Knowledge Reasoning Method Based on Deep Transfer Reinforcement Learning:DTRLpath

In recent years,with the continuous development of deep learning and knowledge graph reasoning methods,more and more researchers have shown great interest in improving knowledge graph reasoning methods by inferring missing facts through reasoning.By searching paths on the knowledge graph and making fact and link predictions based on these paths,deep learning-based Reinforcement Learning(RL)agents can demonstrate good performance and interpretability.Therefore,deep reinforcement learning-based knowledge reasoning methods have rapidly emerged in recent years and have become a hot research topic.However,even in a small and fixed knowledge graph reasoning action space,there are still a large number of invalid actions.It often leads to the interruption of RL agents’wandering due to the selection of invalid actions,resulting in a significant decrease in the success rate of path mining.In order to improve the success rate of RL agents in the early stages of path search,this article proposes a knowledge reasoning method based on Deep Transfer Reinforcement Learning path(DTRLpath).Before supervised pre-training and retraining,a pre-task of searching for effective actions in a single step is added.The RL agent is first trained in the pre-task to improve its ability to search for effective actions.Then,the trained agent is transferred to the target reasoning task for path search training,which improves its success rate in searching for target task paths.Finally,based on the comparative experimental results on the FB15K-237 and NELL-995 datasets,it can be concluded that the proposed method significantly improves the success rate of path search and outperforms similar methods in most reasoning tasks.

Intelligent Manufacturing Engineers’Knowledge Transfer and Innovation Capability:From the Perspective of Big Data Acceptance Attitude

In the face of intelligent manufacturing(or smart manufacturing)human resource shortage,the training of industrial engineers in the field of intelligent manufacturing is of great significance.In academia,the positive link between learning transfer and knowledge innovation is recognized by most scholars,while the learner’s attitude toward big data decision-making,as a cognitive perception,affects learning transfer from the learner’s experienced engineering paradigm to the intelligent manufacturing paradigm.Thus,learning transfer can be regarded as a result of the learner’s attitude,and it becomes the intermediary state between their attitude and knowledge innovation.This paper reviews prior research on knowledge transfer and develops hypotheses on the relationships between learner acceptance attitude,knowledge transfer,and knowledge innovation.

From Knowledge Transfer to Capability Development:The Future of PBL+CBL Teaching Method in Operating Room Nursing Education

Concomitant with the advancement of contemporary medical technology,the significance of perioperative nursing has been increasingly accentuated,necessitating elevated standards for the pedagogy of perioperative nursing.Presently,the PBL(problem-based learning)pedagogical approach,when integrated with CBL(case-based learning),has garnered considerable interest.An extensive literature review has been conducted to analyze the application of the PBL-CBL fusion in the education of perioperative nursing.Findings indicate that this integrative teaching methodology not only enhances students’theoretical knowledge,practical competencies,and collaborative skills but also contributes to the elevation of teaching quality.In conclusion,the PBL-CBL teaching approach holds immense potential for broader application in perioperative nursing education.Nevertheless,it is imperative to continually refine this combined pedagogical strategy to further enhance the caliber of perioperative nursing instruction and to cultivate a greater number of exceptional nursing professionals in the operating room setting.

Application of sparse S transform network with knowledge distillation in seismic attenuation delineation

Time-frequency analysis is a successfully used tool for analyzing the local features of seismic data.However,it suffers from several inevitable limitations,such as the restricted time-frequency resolution,the difficulty in selecting parameters,and the low computational efficiency.Inspired by deep learning,we suggest a deep learning-based workflow for seismic time-frequency analysis.The sparse S transform network(SSTNet)is first built to map the relationship between synthetic traces and sparse S transform spectra,which can be easily pre-trained by using synthetic traces and training labels.Next,we introduce knowledge distillation(KD)based transfer learning to re-train SSTNet by using a field data set without training labels,which is named the sparse S transform network with knowledge distillation(KD-SSTNet).In this way,we can effectively calculate the sparse time-frequency spectra of field data and avoid the use of field training labels.To test the availability of the suggested KD-SSTNet,we apply it to field data to estimate seismic attenuation for reservoir characterization and make detailed comparisons with the traditional time-frequency analysis methods.

