Personal Tacit Knowledge and Global Learning Professional Competencies—Multi-Dimensional Relationships

Global learning professional competencies (GLPCs) are essential for college students to be able to address the impact of globalization in the 21st century. Organizations and society-at-large look to higher education to prepare college students with GLPCs. In addition, there is a body of literature that suggest personal tacit knowledge enhance GLPCs. However, researchers have done little from an empirical perspective to determine the relationship between the use of P-T K and enhancement of GLPCs, hence the purpose of this study. The statistical results revealed significant correlations, p st century knowledge society through use of P-T K.

Intelligent search on integrated knowledge base of traditional Chinese medicine

To semantically integrate heterogeneous resources and provide a unified intelligent access interface, semantic web technology is exploited to publish and interlink machineunderstandable resources so that intelligent search can be supported. TCMSearch, a deployed intelligent search engine for traditional Chinese medicine （TCM）, is presented. The core of the system is an integrated knowledge base that uses a TCM domain ontology to represent the instances and relationships in TCM. Machine-learning techniques are used to generate semantic annotations for texts and semantic mappings for relational databases, and then a semantic index is constructed for these resources. The major benefit of representing the semantic index in RDF/OWL is to support some powerful reasoning functions, such as class hierarchies and relation inferences. By combining resource integration with reasoning, the knowledge base can support some intelligent search paradigms besides keyword search, such as correlated search, semantic graph navigation and concept recommendation.

Ultra-lightweight CNN design based on neural architecture search and knowledge distillation: A novel method to build the automatic recognition model of space target ISAR images

In this paper,a novel method of ultra-lightweight convolution neural network(CNN)design based on neural architecture search(NAS)and knowledge distillation(KD)is proposed.It can realize the automatic construction of the space target inverse synthetic aperture radar(ISAR)image recognition model with ultra-lightweight and high accuracy.This method introduces the NAS method into the radar image recognition for the first time,which solves the time-consuming and labor-consuming problems in the artificial design of the space target ISAR image automatic recognition model(STIIARM).On this basis,the NAS model’s knowledge is transferred to the student model with lower computational complexity by the flow of the solution procedure(FSP)distillation method.Thus,the decline of recognition accuracy caused by the direct compression of model structural parameters can be effectively avoided,and the ultralightweight STIIARM can be obtained.In the method,the Inverted Linear Bottleneck(ILB)and Inverted Residual Block(IRB)are firstly taken as each block’s basic structure in CNN.And the expansion ratio,output filter size,number of IRBs,and convolution kernel size are set as the search parameters to construct a hierarchical decomposition search space.Then,the recognition accuracy and computational complexity are taken as the objective function and constraint conditions,respectively,and the global optimization model of the CNN architecture search is established.Next,the simulated annealing(SA)algorithm is used as the search strategy to search out the lightweight and high accuracy STIIARM directly.After that,based on the three principles of similar block structure,the same corresponding channel number,and the minimum computational complexity,the more lightweight student model is designed,and the FSP matrix pairing between the NAS model and student model is completed.Finally,by minimizing the loss between the FSP matrix pairs of the NAS model and student model,the student model’s weight adjustment is completed.Thus the ultra-lightweight and high accuracy STIIARM is obtained.The proposed method’s effectiveness is verified by the simulation experiments on the ISAR image dataset of five types of space targets.

A dynamic knowledge base based search engine

Search engines have greatly helped us to find the desired information from the Internet. Most search engines use keywords matching technique. This paper discusses a Dynamic Knowledge Base based Search Engine （DKBSE）, which can expand the user＇s query using the keywords＇ concept or meaning. To do this, the DKBSE needs to construct and maintain the knowledge base dynamically via the system＇s searching results and the user＇s feedback information. The DKBSE expands the user＇s initial query using the knowledge base, and returns the searched information after the expanded query.

