Knowledge Acquisition最新文献

英文中文

A multi-functional knowledge management system 一个多功能的知识管理系统

Knowledge Acquisition

Pub Date : 1993-09-01 DOI: 10.1006/KNAC.1993.1011

D. Skuce

Abstract We describe a general purpose knowledge management system, discussing its general goals and features, as well as its use in several very different applications. By "multi-functional", we mean having a wide variety of knowledge management functions such as debugging, formatting, and retrieval, and a wide variety of possible applications. The system, called CODE, functions primarily as a "knowledge engineer's rapid prototyper", or as a "spreadsheet for ideas"; one can experiment rapidly with relationships between concepts and obtain quick feedback on the desirability of changes and additions to a knowledge base. CODE's highly graphic interface permits experimentation with descriptions or definitions of concepts, which are arranged in an inheritance network using a very flexible inheritance mechanism. Several associated subsystems, such as a first order logic system and a simple natural language system, allow various types of syntactic and semantic checks to be performed if desired. We illustrate CODE's flexibility by describing three typical applications: in software engineering, terminology, and ontological design for knowledge-based systems.

本文描述了一个通用的知识管理系统，讨论了它的总体目标和特点，以及它在几种非常不同的应用中的使用。通过“多功能”，我们的意思是具有各种各样的知识管理功能，如调试、格式化和检索，以及各种可能的应用程序。这个名为CODE的系统，主要功能是“知识工程师的快速原型”，或者是“想法的电子表格”;人们可以快速试验概念之间的关系，并获得对知识库进行更改和添加的需求的快速反馈。CODE的高度图形化界面允许对概念的描述或定义进行实验，这些描述或定义使用非常灵活的继承机制安排在继承网络中。几个相关的子系统，如一阶逻辑系统和简单的自然语言系统，允许在需要时执行各种类型的语法和语义检查。我们通过描述三个典型的应用来说明CODE的灵活性:软件工程、术语和基于知识的系统的本体设计。

引用次数: 13

Providing descriptive power to guided self-elicitation 提供描述性的力量来引导自我启发

Knowledge Acquisition

Pub Date : 1993-09-01 DOI: 10.1006/KNAC.1993.1012

G. G. Shephard

Abstract Recent exploratory research developed and tested a guided self-elicitation (GSE) methodology. With GSE, an expert is enabled to capture his/her own performed expertise as production rule-instances. GSE is based on published cognitive research, using a production system view of conscious cognitive information processing and certain demonstrated human abilities: for identifying and categorizing perception, rehearsing and reconstituting prior thought processes and verbal reporting of concurrent cognitive information processing. Experimentally self-elicited decision analyst expertise (leading subjective probability assessment interviews) demonstrates that performed expertise can consist of complex rule-processed knowledge forms. An object model for representing complex knowledge forms is proposed and discussed.

最近的探索性研究开发并测试了一种引导自我启发(GSE)方法。使用GSE，专家可以捕获他/她自己执行的专业知识作为生产规则实例。GSE基于已发表的认知研究，使用有意识认知信息处理的生产系统视图和某些已证明的人类能力:用于识别和分类感知，排练和重构先前的思维过程以及并发认知信息处理的口头报告。通过实验，自我引出的决策分析师专业知识(主要的主观概率评估访谈)表明，执行的专业知识可以由复杂的规则处理的知识形式组成。提出并讨论了一种表示复杂知识形式的对象模型。

引用次数: 2

Models: toward integrated knowledge modeling environments 模型:面向集成知识建模环境

Knowledge Acquisition

Pub Date : 1993-09-01 DOI: 10.1006/KNAC.1993.1010

M. Barbuceanu

Abstract Building knowledge-based problem solvers requires an intellectually challenging modeling stage whose dominance over other activities is now widely recognized. In spite of this, current languages and environments leave the modeling activity on the shoulders of the human, concentrating on the routine programming aspect. Next generation languages and tools will have to explicitly support modeling in the first place. This paper presents a proposal for such a next generation knowledge modeling environment and discusses some steps we have made in this direction. Unlike existing programming environments, knowledge modeling environments focus on manipulating explicit, declarative specifications of problem-solving models which must be acquired, organized, modified, explained, validated, simulated and, eventually, translated into performance computer languages. Programming is only one of the activities supported in such an environment. This paper also discusses the knowledge modeling language we have developed as the foundation of the modeling environment. This language extends term classification technology with refinement, constraints, patterns and events, actions and methods, in order to support the description of both domain and control specifications required by problem-solving models. To substantiate the claims about the adequacy of the language, the paper presents two important modeling applications. The first is developing a full KADS language on top of it and the second is modeling a well known generic problem solving method, "propose-and-revise".