Knowledge Transfer Learning via Dual Density Sampling for Resource-Limited Domain Adaptation

Most existing domain adaptation(DA) methods aim to explore favorable performance under complicated environments by sampling.However,there are three unsolved problems that limit their efficiencies:ⅰ) they adopt global sampling but neglect to exploit global and local sampling simultaneously;ⅱ)they either transfer knowledge from a global perspective or a local perspective,while overlooking transmission of confident knowledge from both perspectives;and ⅲ) they apply repeated sampling during iteration,which takes a lot of time.To address these problems,knowledge transfer learning via dual density sampling(KTL-DDS) is proposed in this study,which consists of three parts:ⅰ) Dual density sampling(DDS) that jointly leverages two sampling methods associated with different views,i.e.,global density sampling that extracts representative samples with the most common features and local density sampling that selects representative samples with critical boundary information;ⅱ)Consistent maximum mean discrepancy(CMMD) that reduces intra-and cross-domain risks and guarantees high consistency of knowledge by shortening the distances of every two subsets among the four subsets collected by DDS;and ⅲ) Knowledge dissemination(KD) that transmits confident and consistent knowledge from the representative target samples with global and local properties to the whole target domain by preserving the neighboring relationships of the target domain.Mathematical analyses show that DDS avoids repeated sampling during the iteration.With the above three actions,confident knowledge with both global and local properties is transferred,and the memory and running time are greatly reduced.In addition,a general framework named dual density sampling approximation(DDSA) is extended,which can be easily applied to other DA algorithms.Extensive experiments on five datasets in clean,label corruption(LC),feature missing(FM),and LC&FM environments demonstrate the encouraging performance of KTL-DDS.

Scene image recognition with knowledge transfer for drone navigation

In this paper,we study scene image recognition with knowledge transfer for drone navigation.We divide navigation scenes into three macro-classes,namely outdoor special scenes(OSSs),the space from indoors to outdoors or from outdoors to indoors transitional scenes(TSs),and others.However,there are difficulties in how to recognize the TSs,to this end,we employ deep convolutional neural network(CNN)based on knowledge transfer,techniques for image augmentation,and fine tuning to solve the issue.Moreover,there is still a novelty detection prob-lem in the classifier,and we use global navigation satellite sys-tems(GNSS)to solve it in the prediction stage.Experiment results show our method,with a pre-trained model and fine tun-ing,can achieve 91.3196%top-1 accuracy on Scenes21 dataset,paving the way for drones to learn to understand the scenes around them autonomously.

Introducing the Principles of Tendon Transfer for Surgical Trainees to Improve Anatomical Knowledge

This article reviewed the principles and outcomes of tendon transfer procedures described in the literature to restore function following injuries delivered in a workshop as a way of improving basic science and anatomical knowledge in surgical trainees preparing for surgical examinations. Post intervention surveys showed an improvement in trainees’ familiarity with musculoskeletal anatomy and engagement in learning with improved readiness for surgical examinations.

Progress and Knowledge Transfer from Science to Technology in the Research Frontier of CRISPR Based on the LDA Model

Purpose:This study explores the underlying research topics regarding CRISPR based on the LDA model and figures out trends in knowledge transfer from science to technology in this area over the latest 10 years.Design/methodology/approach:We collected publications on CRISPR between 2011 and2020 from the Web of Science,and traced all the patents citing them from lens.org.15,904 articles and 18,985 patents in total are downloaded and analyzed.The LDA model was applied to identify underlying research topics in related research.In addition,some indicators were introduced to measure the knowledge transfer from research topics of scientific publications to IPC-4 classes of patents.Findings:The emerging research topics on CRISPR were identified and their evolution over time displayed.Furthermore,a big picture of knowledge transition from research topics to technological classes of patents was presented.We found that for all topics on CRISPR,the average first transition year,the ratio of articles cited by patents,the NPR transition rate are respectively 1.08,15.57%,and 1.19,extremely shorter and more intensive than those of general fields.Moreover,the transition patterns are different among research topics.Research limitations:Our research is limited to publications retrieved from the Web of Science and their citing patents indexed in lens.org.A limitation inherent with LDA analysis is in the manual interpretation and labeling of"topics".Practical implications:Our study provides good references for policy-makers on allocating scientific resources and regulating financial budgets to face challenges related to the transformative technology of CRISPR.Originality/value:The LDA model here is applied to topic identification in the area of transformative researches for the first time,as exemplified on CRISPR.Additionally,the dataset of all citing patents in this area helps to provide a full picture to detect the knowledge transition between S&T.

Time Optimization of Multiple Knowledge Transfers in the Big Data Environment

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.