Knowledge Graph Representation Learning Based on Automatic Network Search for Link Prediction

Link prediction,also known as Knowledge Graph Completion(KGC),is the common task in Knowledge Graphs(KGs)to predict missing connections between entities.Most existing methods focus on designing shallow,scalable models,which have less expressive than deep,multi-layer models.Furthermore,most operations like addition,matrix multiplications or factorization are handcrafted based on a few known relation patterns in several wellknown datasets,such as FB15k,WN18,etc.However,due to the diversity and complex nature of real-world data distribution,it is inherently difficult to preset all latent patterns.To address this issue,we proposeKGE-ANS,a novel knowledge graph embedding framework for general link prediction tasks using automatic network search.KGEANS can learn a deep,multi-layer effective architecture to adapt to different datasets through neural architecture search.In addition,the general search spacewe designed is tailored forKGtasks.We performextensive experiments on benchmark datasets and the dataset constructed in this paper.The results show that our KGE-ANS outperforms several state-of-the-art methods,especially on these datasets with complex relation patterns.

ISI Web of Knowledge跨库检索功能与操作技巧

ISI Web of Knowledge是新一代的学术信息资源整合体系,是Web of science的最新升级平台。 本文主要介绍了这一体系的跨库检索功能及其操作技巧。

Web of Knowledge及其检索功能研究

分析了知网信息量大、链接多、检索功能全等特点,同时也指出了知网操作复杂等缺陷。

Survey of Knowledge Graph Approaches and Applications

With the advent of the era of big data,knowledge engineering has received extensive attention.How to extract useful knowledge from massive data is the key to big data analysis.Knowledge graph technology is an important part of artificial intelligence,which provides a method to extract structured knowledge from massive texts and images,and has broad application prospects.The knowledge base with semantic processing capability and open interconnection ability can be used to generate application value in intelligent information services such as intelligent search,intelligent question answering and personalized recommendation.Although knowledge graph has been applied to various systems,the basic theory and application technology still need further research.On the basis of comprehensively expounding the definition and architecture of knowledge graph,this paper reviews the key technologies of knowledge graph construction,including the research progress of four core technologies such as knowledge extraction technology,knowledge representation technology,knowledge fusion technology and knowledge reasoning technology,as well as some typical applications.Finally,the future development direction and challenges of the knowledge graph are prospected.

A Solution to Knowledge Management in Networked Collaborative Design

Concerning the demands in networked collaborative innovative design, a knowledge-based collaborative design model is introduced the model of knowledge integration along with its relevant supporting techniques are presented. After illustrating the general knowledge search paradigm, a kind of dynamic user model is proposed to improve knowledge search efficiency. At last a short introduction of the system’s implementation is described.

Review on the Work of Agriculture Ontology Research Group

This paper introduces efforts and achievements of Agriculture Ontology Service Research Group of Agricultural Information Institute of Chinese Academy of Agriculture Sciences in last 10 years. It summarizes the research on ontology construction methodology, ontology management system, ontology application and etc.

基于ElasticSearch和语义相似度匹配的教学资源搜索策略

【目的】整合多种教学资源,并在此场景下设计和实现一种高效准确的搜索策略,帮助用户获取丰富的教学内容。【应用背景】教学资源类型众多,数量庞大,用户对于准确检索的需求日益增长,仅基于ElasticSearch进行搜索的效果不尽人意。【方法】在对用户输入的Query进行预处理和分词后,通过ER-BERT语义相似度模型在Query库中匹配出n条近似结果,将其输入到ElasticSearch并构建相关度计算公式,最后按照综合评估的最终得分将匹配结果进行排序。【结果】利用知识图谱技术整合复杂的教学资源,并在此基础上实现了一种基于ElasticSearch和语义相似度匹配的教学资源搜索策略,在保证检索速度的同时可以根据用户检索Query的语义信息进行检索。【结论】实验结果表明使用该教学资源搜索策略增加了检索结果的数量,并在保证检索速度的同时提升了结果的准确性,显著改善了用户的搜索体验。

Necessary and Sufficient Conditions for Feasible Neighbourhood Solutions in the Local Search of the Job-Shop Scheduling Problem

