一种专家地图构建方法

专家地图是专家个人知识和技能信息及专家协作网络信息的记录。基于图论的方法给出专家发现社会协作网的算法。定义一种从企业局域网的异构数据文档中发现专家地图的任务,给出实现该任务的多种模型,并将模型应用于TRCKMS系统的开发中。在W3C语料库上进行的实验表明,该模型能够提高专家发现的效率。     

专家知识地图的关键技术与设计

专家隐性知识是社会的宝贵财富。开发专家知识地图是有效利用专家知识的重要途径。构建专家知识地图的关键技术包括专家信息的获取、知识地图的体系结构与实现技术。本文给出了通过Web挖掘获取专家基本信息、特长信息和社会网络的算法,以及一个专家知识地图的设计方案。     

引文作者主题演化模型在专家检索方面的应用

目前基于科技文献的专家检索方法大多数是静态地获取专家信息,而动态演化的分析方法很少考虑文献的作者、引文作者等外部信息,且很少应用于专家检索领域。基于此,在CAT和ToT模型的基础上构建了引文作者主题演化（CAToT）模型,并给出了一种估计CAToT模型参数的吉布斯采样方法以及该模型在专家检索方面应用的方法。该模型集成了CAT和ToT模型的优势,不仅可以揭示科技文献中隐含的主题、与主题相关的作者和引文作者,而且可以挖掘主题随时间变化的规律以及专家排名的演化规律。以1 557篇ACL、CONLL、EMNLP的会议论文集作为实验数据,通过与CAT模型的对比分析验证了CAToT模型的可行性和有效性。     

贝叶斯网络查询语义扩展的专家发现方法

专家发现是实体检索领域的一个研究热点，针对经典专家发现模型存在索引术语独立性假设与检索性能低的缺陷，提出一种基于贝叶斯网络模型的专家发现方法。该方法模型采用四层网络结构，能够实现图形化的概率推理，同时运用词向量技术能够实现查询术语的语义扩展。实验结果显示该模型在多个评价指标上均优于经典专家发现模型，能够有效实现查询术语语义扩展，提高专家检索性能。     

流程增量挖掘中的模型更新方法

正确发现流程实际运作情况对工作流管理有着重要的意义.流程挖掘抽取系统日志信息,挖掘流程的真实运作模型.目前很多该方面的研究,着重于从一份日志中挖掘出工作流模型.然而,这些挖掘方法只关注日志信息,忽略了流程设计者的先验知识.而且,日志所包含信息量较大,进行一次挖掘耗费较大.因此,希望能结合已有工作流模型及新增日志信息,更新工作流模型.已有研究给出对模型及日志的增量挖掘算法.但是,业务流程会随着时间推移变更,可能已有的任务被取消了,因此在新增的一段日志中该任务没被记录.但由于该任务曾经在已有日志中记录下来,故应用已有挖掘算法或增量挖掘算法,在更新模型中,该任务也会被挖掘出来.提出了一种增量挖掘模型更新的改进算法.通过流程设计者的先验知识及统计任务出现的频率,判断该任务是否被取消.最后给出一个实验,验证算法的可行性.     

基于元数据的异构专家信息管理研究

元数据在网络信息资源的管理、存储和检索中发挥着重大的作用。本文对元数据和基于元数据进行管理的理论基础进行了探讨,说明了元数据的内容、结构、生成过程和依据元数据的管理模式,采用XML描述元数据标准,提出了一种以元数据为核心,以元数据驱动任务操作的异构数据管理策略。将其思想加以扩展并抽象为一个框架模型应用到专家信息管理中,在元数据的层次上来定义和管理异构专家信息。     

专家寻找模型融合框架研究

网络中存在着规模庞大的信息,搜索引擎如Google为网络海量信息的检索提供了有效的途径,但是许多潜藏的知识仍然无法被搜索到。而且,大量知识并未存储于文档或者数据库中,其中大部分仅存在于人脑中。对于网络中无法检索到的知识,则需要找到掌握这些知识的专家,并通过交流获取这些知识。目前专家寻找的方法有语言模型、主题模型等,这些方法各有优缺点。提出一种专家寻找模型融合框架,该框架可有效地将已有的专家寻找模型结合起来,从而提高专家寻找的精确度与鲁棒性。实验结果支持了这一结论。     

基于知识库的专家咨询系统设计与实现

介绍了知识库的研究现状以及使用知识库对专家咨询系统的意义,论述了基于知识库的专家咨询系统的设计,分析了无人值守信息交互的知识地图与知识引擎的设计过程,实现了实时专家咨询模式的专家协同。     

面向对象的家庭全息地图表示与构建

针对现代复杂而多样化的家庭环境,人对家庭服务机器人的服务需求,以及其自身基于家庭全息地图的路径规划的任务要求,借鉴人对空间环境的认知和地图的组建过程,提出了一种改进的面向对象的家庭服务机器人全息地图表示方法,设计出家庭-房间-物品三层表示模型.依据面向对象的思想,分别给出了改进的物品层、房间层和家庭层的面向对象表示方法.基于机器人自身携带的传感器对各层对象的空间数据进行采集.将该表示方法转化成可存储在数据库中的数据类型,将采集的数据存储到数据库中并对地图进行实时的更新.在家庭环境下,机器人基于该全息地图分别对各层进行对象识别和路径规划,实物实验说明基于家庭-房间-物品表示模型的家庭全息地图能满足服务机器人任务的需要.     

