IndRT-GCNets: Knowledge Reasoning with Independent Recurrent Temporal Graph Convolutional Representations

Due to the structural dependencies among concurrent events in the knowledge graph and the substantial amount of sequential correlation information carried by temporally adjacent events,we propose an Independent Recurrent Temporal Graph Convolution Networks(IndRT-GCNets)framework to efficiently and accurately capture event attribute information.The framework models the knowledge graph sequences to learn the evolutionary represen-tations of entities and relations within each period.Firstly,by utilizing the temporal graph convolution module in the evolutionary representation unit,the framework captures the structural dependency relationships within the knowledge graph in each period.Meanwhile,to achieve better event representation and establish effective correlations,an independent recurrent neural network is employed to implement auto-regressive modeling.Furthermore,static attributes of entities in the entity-relation events are constrained andmerged using a static graph constraint to obtain optimal entity representations.Finally,the evolution of entity and relation representations is utilized to predict events in the next subsequent step.On multiple real-world datasets such as Freebase13(FB13),Freebase 15k(FB15K),WordNet11(WN11),WordNet18(WN18),FB15K-237,WN18RR,YAGO3-10,and Nell-995,the results of multiple evaluation indicators show that our proposed IndRT-GCNets framework outperforms most existing models on knowledge reasoning tasks,which validates the effectiveness and robustness.

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.

Expression model for multiple relationships in the ontology of traditional Chinese medicine knowledge

Objective:To explore multiple relationships in traditional Chinese medicine(TCM)knowledge by comparing binary and multiple relationships during knowledge organization.Methods:Characteristics of binary and multiple semantic relationships as well as their associations are described.A method to classify multiple relationships based on the involvement of time is proposed and theoretically validated using examples from the ancient TCM classic Important Formulas Worth a Thousand Gold Pieces.The classification includes parallel multiple relationships,restricted multiple relationships,multiple relationships that involve time,and multiple relationships that involve time restriction.Next,construction of multiple semantic relationships for TCM concepts in each classification using Protege,an ontology editing tool is described.Results:Protege is superior to a binary relationship and less than ideal with multiple relationships during the constitution of concept relationships.Conclusion:When applied in TCM,the semantic relationships constructed by Protege are superior than those constructed by correlation and/or attribute relationships,but less ideal than those constructed by the human cognitive process.

Relationship of knowledge about osteoporosis with education level and life habits

AIM: To assess possible relationships of knowledge and related factors with educational level and osteoporosisrelated life habits.METHODS: This was a cross sectional study conducted on 268 women(≥ 35 years old) from June 2011 to August 2011. The sample collection was done in outpatient clinics in three university hospitals in Isfahan, Iran. We used a demographic questionnaire containingquestions that evaluated osteoporosis-related life habits, including exercise, smoking, intake of calcium and vitamin D supplements and so on. We also used the Osteoporosis Knowledge Assessment Tool to measure osteoporosis knowledge of women.RESULTS: The mean level of knowledge about awareness of osteoporosis, its risk factors and preventive factors were 56, 55 and 22, respectively. The relationship of education level and awareness of osteoporosis, its risk factors and preventive factors was significant, with R = 0.76, R = 0.73 and R = 0.83, respectively(P < 0.001). The relationship of education level and osteoporosis-related life habits was not significant(R = 0.03 and P = 0.56). The relationship of osteoporosis-related life habits and awareness of osteoporosis and its risk factors was significant, with R = 16%, P = 0.006 and R = 16%, P = 0.008, respectively, but the relationship of osteoporosis-related life habits and preventive factors was not significant(R = 0, P = 0.99).CONCLUSION: Iranian women with a higher education level have significantly better knowledge about osteoporosis than women with a lower educational level but they do not use this knowledge in their life.

Knowledge Management and Customer Relationship Management for Accounting Services Companies

By combining the abilities to respond directly to customer requests and to provide the customer with a highly interactive, customized experience, companies have a greater ability today to establish nurture and sustain long-term customer relationships than ever before. The ultimate goal is to transform these relationships into greater profitability by increasing repeat purchase rates and reducing customer acquisition costs. Customer relationship management （CRM） has a significant potential to leverage an organization＇s performance, but it does not come without a clear sense of destination and typically without pain. For the research methodology we use the database of customers of a Romanian accounting services company Vulpoi ＆ Toader Management SRL, which is an important player in this market. The goal of our paper is to find out the link between knowledge management （KM） and CRM for this company and how these ＂innovations＂ contribute to increasing the value of the business.