The meta-heuristic algorithm with local search is an excellent choice for the job-shop scheduling problem(JSP).However,due to the unique nature of the JSP,local search may generate infeasible neighbourhood solutions.In the existing literature,although some domain knowledge of the JSP can be used to avoid infeasible solutions,the constraint conditions in this domain knowledge are sufficient but not necessary.It may lose many feasible solutions and make the local search inadequate.By analysing the causes of infeasible neighbourhood solutions,this paper further explores the domain knowledge contained in the JSP and proposes the sufficient and necessary constraint conditions to find all feasible neighbourhood solutions,allowing the local search to be carried out thoroughly.With the proposed conditions,a new neighbourhood structure is designed in this paper.Then,a fast calculation method for all feasible neighbourhood solutions is provided,significantly reducing the calculation time compared with ordinary methods.A set of standard benchmark instances is used to evaluate the performance of the proposed neighbourhood structure and calculation method.The experimental results show that the calculation method is effective,and the new neighbourhood structure has more reliability and superiority than the other famous and influential neighbourhood structures,where 90%of the results are the best compared with three other well-known neighbourhood structures.Finally,the result from a tabu search algorithm with the new neighbourhood structure is compared with the current best results,demonstrating the superiority of the proposed neighbourhood structure.

An Ontology-based Ranking Model in Search Engines

As the tsunami of data has emerged,search engines have become the most powerful tool for obtaining scattered information on the internet.The traditional search engines return the organized results by using ranking algorithm such as term frequency,link analysis(PageRank algorithm and HITS algorithm)etc.However,these algorithms must combine the keyword frequency to determine the relevance between user’s query and the data in the computer system or internet.Moreover,we expect the search engines could understand users’searching by content meanings rather than literal strings.Semantic Web is an intelligent network and it could understand human’s language more semantically and make the communication easier between human and computers.But,the current technology for the semantic search is hard to apply.Because some meta data should be annotated to each web pages,then the search engine will have the ability to understand the users intend.However,annotate every web page is very time-consuming and leads to inefficiency.So,this study designed an ontology-based approach to improve the current traditional keyword-based search and emulate the effects of semantic search.And let the search engine can understand users more semantically when it gets the knowledge.

知识搜寻与重构如何促进专精特新中小企业绿色技术创新——战略柔性与激励型环境规制的调节作用

专精特新中小企业的创新实力和发展韧性较强,如何促进其绿色技术创新对于实现“双碳”目标和企业可持续发展具有重要意义。基于组织搜寻与知识管理理论,引入知识重构作为中介变量,战略柔性与激励型环境规制作为调节变量,探究知识搜寻对专精特新中小企业绿色技术创新的内在作用机制。基于272家专精特新中小企业调查数据,通过层次回归、Bootstrap等方法进行分析。结果表明:市场知识搜寻与技术知识搜寻均对专精特新中小企业绿色技术创新具有显著影响;知识重构在市场知识搜寻、技术知识搜寻与专精特新中小企业绿色技术创新之间起部分中介作用;战略柔性正向调节知识搜寻对知识重构的影响;激励型环境规制正向调节知识重构对专精特新中小企业绿色技术创新的影响。

初创企业的知识搜索对创业机会识别影响机制--基于企业数字化能力的中介作用

基于知识基础观和动态能力理论,构建知识搜索、企业数字化能力与创业机会识别的理论模型,对251个初创企业创始人样本数据开展信效度检验、共同方法偏差检验、描述统计、相关分析与假设检验,结果显示,知识搜索宽度和深度均正向影响创业机会识别,企业数字化能力促进创业机会识别;数字感知能力、数字协同能力和数字运营能力分别在知识搜索和促进创业机会识别关系中起部分中介作用,其中数字协同能力的中介作用最强。因此初创企业应合理采取知识搜索策略识别创业机会,重视企业数字化能力的培育以及发挥其在知识搜索与创业机会识别间的传导作用。

Seru系统调度优化的知识引导协同进化算法

作为一种新型的生产模式, Seru系统能够兼顾柔性和效率且快速响应市场,已在装配企业得到广泛应用.为了实现实际生产过程生产效率和劳动效率的协同优化,本文研究以最小化最大完工时间和工人总劳动时间为目标的Seru系统多目标调度问题,提出一种知识引导的协同进化算法.首先,将问题分解为Seru构造和Seru调度,构造两个种群分别优化子问题.同时,设计种群规模的调整策略,通过为有潜力的种群分配更多个体来提高协同搜索的效率.进而,通过分析问题的性质,提炼规则性知识用于设计有效的搜索算子和重生成规则,指导精英个体执行知识驱动的增强搜索,从而进一步提升算法的局部开发能力.通过数值仿真和统计性能对比,验证了算法各设计环节的有效性,并取得了显著优于现有最新算法的多目标调度优化性能.

