网络化教育教学资源智能检索知识库设计

在对网络化教育的现状进行分析的基础上,分析网络教学资源知识表示与推理、机器学习、数据挖掘、信息过滤等多项技术构建了智能化检索系统知识库模型,并对该系统执行机制进行了相关介绍。重点对该知识库的结构模式,相关知识获取、以及推理过程等关键技术问题进行了描述和分析。     

粗分析在智能决策支持中的应用

专家知识的归纳获取是智能系统知识自动获取的有效方法.本文针对目前归纳学习中存在的问题,指出粗集理论用于决策支持弥补了传统决策方法的不足.此外还分析粗集和其它软计算方法的集成问题,综述了基于粗集理论的软计算在智能决策支持中的应用,并提出了粗集发展的几个可能方向.     

组织学习影响组织创造力的知识获取路径研究

为了探明组织学习方式与组织创造力之间的相关关系,基于知识的基础观理论,尝试从外部知识获取的视角出发,探索组织学习方式与组织创造力之间的内在机理,根据市场知识与技术知识信息偏重的不同,构建组织学习方式影响组织创造力的外部知识获取路径.采用问卷调查收集275个有效的企业样本,运用结构方程模型方法对样本数据进行统计分析.研究结果表明,探索式学习通过影响市场知识获取进而推动技术知识获取,最终影响组织创造力的作用路径,利用式学习则主要通过对技术知识获取的促进影响组织创造力,技术知识和市场知识获取在组织学习影响组织创造力过程中扮演不同的中介角色,探索式学习能更有效地推动外部知识获取,从而对组织创造力产生影响.     

关系特征、资源获取与初创企业绩效关系实证研究

网络关系在新企业创建和成长过程中具有关键作用,但究竟何种网络特征能够促进新企业资源获取和绩效提高尚不明晰.本文以初创企业为对象,构建了关系特征、资源获取与新企业绩效之间的理论模型,利用322家成立时间在42个月内初创企业的调研数据,对理论模型和假设进行实证分析.分析结果表明,关系强度对知识资源和运营资源获取具有正影响;关系信任对知识资源和运营资源获取具有正影响;网络规模对知识资源获取具有正影响,知识资源获取对新企业财务和成长绩效具有正影响;知识资源获取对运营资源获取具有正影响;知识资源获取对运营资源获取与新企业绩效间关系具有正向的调节作用.但网络规模对运营资源获取的影响不显著,运营资源对初创企业财务和成长绩效的影响不显著.     

本土代工企业产品研发体系构建理论与案例

代工模式是本土企业与跨国公司合作的主要方式,也是本土企业获取先进技术和知识的重要途径.整合组织间学习理论和组织内学习理论,分析代工企业构建产品研发体系的理论过程,建立代工企业从知识获取到研发能力提升过程机制的理论模型,并以鸿海集团为例进行案例分析.研究结果表明,代工企业产品研发体系的构建取决于企业外部知识的获取、内部知识的消化和知识创新等过程,代工企业通过建立学习机制提高合作双方的互动程度,通过建立互信的合作关系以及内部知识的共享、转移和创新机制等措施提高产品研发能力.     

企业客户知识的获取过程及智能实现

从客户知识的产生与获取出发,研究了知识管理系统和知识发现技术等相关技术,以及实现客户知识智能化的过程.     

基于知识共享重用的分布式IDSS 开发平台

知识共享和重用是解决知识危机和软件危机的重要而有效的技术, 是知识工程、人工智能和信息系统中的重要研究内容. 本文介绍该技术在建立分布式协同智能决策支持系统的知识系统及其知识库模型方面的应用, 详细描述了基于知识共享和重用技术的DC IDP 的体系结构、构件库模型及其实现和基于本体论的知识库模型及其实现, 其方法和成果具有普遍意义.     

基于构模知识的管理模型构建方法研究

管理模型的自动构建是模型管理系统一个重要功能.本文提出了一种基于构模知识的管理模型自动构建方法.该方法能从专家构建模型的例子中自动获取构模知识,并在应用的时候,利用这些知识自动构建新的模型.构模过程可以用状态转换描述,构模知识分为模型知识、领域知识和数据知识三个层次,在此基础上,本文着重研究了数据的语义模型和数据知识自动获取算法.     