Knowledge transfer in multi-agent reinforcement learning with incremental number of agents

In this paper, the reinforcement learning method for cooperative multi-agent systems(MAS) with incremental number of agents is studied. The existing multi-agent reinforcement learning approaches deal with the MAS with a specific number of agents, and can learn well-performed policies. However, if there is an increasing number of agents, the previously learned in may not perform well in the current scenario. The new agents need to learn from scratch to find optimal policies with others,which may slow down the learning speed of the whole team. To solve that problem, in this paper, we propose a new algorithm to take full advantage of the historical knowledge which was learned before, and transfer it from the previous agents to the new agents. Since the previous agents have been trained well in the source environment, they are treated as teacher agents in the target environment. Correspondingly, the new agents are called student agents. To enable the student agents to learn from the teacher agents, we first modify the input nodes of the networks for teacher agents to adapt to the current environment. Then, the teacher agents take the observations of the student agents as input, and output the advised actions and values as supervising information. Finally, the student agents combine the reward from the environment and the supervising information from the teacher agents, and learn the optimal policies with modified loss functions. By taking full advantage of the knowledge of teacher agents, the search space for the student agents will be reduced significantly, which can accelerate the learning speed of the holistic system. The proposed algorithm is verified in some multi-agent simulation environments, and its efficiency has been demonstrated by the experiment results.

A Weakly-Supervised Method for Named Entity Recognition of Agricultural Knowledge Graph

It is significant for agricultural intelligent knowledge services using knowledge graph technology to integrate multi-source heterogeneous crop and pest data and fully mine the knowledge hidden in the text.However,only some labeled data for agricultural knowledge graph domain training are available.Furthermore,labeling is costly due to the need for more data openness and standardization.This paper proposes a novel model using knowledge distillation for a weakly supervised entity recognition in ontology construction.Knowledge distillation between the target and source data domain is performed,where Bi-LSTM and CRF models are constructed for entity recognition.The experimental result is shown that we only need to label less than one-tenth of the data for model training.Furthermore,the agricultural domain ontology is constructed by BILSTM-CRF named entity recognition model and relationship extraction model.Moreover,there are a total of 13,983 entities and 26,498 relationships built in the neo4j graph database.

Research into the Influencing Factors of Knowledge Transfer within Innovative Research Teams

Knowledge transfer within university-led innovative research teams helps to maximally gather knowledge sources and promote knowledge dissemination,exchange and digestion among different disciplines. T he effect of transfer directly affects the team's capacity of knowledge innovation and its outcomes. In this paper,a WSB-based research framework for the influencing factors of knowledge transfer within university-led innovative research teams is established by means of grounded theory with help of in-depth interviews,in which five fundamental categories that affect knowledge transfer within teams,namely,knowledge source,knowledge receiver,knowledge transfer context,knowledge characteristics and knowledge transfer medium,are proposed to elaborate on the relationship between the fundamental categories and the effect of knowledge transfer within teams.Finally,a theoretical saturation test is conducted to verify the rationality and scientific tenability of this theoretical framework.

Decision Model of Knowledge Transfer in Big Data Environment

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.

Academia Capabilities,Knowledge Transfer Programme Mechanism and Performance

Knowledge transfer(KT)is an attempt by an entity to copy and utilize an explicit type of knowledge from another entity.The main reason is none other than to expand the ability and increasing the value through inter-organization collaborative affiliation.Nonetheless,questions may arise as to what extent do capabilities,mechanism and performance or success is associated.Using inputs from 154 respondents which consist of various KTP(knowledge transfer program)partners namely from the community(total 94)and industry(total 60),this article highlights the associations between the three main categories of variables.Using Smart PLS(partial least squares),the study provides evidence that academia knowledge,academia readiness,academia skills,and ethics and conduct affect KTP performance through the mediation role of KT mechanism.Academia readiness was also found to be the most significant predictor to KT mechanism.In summary,all the significant capabilities have indirect positive impact towards KTP performance.Thus,higher education institutions must emphasize their internal strength in order to continue supporting the success of inter-organization collaborative affiliation.

Modeling the factors that influence knowledge transfer in mergers and acquisitions

This paper constructs a model on the factors that influence knowledge transfer in mergers and acquisitions(M&A) and validates it via questionnaire surveys. Using 125valid collected questionnaires, multiple linear regression analysis and hierarchical regression analysis showed that five out of the ten factors had a positive effect on knowledge transfer effect. The ranking of factor importance, from high to low, was knowledge explicitness, relationship quality, learning intent, advanced transfer activities, and learning capability, which is fairly consistent with positive factors observed in other interorganizational knowledge transfer researches. Our results also showed that one of the control variables(size of acquired firm) had neither a direct or indirect effect on knowledge transfer in M&A. Additionally, our research found that knowledge distance and degree of M&A integration had a positive influence on knowledge transfer effect at the early stage after M&A, but had a negative influence at the late stage. Based on this research, several suggestions for knowledge transfer in M&A are proposed.