面向Agent的车辆监控空间数据调度模型

设计面向Agent的车辆监控空间数据调度模型,通过软件Agent实体将静态海量地图数据与实时车辆空间数据的调度分解成原子任务进行处理并显示。该模型充分利用Agent的自治性、主动性和反应性特点,自主处理地图数据和车辆信息调度,在软件层次上实现车辆动态信息显示的实时性,有效解决了海量地图数据显示造成的响应过慢或停顿问题,降低了系统设计的复杂度,为如何将Agent思想和方法学应用于GIS开发提供了参考。     

An Effective Framework for Fast Expert Mining in Collaboration Networks: A Group-Oriented and Cost-Based Method

The growth of social networks in modern information systems has enabled the collaboration of experts at a scale that was unseen before.Given a task and a graph of experts where each expert possesses some skills,we tend to find an effective team of experts who are able to accomplish the task.This team should consider how team members collaborate in an effective manner to perform the task as well as how efficient the team assignment is,considering each expert has the minimum required level of skill.Here,we generalize the problem in multiple perspectives.First,a method is provided to determine the skill level of each expert based on his/her skill and collaboration among neighbors.Second,the graph is aggregated to the set of skilled expert groups that are strongly correlated based on their skills as well as the best connection among them.By considering the groups,search space is significantly reduced and moreover it causes to prevent from the growth of redundant communication costs and team cardinality while assigning the team members.Third,the existing RarestFirst algorithm is extended to more generalized version,and finally the cost definition is customized to improve the efficiency of selected team.Experiments on DBLP co-authorship graph show that in terms of efficiency and effectiveness,our proposed framework is achieved well in practice.     

Domain problem-solving expert identification in community question answering

Question-Answering (Q&A) services provide internet users with platforms to exchange knowledge and ideas. The development of Q&A sites, or Community Question Answering (CQA), mainly depends on the high-quality content continuously contributed by users with high-level expertise, who can be recognized as experts. Expert finding is an important task for the authorities of Q&A communities to encourage commitment. In a highly competitive market environment, CQA managers have to take measures to retain and nurture users, especially superior contributors. However, current expertise scoring techniques adopted in CQA often give much credit to very active users and fail to identify real experts. This study aims to develop a robust and practical expert identification framework for Q&A communities, by combining well-designed expertise scoring technique and probabilistic clustering model. With regard to expert identification, a numerical metric of users' expertise is developed as the optimal expert finding strategy, and a clustering algorithm based on Gaussian-Gamma mixture model (GGMM) is proposed to efficiently distinguish experts from nonexperts. In the experiments, the proposed method is applied to real-world datasets collected from subcommunities of Stack Exchange Q&A networks. Results obtained from comparative experiments show that our method achieves better performance than the state-of-the-art methods and demonstrate the effectiveness of the proposed framework. The analysis shows that the framework which combines the proposed expertise scoring technique and Gaussian–Gamma mixture clustering model is capable of detecting excellent domain problem-solving experts who exhibit both domain interest and expertise.     

Group Processes in Software Effort Estimation

The effort required to complete software projects is often estimated, completely or partially, using the judgment of experts, whose assessment may be biased. In general, such bias as there is seems to be towards estimates that are overly optimistic. The degree of bias varies from expert to expert, and seems to depend on both conscious and unconscious processes. One possible approach to reduce this bias towards over-optimism is to combine the judgments of several experts. This paper describes an experiment in which experts with different backgrounds combined their estimates in group discussion. First, 20 software professionals were asked to provide individual estimates of the effort required for a software development project. Subsequently, they formed five estimation groups, each consisting of four experts. Each of these groups agreed on a project effort estimate via the pooling of knowledge in discussion. We found that the groups submitted less optimistic estimates than the individuals. Interestingly, the group discussion-based estimates were closer to the effort expended on the actual project than the average of the individual expert estimates were, i.e., the group discussions led to better estimates than a mechanical averaging of the individual estimates. The groups ability to identify a greater number of the activities required by the project is among the possible explanations for this reduction of bias.     