知识管理过程对新产品开发绩效的影响研究

知识管理是高动荡外部环境下影响新产品开发成败的决定性因素,本文构建了知识管理过程对新产品开发绩效影响的理论模型,研究了知识管理的基本过程及其对新产品开发绩效的影响,并考察了产品创新性和环境动荡性的调节作用。利用来自中国十一个省市206个新产品开发项目的问卷调查数据进行实证分析。研究结果表明知识获取和知识整合对新产品开发绩效具有显著影响,环境动荡性正向调节知识获取、创造、整合与新产品开发绩效关系,而知识创造对新产品开发绩效的影响不显著,产品创新性对知识获取、创造、整合与新产品开发绩效之间关系的调节影响均不显著。     

资源获取、资源管理对创业绩效的影响研究

将创业资源获取划分为运营资源获取与知识资源获取,分别研究其与创业绩效的关系,并引入资源管理这一调节变量,探讨资源管理对资源获取两个维度与创业绩效关系的不同调节作用。以165家创业企业为样本进行实证分析,研究表明,不同类型的资源获取对创业绩效的影响不同:运营资源获取与创业绩效呈倒U型关系;知识资源获取与创业绩效呈U型关系。资源管理在不同类型资源获取与创业绩效之间发挥不同的调节作用:资源管理强化了运营资源获取与创业绩效之间的倒U型关系,弱化了知识资源获取与创业绩效之间的U型关系。     

Integrating Neural Networks and Semi-Markov Processes for Automated Knowledge Acquisition: An Application to Real-time Scheduling*

Recently, artificial neural networks (ANN) have gained attention as a promising modeling tool for building intelligent systems. A number of applications have been reported in areas varying from pattern recognition to bankruptcy prediction. In this paper, we present a creative methodology that integrates computer simulation, semi-Markov optimization, and ANN techniques for automated knowledge acquisition in real-time scheduling. The integrated approach focuses on the synergy between operations research and ANN in eliciting human knowledge, filtering inconsistent data, and building competent models capable of performing at the expert level. The new approach includes three main components. First, computer simulation is used to collect expert decisions. This step allows expert knowledge to be obtained in a non-intrusive way and minimizes the difficulties involved in interviewing experts, constructing repertory grids, or using other similar structures required for manual knowledge acquisition. The data collected from computer simulation are then optimized using a semi-Markov decision model to remove data redundancies, inconsistencies, and errors. Finally, the optimized data are used to build ANN-based expert systems. The integrated approach is evaluated by comparing it with the human expert and using ANN alone in the domain of real-time scheduling. The results indicate that ANN-based systems perform worse than human experts from whom the data were collected, but the integrated approach outperforms human experts and ANN models alone.     

Determining Differences in Expert Judgment: Implications for Knowledge Acquisition and Validation*

In knowledge acquisition, it is often desirable to aggregate the judgments of multiple experts into a single system. In some cases this takes the form of averaging the judgments of those experts. In these situations it is desirable to determine if the experts have different views of the world before their individual judgments are aggregated. In validation, multiple experts often are employed to compare the performance of expert systems and other human actors. Often those judgments are then averaged to establish performance quality of the expert system. An important part of the comparison process should be determining if the experts have a similar view of the world. If the experts do not have similar views, their evaluations of performance may differ, resulting in a meaningless average performance measure. Alternatively, if all the validating experts do have similar views of the world then the validation process may result in paradigm myopia.     

Automating Knowledge Acquisition and Refinement for Decision Support: A Connectionist Inductive Inference Model*

An important application of expert systems technology is to provide support for nonstructured decision making. Usually, nonstructured decision making is characterized by heavy reliance on heuristic knowledge, which is very difficult to articulate or document, and therefore traditional knowledge acquisition approaches are not very successful. The quality and effectiveness of an expert system supporting unstructured decision making is affected when traditional knowledge acquisition approaches are used. To alleviate this problem a model is proposed that combines inductive inference and neural network computing, and an example is presented that illustrates the potential of this model in unstructured decision support.     

Semantic network representation of computer-mediated discussions: Conceptual facilitation form and knowledge acquisition

Prior research on computer-mediated discussions examined their effects on knowledge acquisition without considering the role of the discussion representation. In this study, we investigate and compare the effect of semantic network discussion representations on knowledge acquisition to that of the threaded representations featured in most traditional discussion forums. Furthermore, we identify, define and operationalize a new, i.e., conceptual facilitation (validation of the conceptual organization of the discussion), assessing its role in knowledge acquisition at different levels of restrictiveness. The empirical results of a field experiment indicate that semantic network discussion representations enable the acquisition of more complex and better-integrated knowledge structures than threaded discussion representations. Conceptual facilitation forms entailing different levels of restrictiveness are also found to play a significant role. As the level of conceptual facilitation restrictiveness decreases, knowledge acquisition improves. Our findings empirically demonstrate the importance of accounting for discussion representation as a contingency factor in explaining group discussion processes and outcomes. To practitioners, our study provides empirical evidence on the advantages of semantic networks over threaded representations as an alternative mode for computer-mediated discussion representations. We also suggest guidelines for the selection of appropriate conceptual facilitation for discussion forums intended for knowledge acquisition.     