Transformation and influencing factors of scholarly communication based on knowledge transfer: A case study of science and technology literature

Purpose: The process of scientific literature use can be regarded as that of knowledge transfer. With the help of the knowledge transfer theory and data from scientific literature databases, we explored the behavior of scientific researchers during their scholarly communication, and studied the factors that influenced the behavior of researchers under network environment. Design/methodology/approach: Based on the literature databases of CNKI, Elsevier Science Direct and Springer Link, we used the knowledge transfer theory to construct a model for describing the scholarly communication process, which attempts to find out factors that may influence the communication behavior of researchers. With a focus laid on the absorption behavior of researchers during the knowledge acceptance process, we defined the independent variables of the model and proposed hypotheses on the basis of a comprehensive literature study. Afterwards, college students were invited to participate in a questionnaire survey, which was designed to prove our research model and hypotheses.Findings: Our results showed that during the scholarly communication, it is not the professional knowledge, but the ability and willingness for knowledge acceptance, organizations’ importance and internal atmosphere as well as knowledge authority and relevance that have played a positive significant role in the knowledge transfer performance. In addition, our distance indicators showed that knowledge distance and knowledge transfer performance have significant negative correlations. Research limitations: This study is mainly based on a questionnaire survey of college students, which may limit the generalization of our research results. In addition, more resource types need be considered for further studies.Practical implications: Under network environment, scholarly communication performance based on knowledge transfer theory could greatly contribute to the enrichment of the contentof the knowledge transfer theory, and stretch out the range of the field. In addition, our result could help commercial scientific database providers to learn more about the users’ needs, which would not only benefit both scientific communities and content providers, but also promote scholarly communication effectively. Originality/value: Compared with existing researches which mainly emphasized the model construction of scholarly communication, our study focused the knowledge relevance during the scholarly communication and influence factors that impacted on the performance of knowledge acceptance under the network environment, which could provide helpful guides for further studies.

Intra-firm Horizontal Knowledge Transfer Management

Knowledge transfer is widely emphasized as a strategic issue for firm competition. A model for intra-firm horizontal knowledge transfer is proposed to model horizontal knowledge transfer to solve some demerits in current knowledge transfer researches. The concept model of intra-firm horizontal knowledge transfer was described and a framework was provided to define the main components of the transfer process. Horizontal knowledge transfer is that knowledge is transferred from the source to the same hierarchical level recipients as the target. Horizontal knowledge transfer constitutes a strategic area of knowledge management research. However, little is known about the circumstances under which one particular mechanism is the most appropriate. To address these issues, some significant conclusions are drawn concerning knowledge transfer mechanisms in a real-world setting.

A model for knowledge transfer in a multi-agent organization based on lattice kinetic model

A study on knowledge transfer in a mutli-agent organization is performed by applying the basic principle in physics such as the kinetic theory.Based on the theoretical analysis of the knowledge accumulation process and knowledge transfer attributes,a special type of knowledge field(KF)is introduced and the knowledge diffusion equation(KDE)is developed.The evolution of knowledge potential is modeled by lattice kinetic equation and verified by numerical experiments.The new equation-based modeling developed in this paper is meaningful to simulate and predict the knowledge transfer process in firms.The development of the lattice kinetic model(LKM)for knowledge transfer can contribute to the knowledge management theory,and the managers can also simulate the knowledge accumulation process by using the LKM.

Optimal Model of Continuous Knowledge Transfer in the Big Data Environment

With market competition becoming fiercer,enterprises must update their products by constantly assimilating new big data knowledge and private knowledge to maintain their market shares at different time points in the big data environment.Typically,there is mutual influence between each knowledge transfer if the time interval is not too long.It is necessary to study the problem of continuous knowledge transfer in the big data environment.Based on research on one-time knowledge transfer,a model of continuous knowledge transfer is presented,which can consider the interaction between knowledge transfer and determine the optimal knowledge transfer time at different time points in the big data environment.Simulation experiments were performed by adjusting several parameters.The experimental results verified the model’s validity and facilitated conclusions regarding their practical application values.The experimental results can provide more effective decisions for enterprises that must carry out continuous knowledge transfer in the big data environment.

Research on customer knowledge transferring mode and mechanism in internet-based new product development

With the development of internet technology, customers play more and more important roles in new product development. The paper defines customer knowledge; then analyses the modes of customer knowledge transferring based on SECI model and information emission model. Finally customer knowledge transferring mechanism is discussed.