异质性环境规制对企业差异化绿色创新行为的影响——知识搜寻的中介作用

环境规制与绿色创新的关系一直备受争议,这可能与多数文献忽视环境规制(命令型与市场型)与绿色创新(策略性与实质性)的双重异质性有关。基于制度理论与知识管理理论,构建“环境规制—知识搜寻—企业绿色创新”理论框架,综合运用多元线性回归与模糊集定性比较分析(fsQCA)方法,采用285份企业调查问卷验证研究假设。结果显示:命令型环境规制仅能促进策略性绿色创新,市场型环境规制仅能促进实质性绿色创新;知识搜寻深度在命令型环境规制与策略性绿色创新间发挥中介作用,知识搜寻广度在市场型环境规制与实质性绿色创新间发挥中介作用。进一步地,通过fsQCA分析得到5种前因构型,其中,高策略性绿色创新与高实质性绿色创新的组态路径分别有2条和3条。结论可拓展环境规制对企业绿色创新影响的研究维度,并为政府调整环境规制政策、企业应对外部制度压力提供参考。

跨界知识搜寻、吸收能力对制造企业突破式创新的影响研究

基于创新理论、资源基础观理论与组织学习理论,构建跨界知识搜寻、吸收能力、战略柔性与企业突破式创新的理论模型和分析框架,运用SPSS和SmartPLS软件进行实证检验。研究结果表明:技术知识跨界搜寻正向影响企业突破式创新;市场知识跨界搜寻对企业突破式创新没有显著影响;知识跨界搜寻正向影响企业吸收能力;吸收能力正向影响企业突破式创新;吸收能力在跨界知识搜寻与企业突破式创新之间发挥显著中介作用;战略柔性在跨界知识搜寻对企业突破式创新的影响中发挥显著调节作用。

环保法影响资源型企业绿色创新效应研究——基于外部知识搜寻的视角

借助环境规制促使资源型企业绿色创新,已成为缓解环境压力、实现提质增效、推动高质量发展的关键所在与必然选择。以环保法为代表,基于2010-2019年上市公司微观数据,从不利因素和有利条件两方面深入探讨了环境规制对资源型企业绿色创新的影响效应。研究发现:若环境规制忽视资源型企业合规成本等因素将不利于资源型企业绿色创新效应,该结论通过一系列稳健性检验和因果识别检验,佐证结论可靠。进一步研究发现增强知识搜寻能力和优化外部创新知识环境能够降低资源型企业合规成本的不利影响,实现环境规制对资源型企业绿色创新的促进效应。文章厘清了环境规制如何有效缓解合规成本的不利影响因素,进而提升资源型企业绿色创新效应的影响机理,对推动经济绿色转型、实现高质量发展提供了理论支撑与政策依据。

技术多元化能够促进企业绩效提升吗--技术重组模式与科学知识搜索的调节作用

技术多元化是企业获取竞争优势的关键,对于其如何影响企业绩效这一问题,学界存在不同观点。解决跨领域技术融合难题是企业从技术多元化战略中获益的关键,然而现有文献忽略了不同企业技术重组搜索特征差异,对于如何优化技术重组搜索过程以提升技术多元化战略实施效果这一问题尚未提供答案。从技术重组搜索视角出发,探究企业技术重组模式、科学知识搜索对技术多元化与企业绩效关系的调节作用。基于我国高技术产业上市公司面板数据的实证研究发现:技术多元化与企业绩效呈倒U型关系;技术重组创造促使该倒U型关系曲线变得平坦,技术重组再利用促使该倒U型关系曲线变得陡峭;科学知识搜索强度的调节作用不显著,但在企业科学知识搜索强度较高且擅长技术重组创造的情况下,技术多元化与绩效正相关,从而将“技术多元化陷阱”转变为“技术多元化优势”。随着技术多元化程度加深,企业不仅可以通过积累资源,而且能够通过优化技术重组过程,由技术重组再利用模式向技术重组创造模式转变,并通过加强科学知识搜索运用从技术多元化战略中获益。