构建基于知识的问题解决方案需要一个智力上具有挑战性的建模阶段，其对其他活动的支配地位现在已被广泛认识。尽管如此，当前的语言和环境将建模活动留在了人类的肩膀上，专注于日常编程方面。下一代语言和工具必须首先显式地支持建模。本文提出了这样一个下一代知识建模环境的建议，并讨论了我们在这个方向上所做的一些步骤。与现有的编程环境不同，知识建模环境侧重于操作问题解决模型的显式、声明性规范，这些模型必须被获取、组织、修改、解释、验证、模拟，并最终翻译成性能计算机语言。编程只是这种环境中支持的活动之一。本文还讨论了我们开发的知识建模语言作为建模环境的基础。这种语言通过细化、约束、模式和事件、操作和方法扩展了术语分类技术，以支持问题解决模型所需的域和控制规范的描述。为了证实该语言的充分性，本文提出了两个重要的建模应用。第一个是在它的基础上开发一个完整的KADS语言，第二个是为一个众所周知的通用问题解决方法“提议-修改”建模。

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引用次数: 19

Attribute focusing: machine-assisted knowledge discovery applied to software production process control 属性聚焦:应用于软件生产过程控制的机器辅助知识发现

Knowledge Acquisition

Pub Date : 1993-07-11 DOI: 10.1006/KNAC.1994.1014

I. Bhandari

Abstract How can people who are not trained in data analysis discover knowledge from a database of attribute-valued data? I address this question by presenting a man-machine approach to knowledge discovery called Attribute Focusing and its application to software production process control. Attribute Focusing utilizes an automatic filter to focus attention on that small part of a large amount of data which is interesting. A person studies that part in a manner which leads him to discover knowledge about the physical situation to which the data pertain. Specifically, the paper describes: 1. A model of interestingness of data based on the magnitude of data values, the association of data values and basic knowledge of the limits of human processing. 2. The use of that model of interestingness by people to discover knowledge. 3. The application of the Attribute Focusing approach to diagnose and correct the software production process. Based on the results that have been observed, the paper concludes that man-machine approaches to knowledge discovery should be emphasized much more than has been in the past, and that Attribute Focusing is a powerful, practical approach to such discovery.

没有受过数据分析训练的人如何从属性值数据的数据库中发现知识?我通过提出一种名为“属性聚焦”的人机知识发现方法及其在软件生产过程控制中的应用来解决这个问题。属性聚焦利用自动过滤器将注意力集中在大量数据中有趣的一小部分上。一个人以一种使他发现有关数据所属的物理情况的知识的方式研究该部分。具体来说，本文描述了:1。基于数据值的大小、数据值的关联和人类处理极限的基本知识的数据兴趣模型。2. 人们利用这种有趣的模式来发现知识。3.应用属性聚焦方法对软件生产过程进行诊断和纠正。根据观察到的结果，本文得出结论，人机方法的知识发现应该比过去更加重视，属性聚焦是一种强大的、实用的方法。

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引用次数: 36

Knowledge acquisition in the small: building knowledge-acquisition tools from pieces 小规模的知识获取：从碎片构建知识获取工具

Knowledge Acquisition

Pub Date : 1993-06-01 DOI: 10.1006/knac.1993.1009

Jay T. Runkel, William P. Birmingham

The knowledge-systems community is interested in easing the knowledge-system development process. One approach, the mechanisms approach, views knowledge systems as a set of tasks, each of which can be realized by a computation mechanism. To be effective, knowledge-acquisition (KA) tools must be automatically configured once a set of mechanisms has been selected. We present a method for automatically generating a model-based KA tool for a given set of mechanisms. The method advocates combining KA mechanisms, which acquire knowledge in the small, and a set of strategies that provide a global view of the KA activity. We show that these global strategies are necessary for the KA tool to efficiently interact with a domain expert.