Cultural Background Knowledge and English Teaching

Culture background knowledge in English teaching arose in eighties of the twentieth century. It has been closely linked with the reformation and open in China. With the increase of exchange between China and English speaking countries resulting from the reform and opening policy to the outside world, people have come to find that due to the lack of profound knowledge of the culture of the English speaking, "cultural shock"or "cultural conflict" often arises when Chinese people communicate with Westerners, which leads to the realization that the pure-linguistic centered English teaching form cannot meet the demands of the social development any longer. Culture teaching must be introduced into English teaching in order to learn English language well and communicate well.This essay explains the importance of the cultural background knowledge teaching through analyzing the relationship of the language and culture as well as communicating barrier caused by the cultural differences, and it also submits falsifiable suggestions about how to introduce background knowledge into English teaching.

Updating conventional soil maps by mining soil–environment relationships from individual soil polygons

Conventional soil maps contain valuable knowledge on soil–environment relationships.Such knowledge can be extracted for use when updating conventional soil maps with improved environmental data.Existing methods take all polygons of the same map unit on a map as a whole to extract the soil–environment relationship.Such approach ignores the difference in the environmental conditions represented by individual soil polygons of the same map unit.This paper proposes a method of mining soil–environment relationships from individual soil polygons to update conventional soil maps.The proposed method consists of three major steps.Firstly,the soil–environment relationships represented by each individual polygon on a conventional soil map are extracted in the form of frequency distribution curves for the involved environmental covariates.Secondly,for each environmental covariate,these frequency distribution curves from individual polygons of the same soil map unit are synthesized to form the overall soil–environment relationship for that soil map unit across the mapped area.And lastly,the extracted soil–environment relationships are applied to updating the conventional soil map with new,improved environmental data by adopting a soil land inference model(SoLIM)framework.This study applied the proposed method to updating a conventional soil map of the Raffelson watershed in La Crosse County,Wisconsin,United States.The result from the proposed method was compared with that from the previous method of taking all polygons within the same soil map unit on a map as a whole.Evaluation results with independent soil samples showed that the proposed method exhibited better performance and produced higher accuracy.

Knowledge Transfer and Management Consulting:The Effect of Consultant and Client Characteristics

Knowledge transfer(KT)from the consultant to the client is an important area that needs to be repeatedly addressed and thoroughly understood.The aim of this research was to examine the assumption that client characteristics and consultant competencies play a defining role in the effective transfer of knowledge to the client party.The authors examined the critical aspects and competencies required of the consultant,and the characteristics and attitudes required of the client,which would contribute to a successful transfer of knowledge,through unstructured in-depth interviews and concise questionnaires.Eighty consulting assignments were studied from both the client side and the consultant side.A conceptual model is presented,factor analysis was used to validate the constructs,and partial least squares were used to test the model.The findings showed that the consultants’professionalism,skills,and behavior were significant contributors to KT to the client.Surprisingly,neither the consultant knowledge nor client characteristics had any significance to the KT to the client.

An adaptive representation model for geoscience knowledge graphs considering complex spatiotemporal features and relationships

Geoscience knowledge graph(GKG)can organize various geoscience knowledge into a machine understandable and computable semantic network and is an effective way to organize geoscience knowledge and provide knowledge-related services.As a result,it has gained significant attention and become a frontier in geoscience.Geoscience knowledge is derived from many disciplines and has complex spatiotemporal features and relationships of multiple scales,granularities,and dimensions.Therefore,establishing a GKG representation model conforming to the characteristics of geoscience knowledge is the basis and premise for the construction and application of GKG.However,existing knowledge graph representation models leverage fixed tuples that are limited in fully representing complex spatiotemporal features and relationships.To address this issue,this paper first systematically analyzes the categorization and spatiotemporal features and relationships of geoscience knowledge.On this basis,an adaptive representation model for GKG is proposed by considering the complex spatiotemporal features and relationships.Under the constraint of a unified spatiotemporal ontology,this model adopts different tuples to adaptively represent different types of geoscience knowledge according to their spatiotemporal correlation.This model can efficiently represent geoscience knowledge,thereby avoiding the isolation of the spatiotemporal feature representation and improving the accuracy and efficiency of geoscience knowledge retrieval.It can further enable the alignment,transformation,computation,and reasoning of spatiotemporal information through a spatiotemporal ontology.