Combining Experts’ Judgments: Comparison of Algorithmic Methods Using Synthetic Data

Expert judgment (or expert elicitation) is a formal process for eliciting judgments from subject‐matter experts about the value of a decision‐relevant quantity. Judgments in the form of subjective probability distributions are obtained from several experts, raising the question how best to combine information from multiple experts. A number of algorithmic approaches have been proposed, of which the most commonly employed is the equal‐weight combination (the average of the experts’ distributions). We evaluate the properties of five combination methods (equal‐weight, best‐expert, performance, frequentist, and copula) using simulated expert‐judgment data for which we know the process generating the experts’ distributions. We examine cases in which two well‐calibrated experts are of equal or unequal quality and their judgments are independent, positively or negatively dependent. In this setting, the copula, frequentist, and best‐expert approaches perform better and the equal‐weight combination method performs worse than the alternative approaches.     

The Aggregation of Expert Judgment: Do Good Things Come to Those Who Weight?

Good policy making should be based on available scientific knowledge. Sometimes this knowledge is well established through research, but often scientists must simply express their judgment, and this is particularly so in risk scenarios that are characterized by high levels of uncertainty. Usually in such cases, the opinions of several experts will be sought in order to pool knowledge and reduce error, raising the question of whether individual expert judgments should be given different weights. We argue—against the commonly advocated “classical method”—that no significant benefits are likely to accrue from unequal weighting in mathematical aggregation. Our argument hinges on the difficulty of constructing reliable and valid measures of substantive expertise upon which to base weights. Practical problems associated with attempts to evaluate experts are also addressed. While our discussion focuses on one specific weighting scheme that is currently gaining in popularity for expert knowledge elicitation, our general thesis applies to externally imposed unequal weighting schemes more generally.     

Expert knowledge,expert systems and commercial interests

Experience to date in building expert systems has brought a general realization of the complexity of the effort required for producing systems capable of operating on ‘real-world’ problems. The most successful knowledge-based/expert systems built to date, e.g. DENDRAL, MYCIN, PROSPECTOR and R1 have demonstrated that a considerable investment in time and dedication on the part of systems designers and experts is required to create a fully operational system. The great majority of development projects attempting to harness this new programming technology do not extend beyond creation of a prototype system largely due to lack of necessary resources, i.e. time, money and know-how. The purpose of this paper is to discuss some of the more important issues associated with development of expert systems and to provide an overview of the commercial and industrial efforts of putting expert systems to work. This paper should be of interest to those who have gained their knowledge of expert systems from the reading of non-specialist publications and thus may have been exposed to somewhat over enthusiastic accounts of the subject.     

在线负面口碑处理的专家识别方法研究

现有专家识别大多建立在专家知识能力的基础上，然而在负面口碑处理的专家识别中，仅考虑知识能力并不能满足各主体的价值需求。本文从资源角度理解专家识别，建立了专家识别资源映射框架，并依据此框架分析了专家识别的显性资源映射和隐性资源映射过程。除了考虑直接体现用户专业水平的知识能力，还考虑了专家参与的情感抚慰能力和互动程度能力，以此构建的人工神经网络模型在实验中表现出了良好的性能。     

企业隐性知识导航方法研究

在对企业隐性知识特性及其研究现状分析的基础上,提出了企业隐性知识导航方法。该方法包括2个方面内容:基于平衡计分卡的专家导航方法,充分考虑了专家之间的能力差别;基于多关系专家网络的专家导航路径查询方法和相关专家查询方法,充分考虑了专家之间的多重关系,该方法为管理者发现及考核专家提供了依据。在此基础上,利用社会网络分析工具Sociom etryP ro对专家网络进行评价,得到了专家个人特征结果,并指出了改善企业隐性知识共享的具体措施。     

Infrastructure Vulnerability Assessment Model (I-VAM)

Quantifying vulnerability to critical infrastructure has not been adequately addressed in the literature. Thus, the purpose of this article is to present a model that quantifies vulnerability. Vulnerability is defined as a measure of system susceptibility to threat scenarios. This article asserts that vulnerability is a condition of the system and it can be quantified using the Infrastructure Vulnerability Assessment Model (I-VAM). The model is presented and then applied to a medium-sized clean water system. The model requires subject matter experts (SMEs) to establish value functions and weights, and to assess protection measures of the system. Simulation is used to account for uncertainty in measurement, aggregate expert assessment, and to yield a vulnerability (Omega) density function. Results demonstrate that I-VAM is useful to decisionmakers who prefer quantification to qualitative treatment of vulnerability. I-VAM can be used to quantify vulnerability to other infrastructures, supervisory control and data acquisition systems (SCADA), and distributed control systems (DCS).