“知识管理”在软件企业中应用的分析和研究

在新经济时代，知识经济越来越被人们所推崇，同时知识管理也正成为一个热门话题，越来越多的企业正忙于建没企业级知识库，推行企业级的知识管理。该就如何在软件企业中推行知识管理进行了探讨。     

从文本中获取植物知识方法的研究

知识获取一直是人工智能中的一个关键问题.当前,知识的文本挖掘（KAT）已经成为计算机领域的一个重要的研究课题.本文中,给出了基于植物本体的从海量网页文本库中自动获取植物领域知识的方法.该方法包括两个部分,一是植物本体（Botanical Ontology）,它是顾芳博士等建立的生物本体的扩展.第二部分是以植物本体为基础,在网络文本库中进行文本挖掘（Text Mining）,自动获取植物知识.实验证明,基于本体的文本挖掘是一种有效的知识获取方法.     

A Survey of Commonsense Knowledge Acquisition

Collecting massive commonsense knowledge (CSK) for commonsense reasoning has been a long time standing challenge within artificial intelligence research. Numerous methods and systems for acquiring CSK have been developed to overcome the knowledge acquisition bottleneck. Although some specific commonsense reasoning tasks have been presented to allow researchers to measure and compare the performance of their CSK systems, we compare them at a higher level from the following aspects: CSK acquisition task (what CSK is acquired from where), technique used (how can CSK be acquired), and CSK evaluation methods (how to evaluate the acquired CSK). In this survey, we first present a categorization of CSK acquisition systems and the great challenges in the field. Then, we review and compare the CSK acquisition systems in detail. Finally, we conclude the current progress in this field and explore some promising future research issues.     

An Analysis of Knowledge Acquisition Roles and Participants

Much recent research effort in the field of knowledge acquisition (KA) has focussed on extending knowledge acquisition techniques and processes to include a wider array of participants and knowledge sources in a variety of knowledge acquisition scenarios. As the domain of expert systems applications and research has expanded, techniques have been developed to acquire and incorporate knowledge from groups of experts and from various sources such as text, video, and audio tapes. However, the dominant participant-role model remains that of the knowledge engineer eliciting knowledge from one or more human experts. This conceptual gap has contributed to the major divisions in the KA field between researchers interested in manual KA and those developing tools for automated KA. This article considers the wide variety of possible KA scenarios and presents a meta-view of KA participants and the roles they may assume.We suggest that it is more appropriate to think of knowledge acquisition participants as playing one or more roles. These include knowledge sources, agents and targets for KA processes. We also present a participant model drawn from research in decision support systems that more accurately characterizes the diversity of the entities participating in the KA process. This view is more inclusive as it allows us to consider both human-human and human-computer KA interactions as well as the whole variety of knowledge sources and targets. A careful consideration of the meta-view and its associated role-participant mappings also yields the new ideas of the elemental and composite role and the multi-role entity. These new constructs are then used to identify areas where research is currently needed and to generate specific research issues. Taken altogether, this view allows a more flexible consideration of the many possible combinations that can and frequently do occur in actual KA situations.     

Automated Knowledge Acquisition for Strategic Knowledge

Strategic knowledge is used by an agent to decide what action to perform next, where actions have consequences external to the agent. This article presents a computer-mediated method for acquiring strategic knowledge. The general knowledge acquisition problem and the special difficulties of acquiring strategic knowledge are analyzed in terms of representation mismatch: the difference between the form in which knowledge is available from the world and the form required for knowledge systems. ASK is an interactive knowledge acquisition tool that elicits strategic knowledge from people in the form of justifications for action choices and generates strategy rules that operationalize and generalize the expert's advice. The basic approach is demonstrated with a human–computer dialog in which ASK acquires strategic knowledge for medical diagnosis and treatment. The rationale for and consequences of specific design decisions in ASK are analyzed, and the scope of applicability and limitations of the approach are assessed. The paper concludes by discussing the contribution of knowledge representation to automated knowledge acquisition.     

基于服装知识本体的知识获取与联通分析

服装知识在服装知识查询系统、计算机辅助服装工艺设计和服装智能教学系统等高技术产品中具有重要的作用,因此有必要建立一个健壮、协调、具有良好联通性的服装知识库,以便为用户/服装设计师提供更为便捷和良好的知识服务。为此,论文提出了一种基于知识本体的服装知识获取方法,设计了较为完善的服装概念本体体系。另外,提出了知识联通的方法,以解决不同的知识源之间存在的如下问题一是知识的不一致性,二是知识粒度(Granularity)不同,三是知识的精度不同,以尽可能保证所获取知识的一致性、完备性和精确性。     

基于本体的医学知识获取

In this paper, we introduce an ontology-mediated method for medical knowledge acquisition and analysis.Using the method we establish an ontological structure and ontologies for the Medical Knowledge Base (or NKIMed ). To check the consistency of the acquired knowledge, we use a set of medicine-specific axioms. These axioms are also used in knowledge inference, and interconnection between diiferent medical concepts. Finally, two applications of NKIMed, i.e. intelligent teachinu systems and speech diagnosis are illustrated.     

基于事件的知识处理研究综述

本文对近年来基于事件的知识处理研究进行了综述,从事件的定义开始,到事件的表示、提取方法和具体应用来说明该领域的研究进展.许多科学家认为人们是以事件为单位来体验和认识世界的,事件符合人们的正常认知规律,对事件的研究有广阔的前景,将成为基于概念的知识处理技术的必要补充和发展,为知识处理注入新的活力.     

医学专家系统中知识表示、获取和推理的两种方法

文章提出使用模糊数学的方法和基于规则的神经网络的方法来构造一个呼吸道疾病方面的专家系统,包括知识的表示、获取和推理。对模糊数学方法,用模糊集来表示所考虑的症状与所有可能的疾病。医学知识存储在症状与疾病的模糊关系上。推理时使用模糊关系合成的方法。对基于规则的神经网络方法,从规则集中自动构造网络的结构,确定隐层节点数和连接权值。用并行的方法进行推理。     

基于场合和角色的情绪知识获取与分析

如何从海量的知识中发现、组织和表示情绪知识,是智能系统和常识研究的一个难点。该文以认知心理学为理论基础,分析了角色、场合和情绪的结构及其关系。从角色和场合出发对情绪知识进行获取与形式化,提出了一种通用的情绪知识获取方法。以交通场合为例,描述该场合下的角色、子场合与情绪规则的获取过程。对获取后的情绪知识存在的一致性、完备性和冗余性问题进行了分析。