简论新闻与日常生活的关系

新闻与日常生活的关系,是人与新闻关系的基础,是新闻学应该关注的重要研究对象,也是构建当代中国新闻学完整自主知识体系不可或缺的组成部分。从日常(生活)新闻本身、日常新闻与非日常新闻及非日常新闻与日常生活关系三个方面,对新闻与日常生活关系问题做了初步论述。研究认为:日常新闻就是关于日常生活事实的新闻;日常新闻与非日常新闻构成了共同的新闻世界;日常新闻与非日常新闻在人们的日常生活中具有不同的地位和作用。关于新闻与日常生活关系的系统研究还需要在未来继续展开。

人工智能时代的知识学习与教育变革

随着人工智能的崛起及其对部分人脑活动的替代,其“创造性毁灭”效应对社会带来巨变的同时也形成了前所未有的挑战,如何应对这种巨变及其挑战已经成为人工智能时代学习与教育领域的紧要任务。通过梳理人工智能对当今社会发展造成的影响与挑战,探讨了人工智能在改变人类生活环境与经济需求的同时对知识学习的目标、内容与方式的影响,说明人类在教育和知识学习上如何应对人工智能的挑战。人工智能作为人类智能的延伸与替代,虽然将会极大地改变包括人类教育与知识学习的方式和内容在内的教育的显性功能,但其工具实质不会改变教育与知识学习的本质属性。根据劳动者与人工智能的三重关系及人类自身发展的要求,在新的时代,人类更应以学习者为中心,保持持续学习直至终身学习的能力,重视立体复合知识及其转化能力的学习,培养新型立体多维复合型人才。与此相应,教育体制也必然需要进行适应性的改革,使教育实现更大规模和更深覆盖的同时,助力人的全面发展及“个体禀赋”的提升,从而在提升认知的过程中完善自我。

知识网络视角下科学、技术、产业间创新驱动关系识别方法研究

开展科学、技术、产业之间创新驱动关系研究,对于实现科学发现、技术创新与产业的深度有效融合与发展具有重要意义。本文从知识网络视角展开科学、技术、产业间创新驱动关系识别方法研究。首先,通过构建时效知识网络,利用社区网络结构熵来表征知识网络结构特征;其次,利用网络结构特征信息间的传递熵探究创新过程中科学、技术、产业三个系统相互驱动的关系;再其次,采用互信息探测主要驱动关系下知识传递路径的变化特点;最后,以再生医学(干细胞)领域作为实证对象,实证研究发现不同时间阶段,科学、技术、产业之间的相互驱动关系会发生转变,三者知识传递路径上知识内容前后继承关系也不同,科学驱动技术过程中的知识传递路径继承性特征持续增强,而技术驱动科学过程中的知识传递路径继承性呈现不稳定、非连续性特征。此外,通过多种方式论证了本文方法的科学有效性,可以用于其他创新领域科学、技术、产业间创新驱动关系的识别。

基于混合智能的街景影像知识提取方法

针对街景影像目标的智能化提取难题,本文提出了一种基于混合智能的街景影像知识提取方法(K-CAPSNet)。首先,在现有全景分割网络的基础上,同时关注街景影像的通道信息和空间信息,发展了一种联合注意力机制的全景分割网络,以提高目标分割精度;其次,将人们在生产、生活中形成的街景知识融入街景影像认知过程,借助先验知识设置目标标记阈值,对分割结果进行优化;然后,进一步根据街景影像先验知识验证街景目标之间的拓扑关系并利用深度信息进行空间关系知识挖掘;最后,采用语义模板对街景目标类型、数量及空间关系进行描述和表达。试验表明,相较于基线网络,本文方法在全景分割质量和识别质量方面都有明显提升,较好地实现了对街景影像知识的提取与表达。

知识状态集合准确界定的意义

教育认知诊断基于被试的作答反应估计其知识状态从而进行分类.尽管给定属性及其层级关系,其知识状态集合是唯一的,但对此并未形成共识.如有的软件包认为同一属性层级关系可对应2种不同的知识状态集合;有的研究仅仅给出属性及其层级关系的知识状态集合的真子集.针对这些情况,该文从理论上和应用上进行了分析,并给出修正的建议.

数字化能力对制造企业绿色创新的影响——关系强度与知识耦合的链式中介作用

数字化能力是数字价值释放的关键,也是企业运用数字技术整合价值网络中各种资源以推动系统性数字变革,进而实现数字价值创造的能力,但现有文献对数字化能力与绿色创新关系的探讨有限。基于动态能力理论,引入关系强度与知识耦合作为有因果的双中介变量,构建数字化能力通过关系强度、知识耦合作用于绿色创新的链式中介模型,采用多元回归和Bootstrap方法对409份制造企业问卷调查数据进行实证分析。结果表明:数字化能力正向影响制造企业绿色创新;关系强度与知识耦合在其中发挥部分中介作用;数字化能力通过关系强度、知识耦合的链式中介作用间接影响绿色创新。结论可揭示数字化能力对制造企业绿色创新的影响机制,为制造企业提高绿色创新绩效提供参考。

