs.First,each language’s specific semantics,structures,and expressions contribute to the increased heterogeneity of the KG.Therefore,the completion of MKGCs necessitates a thorough consideration of the heterogeneity of the KG and the effective integration of its heterogeneous features.Second,MKGCs typically have a large graph scale due to the need to store and manage information from multiple languages.However,current relation-aware GNNs often inherit complex GNN operations,resulting in unnecessary complexity.Therefore,it is necessary to simplify GNN operations.To address these limitations,we propose a Simplified Multi-view Graph Neural Network(SMGNN)for MKGC.SM-GNN incorporates two simplified multiview GNNs as components.One GNN is utilized for learning multi-view graph features to complete the KG.The other generates new alignment pairs,facilitating knowledge transfer between different views of the KG.We simplify the two multiview GNNs by retaining feature propagation while discarding linear transformation and nonlinear activation to reduce unnecessary complexity and effectively leverage graph contextual information.Extensive experiments demonstrate that our proposed model outperforms competing baselines.The code and dataset are available at the website of github.com/dbbice/SM-GNN.
基金funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement 862858(ADAPT)the Slovenian Research Agency under grant agreements 1000-15-0105,Z7-1888,J4-1777,P4-0165,N4-0199,Z4-50146,and J4-3089ELIXIR,the research infrastructure for life science data through the ELIXIR Implementation Study“Increasing plant data findability for ELIXIR and beyond”and ELIXIR-SI.We gratefully acknowledge funding from the Deutsche Forschungsgemeinschaft(DFG)to U.C.V.(INST 217/939-1 FUGG).
文摘Stress Knowledge Map(SKM;https://skm.nib.si)is a publicly available resource containing two complementary knowledge graphs that describe the current knowledge of biochemical,signaling,and regulatory molecular interactions in plants:a highly curated model of plant stress signaling(PSS;543 reactions)and a large comprehensive knowledge network(488390 interactions).Both were constructed by domain experts through systematic curation of diverse literature and database resources.SKM provides a single entry point for investigations of plant stress response and related growth trade-offs,as well as interactive explorations of current knowledge.PSS is also formulated as a qualitative and quantitative model for systems biology and thus represents a starting point for a plant digital twin.Here,we describe the features of SKM and show,through two case studies,how it can be used for complex analyses,including systematic hypothesis generation and design of validation experiments,or to gain new insights into experimental observations in plant biology.
文摘The paper discusses two basic principles derived from results of studies concerning foundations of micro-theories of knowledge creation; these are Multimedia Principle and Emergence Principle. Their epistemic, systemic and metaphysical importance is discussed, together with their relations to the episteme of technology treated as a separate cultural sphere. A spiral of evolutionary knowledge creation is presented, in which an extended Falsification Principle plays the role of an objectifying feedback; this spiral is related to an episteme of Evolutionary Constructive Objectivism proposed earlier for the coming knowledge civilisation age.
文摘The paper starts from a discussion of the concepts of knowledge management versus technology management, and the emergence of knowledge sciences. This is followed be a summary of recent results in the theory of knowledge creation. Most of them concern diverse spirals of creative interplay between rational （explicit） and intuitive or emotional （tacit） aspects of knowledge. Some of them concentrate on organizational （market or purpose-oriented） knowledge creation, other describe academic （research-oriented） knowledge creation. The problem addressed in this paper is how to integrate diverse spirals of knowledge creation into a prescriptive or exemplar model that would help to overcome the differences between organizational （market-oriented） and normal academic knowledge creation. As such prescriptive approach, the JAIST Nanatsudaki Model of knowledge creation is proposed. It consists of seven spirals, known from other studies, but integrated in a sequence resulting from the experience of authors in practical management of research activities. Not all of these spirals have to be fully utilized, depending on a particular application, but all of them relate to some essential aspects of either academic or organizational knowledge creation. The paper presents Nanatsudaki Model in detail with comments on consecutive spirals. The results of a survey of opinions about creativity conditions at JAIST indicate the importance of many spirals constituting the Nanatsudaki Model. Directions of further testing the Nanatsudaki Model are indicated.