知识系统社区对简化知识系统开发过程感兴趣。一种方法，即机制方法，将知识系统视为一组任务，每个任务都可以通过计算机制来实现。为了有效，一旦选择了一组机制，就必须自动配置知识获取（KA）工具。我们提出了一种为给定的一组机制自动生成基于模型的KA工具的方法。该方法主张将获取小规模知识的KA机制与一套提供KA活动全局视图的策略相结合。我们展示了这些全局策略对于KA工具与领域专家有效交互是必要的。

引用次数: 20

A translation approach to portable ontology specifications 一种可移植本体规范的翻译方法

Knowledge Acquisition

Pub Date : 1993-06-01 DOI: 10.1006/knac.1993.1008

Thomas R. Gruber

To support the sharing and reuse of formally represented knowledge among AI systems, it is useful to define the common vocabulary in which shared knowledge is represented. A specification of a representational vocabulary for a shared domain of discourse—definitions of classes, relations, functions, and other objects—is called an ontology. This paper describes a mechanism for defining ontologies that are portable over representation systems. Definitions written in a standard format for predicate calculus are translated by a system called Ontolingua into specialized representations, including frame-based systems as well as relational languages. This allows researchers to share and reuse ontologies, while retaining the computational benefits of specialized implementations.

We discuss how the translation approach to portability addresses several technical problems. One problem is how to accommodate the stylistic and organizational differences among representations while preserving declarative content. Another is how to translate from a very expressive language into restricted languages, remaining system-independent while preserving the computational efficiency of implemented systems. We describe how these problems are addressed by basing Ontolingua itself on an ontology of domain-independent, representational idioms.

为了支持在人工智能系统之间共享和重用形式化表示的知识，定义表示共享知识的通用词汇表是有用的。为共享的话语领域——类、关系、函数和其他对象的定义——指定一个代表性词汇表被称为本体论。本文描述了一种用于定义在表示系统上可移植的本体的机制。以谓词演算标准格式编写的定义由一个名为Ontolingua的系统翻译成专门的表示，包括基于框架的系统和关系语言。这允许研究人员共享和重用本体，同时保留专门实现的计算优势。我们讨论了可移植性的翻译方法如何解决几个技术问题。一个问题是如何在保留声明性内容的同时适应表示之间的风格和组织差异。另一个问题是如何从一种表达能力很强的语言翻译成受限制的语言，保持系统独立性，同时保持实现系统的计算效率。我们描述了如何通过将Ontolingua本身建立在与领域无关的、具有代表性的习语的本体之上来解决这些问题。

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引用次数: 13627

Integrating conceptual and operational modeling: a case study 集成概念建模和操作建模：一个案例研究

Knowledge Acquisition

Pub Date : 1993-06-01 DOI: 10.1006/knac.1993.1006

Marc Linster

We argue that it is important for the development of large knowledge models to integrate conceptual and operational modeling. We show that conceptual models can be operationalized by continuous refinement, without the need for a separate manual and structure-transforming implementation phase. Moreover, we show that such a continuity can be the basis for a fruitful integration of both kinds of modeling in a spiral development cycle. This allows us to integrate the best of both worlds: (1) the sloppiness required by conceptual modeling in order to develop structures unhampered by the constraints of an operational language; and (2) the feedback that an operational language provides for the ongoing model development process by allowing for testing, validating, and analysing the formalized structures of the model. To support our claims, we show how a large conceptual model of cancer-chemotherapy administration benefits from this integrating view on modeling.

我们认为，集成概念建模和操作建模对于开发大型知识模型很重要。我们表明，概念模型可以通过不断完善来操作，而不需要单独的手册和结构转换实施阶段。此外，我们还表明，这种连续性可以成为螺旋式开发周期中两种建模的富有成效的集成的基础。这使我们能够两全其美：（1）概念建模所需的草率，以便开发不受操作语言约束的结构；以及（2）操作语言通过允许测试、验证和分析模型的形式化结构，为正在进行的模型开发过程提供的反馈。为了支持我们的说法，我们展示了癌症-化疗管理的大型概念模型如何从这种整合的建模观点中获益。

引用次数: 6

The Active Glossary: taking integration seriously 主动词汇表:认真对待集成

Knowledge Acquisition

Pub Date : 1993-06-01 DOI: 10.1006/KNAC.1993.1007

G. Klinker, David Marques, J. McDermott

Abstract Developing automated support for any workplace involves analysing a workplace, designing a problem-solving approach and knowledge base, populating that knowledge base with information required by the problem-solving approach, and introducing the new support into the workplace. Each of these development phases produces different components of the solution for supporting a workplace. Existing knowledge-acquisition tools support only a subset of the development phases, and the solution components they generate are not integrated: it is left to the developer to create and maintain a mapping between the different solution components resulting from the different development phases. A current trend in knowledge acquisition is to move towards coherent knowledge-engineering environments supporting the entire solution-development cycle. This emphasizes the need for tools that assist developers with integrating the different solution components produced by the knowledge-engineering environment into a coherent system. This paper introduces such an integration tool: the Active Glossary. The Active Glossary is part of the Spark, Burn, FireFighter knowledge-engineering environment. It assists a development team with describing workplaces and programming constructs so that their similarities and differences are made explicit. The result is an explicit mapping between the outcome of a workplace analysis and the design of a problem-solving approach. The Active Glossary further assists the development team with exploiting the similarities for the purpose of reusing previously defined workplace descriptions and programming constructs for new situations.