知识图谱技术及其在家禽产业中的应用

知识图谱作为结构化语义网络,能够存储实体间丰富的关系信息,广泛应用于多个领域,在家禽产业中展现出显著的实用价值。文章阐述了知识图谱的组成元素、主要技术流程及其在家禽产业中的应用现状和挑战,以期为知识图谱在家禽产业中的应用提供参考。

知识引导的视觉关系检测模型

视觉关系检测(VRD)任务是在目标识别的基础上,进一步检测目标对象之间的关系,属于视觉理解和推理的关键技术。由于对象之间交互组合,容易造成对象间关系组合爆炸的问题,从而产生很多关联性较弱的实体对,导致后续的关系检测召回率较低。针对上述问题,提出知识引导的视觉关系检测模型。首先构建视觉知识,对常见的视觉关系检测数据集中的实体标签和关系标签进行数据分析与统计,得到实体和关系间交互共现频率作为视觉知识;然后利用所构建的视觉知识,优化实体对的组合流程,降低关联性较弱的实体对得分,提升关联性较强的实体对得分,进而按照实体对的得分排序并删除得分较低的实体对,对于实体之间的关系也同样采用知识引导的方式优化关系得分,从而提升模型的召回率。在公开数据集视觉基因库(VG)和VRD中验证所提模型的效果:在谓词分类任务中,与现有模型PE-Net(Prototype-based Embedding Network)相比,在VG数据集上,召回率Recall@50和Recall@100分别提高了1.84和1.14个百分点;在VRD数据集上,相较于Coacher,Recall@20、Recall@50和Recall@100分别提高了0.22、0.32和0.31个百分点。

基于多模态和知识蒸馏的教材知识图谱构建方法

为了高效构建教育领域多模态学科知识图谱,提出了基于大模型知识蒸馏和多模型协作推理的教材文本实体关系抽取算法。在模型训练阶段,利用闭源的千亿参数模型对文本数据进行标注,实现隐式知识蒸馏。然后对开源十亿规模参数模型进行领域数据指令微调,提升开源模型实体关系抽取任务的指令遵循能力。在模型推理阶段,闭源模型作为指导模型,开源的十亿规模参数模型作为执行模型。实验结果表明知识蒸馏、多模型协作、领域数据指令微调具有有效性,显著提高了基于指令提示的教材文本实体关系抽取任务的效果。提出了显隐式知识增强的教材示意图多模态命名实体识别算法。利用图像OCR、视觉语言模型等技术提取了教材示意图中的文字信息、全局内容描述信息。通过显式知识库检索增强和隐式LLM提示增强的方法,得到图像-标题对中可能关联的辅助知识,并将显式知识库和隐式LLM得到的知识进一步融合,形成最终的辅助知识。将示意图辅助知识和示意图标题进行拼接,实现教材示意图标题的多模态命名实体识别。实验结果表明,该算法具有先进性,同时增强了算法的可解释性。

领导—员工关系对旅游企业员工知识隐藏行为的影响研究

基于中国旅游企业实践,文章认为领导—员工关系并非单一维度的社会交换关系,而是涵盖信任支持、理性契约和被动执行3个维度的复杂关系。在此基础上,文章援引资源保存理论,引入内部人身份感知为中介变量、心理安全感为调节变量,构建了领导—员工关系对员工知识隐藏的影响机制模型,并运用SPSS 26.0,对479份旅游企业员工问卷数据进行分析,结果发现:信任支持关系和理性契约关系与员工知识隐藏显著负相关,而被动执行关系与知识隐藏不显著相关;内部人身份感知分别在信任支持关系和理性契约关系影响知识隐藏的过程起到部分中介作用;心理安全感正向调节内部人身份感知与知识隐藏的关系。

军民融合战略背景下企业间价值共创对创新绩效的影响——关系学习、知识整合及数字化转型的作用机制

民口企业实现创新绩效增长与构建可持续竞争优势的关键是借助关系学习和知识整合能力获取创新资源。以创新生态系统理论为基础,结合军民融合战略背景,实证研究企业间价值共创对创新绩效的影响,以及关系学习、知识整合和数字化转型的作用机制。结果表明,企业间价值共创对企业创新绩效具有显著正向影响。此外,军民融合战略背景下,企业关系学习、知识整合在价值共创与企业创新绩效间发挥中介作用,企业数字化转型对价值共创和企业关系学习的关系具有正向调节作用。结论有助于揭示军民融合创新生态系统中企业创新绩效提升的复杂过程机制,为军民融合战略背景下我国民口企业通过价值共创促进创新和实现绩效增长提供理论依据与实践指导。