Authority updating: An expert authority evaluation algorithm considering post‐evaluation and power indices in social networks

In group assessment, the focus is on finding high‐authority experts to improve the reliability of assessment results. In this study, we propose an authority updating algorithm while considering the power and judgement reliability of an expert on the basis of social networks and post‐evaluations. A network power index is established and used to reflect the power of an expert while considering social networks. The measurement of the judgement reliability of an expert considers the post‐evaluation of the objects selected by experts, thereby more scientifically reflecting the reliability of experts. The analysis shows the following: although the social‐network structure influences the authority of experts, the influence weakens when the assessment group is a highly or even fully connected group; the network effect may increase the authority of some experts and reduce that of others, and it will weaken as the network connectivity increases; moreover, the judgement reliability and authority of an expert while considering post‐evaluation can encourage him/her to make fair assessments and strive to reduce his/her motivation and cognitive biases.     

Group prioritisation with unknown expert weights in incomplete linguistic context

In this paper, we study a group prioritisation problem in situations when the expert weights are completely unknown and their judgement preferences are linguistic and incomplete. Starting from the theory of relative entropy (RE) and multiplicative consistency, an optimisation model is provided for deriving an individual priority vector without estimating the missing value(s) of an incomplete linguistic preference relation. In order to address the unknown expert weights in the group aggregating process, we define two new kinds of expert weight indicators based on RE: proximity entropy weight and similarity entropy weight. Furthermore, a dynamic-adjusting algorithm (DAA) is proposed to obtain an objective expert weight vector and capture the dynamic properties involved in it. Unlike the extant literature of group prioritisation, the proposed RE approach does not require pre-allocation of expert weights and can solve incomplete preference relations. An interesting finding is that once all the experts express their preference relations, the final expert weight vector derived from the DAA is fixed irrespective of the initial settings of expert weights. Finally, an application example is conducted to validate the effectiveness and robustness of the RE approach.     

Expert knowledge acquisition and the unwilling expert: a knowledge engineering perspective

Expert systems are an evolving technology with the potential to make human expertise widely and cheaply available. The literature describing the development of expert systems generally assumes that experts willingly give up their knowledge. This is unrealistic and may be a reason why most expert system projects fail. This paper explores the problem of unwilling experts from the perspective of a knowledge engineer building an expert system. The link between knowledge and organizational power is established and human motivation theories are discussed. Finally, a new motivational approach is introduced to help the knowledge engineer deal with unwilling experts.     

A survey on expert finding techniques

Finding experts in specified areas is an important task and has attracted much attention in the information retrieval community. Research on this topic has made significant progress in the past few decades and various techniques have been proposed. In this survey, we review the state-of-the-art methods in expert finding and summarize these methods into different categories based on their underlying algorithms and models. We also introduce the most widely used data collection for evaluating expert finding systems, and discuss future research directions.     

A hierarchical expert system for computer process control

This paper reports on the design and implementation of an expert system for computer process control (HESCPC). The complexity of the expertise necessary for computer process control applications requires that the expert system architecture be structured into a hierarchy of classes of specialized experts. The architecture of HESCPC integrates four classes of expert systems: operator/manager companion expert class, control system algorithm design expert class, hardware expert class, and software expert class. The paper is concerned with the design and implementation of the general system architecture, an operator adviser expert for a nuclear power plant and a control system designer expert using a state space feedback approach. Although the design and implementation aspects of all experts are discussed, the emphasis is on the latter.

At this stage of the HESCPC development, the declarative knowledge represented by 423 metarules and 1261 rules is distributed on a hierarchical structure among 20 experts on different levels of the hierarchy which are able to communicate among themselves to solve difficult control problems.

Examples of control system design sessions of linear mono and multivariable systems using feedback state space approach are given. A run time of an operator-adviser data-driven expert system for a nuclear plant is also presented.     

决策关联分析下的专家权重自适应调整研究

针对多属性群决策问题,在已知属性权重和专家主观权重的基础上,提出了一种基于决策关联度的专家权重自适应调整方法。通过专家的个体决策与群体决策之间的决策关联度对专家的客观权重进行调整,从而得到相对稳定的决策结果和相应的专家综合权重。实验结果表明调整后的专家权重与决策结果是合理的,而且收敛速度更快。     

Review Expert Collaborative Recommendation Algorithm Based on Topic Relationship

The project review information plays an important role in the recommendation of review experts. In this paper, we aim to determine review expert's rating by using the historical rating records and the final decision results on the previous projects, and by means of some rules, we construct a rating matrix for projects and experts. For the data sparseness problem of the rating matrix and the “cold start” problem of new expert recommendation, we assume that those projects/experts with similar topics have similar feature vectors and propose a review expert collaborative recommendation algorithm based on topic relationship. Firstly, we obtain topics of projects/experts based on latent Dirichlet allocation (LDA) model, and build the topic relationship network of projects/experts. Then, through the topic relationship between projects/experts, we find a neighbor collection which shares the largest similarity with target project/expert, and integrate the collection into the collaborative filtering recommendation algorithm based on matrix factorization. Finally, by learning the rating matrix to get feature vectors of the projects and experts, we can predict the ratings that a target project will give candidate review experts, and thus achieve the review expert recommendation. Experiments on real data set show that the proposed method could predict the review expert rating more effectively, and improve the recommendation effect of review experts.