文摘The early concept of knowledge graph originates from the idea of the semantic Web,which aims at using structured graphs to model the knowledge of the world and record the relationships that exist between things.Currently publishing knowledge bases as open data on the Web has gained significant attention.In China,Chinese Information Processing Society of China(CIPS)launched the OpenKG in 2015 to foster the development of Chinese Open Knowledge Graphs.Unlike existing open knowledge-based programs,OpenKG chain is envisioned as a blockchain-based open knowledge infrastructure.This article introduces the first attempt at the implementation of sharing knowledge graphs on OpenKG chain,a blockchain-based trust network.We have completed the test of the underlying blockchain platform,and the on-chain test of OpenKG’s data set and tool set sharing as well as fine-grained knowledge crowdsourcing at the triple level.We have also proposed novel definitions:K-Point and OpenKG Token,which can be considered to be a measurement of knowledge value and user value.1,033 knowledge contributors have been involved in two months of testing on the blockchain,and the cumulative number of on-chain recordings triggered by real knowledge consumers has reached 550,000 with an average daily peak value of more than 10,000.For the first time,we have tested and realized on-chain sharing of knowledge at entity/triple granularity level.At present,all operations on the data sets and tool sets at OpenKG.CN,as well as the triplets at OpenBase,are recorded on the chain,and corresponding value will also be generated and assigned in a trusted mode.Via this effort,OpenKG chain looks forward to providing a more credible and traceable knowledge-sharing platform for the knowledge graph community.
文摘This paper proposes a knowledge-scientific approach to evaluation of community service systems from the viewpoints of knowledge creation, consciousness reform, and value co-creation. A concrete example of the commtmity service system treated here is an education program for old men to find their reason for living after the retirement. After introducing this program and the traditional evaluation methods for such a program, the paper emphasizes the necessity of developing new evaluation methods for such a community service system based on knowledge science. The paper proposes a new evaluation framework and reports an actual evaluation result using the interview data from participants in that program.
基金supported by National Natural Science Foundation of China (Grant No. 71471169 and Grant No. 71071151)
文摘In this paper, an application mode and method of knowledge management in remanufacturing engineering management is established based on Nonaka's SECI model. The relationships between knowledge transfer,knowledge sharing and remanufacturing engineering management are highlighted. It is noticeable that a great deal of knowledge transfer and sharing activities, which can improve the performance of remanufacturing engineering management constantly, are involved in remanufacturing engineering.
基金National key research and development program(2020AAA0108500)National Natural Science Foundation of China Project(No.U1836118)Key Laboratory of Rich Media Digital Publishing,Content Organization and Knowledge Service(No.:ZD2022-10/05).
文摘Machine reading comprehension has been a research focus in natural language processing and intelligence engineering.However,there is a lack of models and datasets for the MRC tasks in the anti-terrorism domain.Moreover,current research lacks the ability to embed accurate background knowledge and provide precise answers.To address these two problems,this paper first builds a text corpus and testbed that focuses on the anti-terrorism domain in a semi-automatic manner.Then,it proposes a knowledge-based machine reading comprehension model that fuses domain-related triples from a large-scale encyclopedic knowledge base to enhance the semantics of the text.To eliminate knowledge noise that could lead to semantic deviation,this paper uses a mixed mutual ttention mechanism among questions,passages,and knowledge triples to select the most relevant triples before embedding their semantics into the sentences.Experiment results indicate that the proposed approach can achieve a 70.70%EM value and an 87.91%F1 score,with a 4.23%and 3.35%improvement over existing methods,respectively.
基金funded by National Natural Science Foundation of China(Grant no.91846204 and U19B2027)National Key Research and Development Program of China(Grant no.2018YFB1402800).
文摘Knowledge graphs(KGs)express relationships between entity pairs,and many real-life problems can be formulated as knowledge graph reasoning(KGR).Conventional approaches to KGR have achieved promising performance but still have some drawbacks.On the one hand,most KGR methods focus only on one phase of the KG lifecycle,such as KG completion or refinement,while ignoring reasoning over other stages,such as KG extraction.On the other hand,traditional KGR methods,broadly categorized as symbolic and neural,are unable to balance both scalability and interpretability.To resolve these two problems,we take a more comprehensive perspective of KGR with regard to the whole KG lifecycle,including KG extraction,completion,and refinement,which correspond to three subtasks:knowledge extraction,relational reasoning,and inconsistency checking.In addition,we propose the implementation of KGR using a novel neural symbolic framework,with regard to both scalability and interpretability.Experimental results demonstrate that our proposed methods outperform traditional neural symbolic models.
文摘This paper considers the regional vitalization problem and discusses the methodology to create regional vitalization plans, which include activating the local economy, enriching people＇s lives, and activating the feelings of people, by new initiatives. Activily underlying the methodology is the experience of implementing several actual projects with the local residents, and theory underlying the methodology is the knowledge construction and justification theory based on knowledge management and systems thinking. Introducing an actual vitalization project as an illustrative example, the paper proposes a knowledge reconstruction and justification procedure for regional vitalization.
