Automated Support for Building and Extending Expert Models

Building a knowledge-based system is like developing a scientific theory. Although a knowledge base does not constitute a theory of some natural phenomenon, it does represent a theory of how a class of professionals approaches an application task. As when scientists develop a natural theory, builders of expert systems first must formulate a model of the behavior that they wish to understand and then must corroborate and extend that model with the aid of specific examples. Thus there are two interrelated phases of knowledge-base construction: (1) model building and (2) model extension. Computer-based tools can assist developers with both phases of the knowledge-acquisition process. Workers in the area of knowledge acquisition have developed computer-based tools that emphasize either the building of new models or the extension of existing models. The PROTÉGÉ knowledge-acquisition system addresses these two activities individually and facilitates the construction of expert systems when the same general model can be applied to a variety of application tasks.     

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.     

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。     

知识获取问题初探

本文借助于PCP心理学和模糊数学理论,介绍了一种新型的知识获取方法.     

知识获取研究

知识获取是知识从外部知识源到计算机内部的转换过程,是当前知识工程研究的热点和难点问题之一。该文阐述了知识获取的定义、方法和最终目标,重点介绍了显性知识和隐性知识的获取方法,并分析了这两类方法的优缺点,最后给出了知识获取的困难和一些改进思想。     

关于知识工程的发展综述

人工智能与计算机技术的结合产生了所谓"知识工程",本文首先从知识工程的基本概念入手,依次对其包含的知识的获取、知识的表示和知识的运用三大要素进行详细的总结概述,同时提出将知识管理加入知识工程对于其实戢应用的巨大作用.     

基于本体的几何学知识获取及知识表示

文中研究了基于本体的几何学知识获取,并运用此方法获取几何学领域中的类、属性、关系及公理等;同时,研究了基于本体的知识表示,并将几何学知识转化为该领域本体,用RDF/OWL的形式进行描述.在此基础上构建了一个可共享、可重用、可扩展的几何学本体,形成了一套较完整的几何学知识获取和知识表示体系,将其应用于建立几何学知识库,可大大提高知识搜索和知识推理的效率,并为几何学专家系统、几何学信息检索、几何学教育系统、自然语言理解等领域提供智能基础.     

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.     

Knowledge Acquisition Advisor (KA): An interactive knowledge-acquisition tool for expert system development

A new interactive knowledge acquisition tool, called Knowledge Acquisition Advisor (KA²), is presented in this paper. The new tool will help knowledge engineers to conduct effective knowledge-elicitation interviews with domain experts through structured knowledge acquisition for both analytic and synthetic problems. A graphic modeling data structure, called Knowledge Graph is proposed, which allows knowledge engineers to model domain problems with their images and understanding. By using Knowledge Graph, knowledge engineers are able to decompose a domain problem into several components, to model the feature of each component, and to explore their relations by linking them with sets of questions. These questions can later be employed to guide the KA interview. Moreover, KA² is particularly useful for interview through computer networks, so the knowledge acquisition can take place remotely.     

Parallel Automated Knowledge Acquisition of Case-Based Semantic Networks from Relational Databases

The number of databases that are accessible over networks within organizations is increasing. This paper presents a methodology for automatically converting the data in these databases into a useful knowledge base of case-based semantic networks that can be accessed through a browsing facility. A parallel processing strategy has been implemented for this knowledge acquisition process to support its scalability to large databases. This methodology has potential application in the development of organizational intranets. It can also be used for retrospective browsing of the context of interesting patterns discovered by data mining. The database examples used in this paper are from clinical laboratories that provide data to a hospital infection control committee. Even though the results presented here use a single domain, the methodology can be used with no changes to explore the construction of multidomain knowledge bases.     

基于多知识源的语义搭配知识获取及表示方法

为更全面准确地从字词搭配中获取语义搭配信息,提出一种新的语义搭配知识提取模型和知识库的表示方法.利用特殊度度量词语搭配的相关程度,在此基础上,基于知网的语义信息,实现从42万条记录的词语搭配中定量地抽取语义搭配信息.实验结果表明,该方法的语义搭配准确率为92.1％,且较大地扩充了字词搭配的规模.     

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。     

基于专利的知识获取系统研究

应用现有的数据库技术、专利知识以及专家系统技术，研制了一个基于专利的知识获取系统，方便了以数据库为载体的专利知识系统的管理维护一体化。     

农业智能系统的知识获取技术研究

作者及其团队长期针对农业领域的知识获取技术进行了系列性研究.阐述了运用智能引导、机器学习、数据挖掘、智能计算等技术的人工和自动/半自动的知识获取方法.这些方法能够有效地获取领域知识,发现隐含模式,进行知识精化.研发了知识获取工具.这些方法和工具反映了知识获取技术对农业信息工程所起的重要作用.     

大学水平的“数学分析知识”的获取和分析研究

在过去的几十年里,数学软件系统变得越来越强大和复杂.各种数学系统的集成变得非常有意义.基于Web的分布式数学平台使得与数学相关的活动（称之为数学服务）都可以在Web上得以实现.所有这些都需要一个底层的、可共享的、面向内容的知识库作为支撑.本文介绍一个大学水平的、可共享的数学分析知识库的获取和分析方法.NKIMath采用基于本体、框架、逻辑和类型的形式表示,采用面向概念的知识获取方法,从一套大学本科数学分析课本（2本）中获取了所有主要概念和定理.本文还总结和分析了知识获取过程中出现的错误.     

基于模拟退火算法的知识获取方法的研究

从优化角度提出了从事例中获取知识的机器学习方法。该方法利用模拟退火算法,按照预定的优化目标,从事例中生成最优的产生式规则,给出其算法,并以旋转机械故障诊断知识获取为例,阐述了基于模拟退火算法的知识获取机制及其实现方法。     

A Safe-Control Paradigm for Human–Robot Interaction

This paper introduces a new approach to control a robot manipulator in a way that is safe for humans in the robot's workspace. Conceptually the robot is viewed as a tool with limited autonomy. The limited perception capabilities of automatic systems prohibits the construction of failsafe robots of the capability of people Instead, the goal of our control paradigm is to make the interaction with a robot manipulator safe by making the robot's actions predictable and understandable to the human operator. At the same time the forces the robot applies with any part of its body to its environment have to be controllable and limited. Experimental results are presented of a human-friendly robot controller that is under development for a Barrett Whole Arm Manipulator robot.     

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.