为任何工作场所开发自动化支持包括分析工作场所，设计解决问题的方法和知识库，用解决问题的方法所需的信息填充知识库，并将新的支持引入工作场所。这些开发阶段中的每一个都会产生用于支持工作场所的解决方案的不同组件。现有的知识获取工具只支持开发阶段的一个子集，并且它们生成的解决方案组件没有集成:开发人员需要创建并维护来自不同开发阶段的不同解决方案组件之间的映射。知识获取的当前趋势是向支持整个解决方案开发周期的连贯知识工程环境移动。这强调了对工具的需求，这些工具可以帮助开发人员将由知识工程环境产生的不同解决方案组件集成到一个连贯的系统中。本文介绍了这样一个集成工具:活动词汇表。活动词汇表是Spark, Burn, FireFighter知识工程环境的一部分。它帮助开发团队描述工作场所和编程结构，使它们的相似点和不同点变得明确。结果是在工作场所分析的结果和解决问题方法的设计之间的显式映射。Active Glossary进一步帮助开发团队利用相似性，以便为新情况重用以前定义的工作场所描述和编程结构。

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引用次数: 20

Formally specifying reusable knowledge model components 正式指定可重用的知识模型组件

Knowledge Acquisition

Pub Date : 1993-06-01 DOI: 10.1006/KNAC.1993.1005

M. Aben

This paper outlines some of the problems with using predefined building blocks to specify knowledge level models of problem solving, in particular in the context of the KADS methodology. The definition of the basic building blocks in KADS, the primitive inferences, or knowledge sources, often seems to be inadequate to aid the knowledge engineer in constructing an abstract model of problem solving. We argue that the informal, verbal way in which the building blocks are defined is the cause of this problem, and propose to formalize them to make their semantics clear and to assess the consequences of various modeling decisions. We discuss choices among different formalizations, and show in detail the formalization of one class of knowledge sources.

本文概述了使用预定义的构建块来指定问题解决的知识级模型的一些问题，特别是在KADS方法的上下文中。KADS中基本构建块、原始推理或知识来源的定义似乎常常不足以帮助知识工程师构建解决问题的抽象模型。我们认为，定义构建块的非正式的、口头的方式是造成这个问题的原因，并建议将它们形式化，以使其语义清晰，并评估各种建模决策的后果。我们讨论了不同形式化的选择，并详细展示了一类知识来源的形式化。

引用次数: 42

Integrating conceptual and operational modeling: a case study 集成概念和操作建模:一个案例研究

Knowledge Acquisition

Pub Date : 1993-06-01 DOI: 10.1006/KNAC.1993.1006

M. Linster

Abstract We argue that it is important for the development of large knowledge models to integrate conceptual and operational modeling. We show that conceptual models can be operationalized by continuous refinement, without the need for a separate manual and structure-transforming implementation phase. Moreover, we show that such a continuity can be the basis for a fruitful integration of both kinds of modeling in a spiral development cycle. This allows us to integrate the best of both worlds: (1) the sloppiness required by conceptual modeling in order to develop structures unhampered by the constraints of an operational language; and (2) the feedback that an operational language provides for the ongoing model development process by allowing for testing, validating, and analysing the formalized structures of the model. To support our claims, we show how a large conceptual model of cancer-chemotherapy administration benefits from this integrating view on modeling.

摘要本文认为，集成概念建模和操作建模对于开发大型知识模型非常重要。我们展示了概念模型可以通过持续的细化来操作，而不需要单独的手工和结构转换实现阶段。此外，我们还表明，这种连续性可以成为在螺旋开发周期中卓有成效地集成两种建模的基础。这使我们能够整合两个世界的优点:(1)为了开发不受操作语言约束的结构，概念建模所需的马虎性;(2)通过允许测试、验证和分析模型的形式化结构，操作语言为正在进行的模型开发过程提供的反馈。为了支持我们的观点，我们展示了一个大型的癌症化疗给药概念模型是如何受益于这种整合模型的观点的。

引用次数: 6

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