一种近似概念格及其在知识发现中的应用研究*

针对经典概念格基于完备形式背景,在缺值形式背景下不能很好的获取关联规则知识的问题,在偏小近似形式背景的基础上,提出了一种τ-近似概念格来解决信息不完备背景下的知识发现问题,利用它能够获取精度和可信度较高的关联规则知识。文章对τ-近似概念格的构建及其关联规则提取过程进行了详细描述,并将其与偏小近似概念格和偏大近似概念格这两种解决策略进行了案例分析对比,验证了它的可行性和有效性。     

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.     

THE USEFULNESS OF A MACHINE LEARNING APPROACH TO KNOWLEDGE ACQUISITION

This paper presents results of experiments showing how machine learning methods arc useful for rule induction in the process of knowledge acquisition for expert systems. Four machine learning methods were used: ID3, ID3 with dropping conditions, and two options of the system LERS (Learning from Examples based on Rough Sets): LEM1 and LEM2. Two knowledge acquisition options of LERS were used as well. All six methods were used for rule induction from six real-life data sets. The main objective was to lest how an expert system, supplied with these rule sets, performs without information on a few attributes. Thus an expert system attempts to classify examples with all missing values of some attributes. As a result of experiments, it is clear that all machine learning methods performed much worse than knowledge acquisition options of LERS. Thus, machine learning methods used for knowledge acquisition should be replaced by other methods of rule induction that will generate complete sets of rules. Knowledge acquisition options of LERS are examples of such appropriate ways of inducing rules for building knowledge bases.     

Representing Probabilistic Rules with Networks of Gaussian Basis Functions

There is great interest in understanding the intrinsic knowledge neural networks have acquired during training. Most work in this direction is focussed on the multi-layer perceptron architecture. The topic of this paper is networks of Gaussian basis functions which are used extensively as learning systems in neural computation. We show that networks of Gaussian basis functions can be generated from simple probabilistic rules. Also, if appropriate learning rules are used, probabilistic rules can be extracted from trained networks. We present methods for the reduction of network complexity with the goal of obtaining concise and meaningful rules. We show how prior knowledge can be refined or supplemented using data by employing either a Bayesian approach, by a weighted combination of knowledge bases, or by generating artificial training data representing the prior knowledge. We validate our approach using a standard statistical data set.     

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.     

McClellan transformation and the construction of biorthogonal wavelet bases of LR

Bidimensional wavelet bases are constructed by means of McClellan's transformation applied to a pair of one-dimensional biorthogonal wavelet filters. It is shown that under some conditions on the transfer function F(ω₁,ω₂) associated to the McClellan transformation and on the dilation matrix D, it is possible to construct symmetric compactly supported biorthogonal wavelet bases of L²(R²). Finally, the construction method is illustrated by means of numerical examples.     

Modeling tasks with mechanisms

Building a problem solver and acquiring the knowledge needed to operate it are the two central goals of knowledge engineering. to achieve these goals, knowledge engineers construct models of the domain and of the task of interest. the various approaches used for modeling, however, have so far failed to define methods and techniques that can be applied across domains and tasks, and to produce models that can be reused in future applications. In this article, we propose that both of these objectives can be achieved by the use of building blocks called mechanisms. We examine the composition of mechanisms and also show how these mechanisms can be manipulated to construct problemsolving methods. We present PROTÉGÉ-II, a knowledge-acquisition shell that uses problem-solving methods to drive the modeling of tasks, the automatic generation of knowledge-acquisition tools, and the control flow of the problem solver. the modeling of tasks, within the context of PROTÉGÉ-II, is illustrated with two examples: one from the game domain and another from the medical-therapy domain. In addition, we introduce the conceptual basis for a library of mechanisms that serves as a repository of reusable knowledge components. © 1993 John Wiley & Sons, Inc.     

Some knowledge-acquisition methods for prospector-like systems

The paper deals with the knowledge-acquisition methods designed and tested under the FEL-EXPERT Project, which is aimed at the development of rule-based diagnostic shells. Three different approaches have been used for knowledge acquisition: pattern-recognition, decision-tree, and intensional, pure probabilistic approaches. The designed methods may be applied to a wide subclass of rule-based diagnostic systems that exploit the pseudo-Bayesian model for uncertainty handling. The experimental results are discussed.     

基于对象模型的自组织专家系统

在研制的一个基于对象模型的自组织专家系统中 ,通过对机器人的行走装置进行模型化 ,建立了对象的模糊知识库 ,并根据控制的目标 ,设计了推理机。系统无需精确的数学模型 ,能根据输入、输出变量 ,自动修改控制规则 ,达到优化控制的目的。     

新闻知识图谱中知识融合量化评估研究

现阶段检验知识图谱融合程度的方法大多是基于句法检查和实例检验的人工方法,或对知识库中的逻辑规则进行推理,以此判断图谱是否具有完备性和一致性,但缺少对知识融合量化评估的具体方法。提出了一种基于幂律定律的量化评估方法,并用融合后的长江大保护新闻知识图谱节点数进行验证,发现随着节点相似度的升高,当前图谱节点的融合度增加,融合后节点数急剧下降,幂律模型拟合优度良好,调整决定系数均大于0.98。这一检验方法可为新闻知识图谱的融合度检验提供新的标准。     

Integrating forward-chaining rules with operations and permanent knowledge bases

Traditionally, rule-based forward-chaining systems are considered to be standalone, working on a volatile memory. This paper focuses on the integration of forward-chaining rules with command-driven programming paradigms in the context of permanent, integrated knowledge bases. A system architecture is proposed that integrates the data management functions of large computerized knowledge bases into a module called a knowledge base management system (KBMS). Experiences we had in integrating rules with operations into a prototype KBMS called DALI are surveyed. For this integration, a new form of production rule, called the activation pattern controlled rule, is introduced, which augments traditional forward-chaining rules by a second, additional left-hand side, which allows making rules sensitive to calls of particular operations. Activation pattern controlled rules play an important role in DALI's system architecture, because they facilitate the storage of knowledge that has been specified relying on mixed programming, a combination of data-driven, command-driven, and preventive programming. The general problems of implementing permanent knowledge bases that contain rules and operations are discussed, and an algorithm for implementating activation pattern controlled rules, called IPTREAT, a generalization of the TREAT algorithm, is provided. Furthermore, the paper intends to clarify the differences between traditional, volatile rule-based systems and rule-based systems that are geared toward knowledge integration by supporting a permanent knowledge base.This paper is an extended and significantly revised version of a paper entitled Integrating Rules into a Knowledge Base Management System, which was presented at the First International Conference on Systems Integration, April 1990 [1].     

容差近似空间的广义概念格模型研究

在粗糙集合理论中,近似空间概念格之间存在着有趣的对应关系,利用概念格研究知识的约简和发现。更直观和更有效,但已有的概念格模型是基于近似空间的等价类划分的。等价类划分过于苛刻,扩展的基于容差关系的近似空间具有更广泛的意义,但目前未见有相应的格模型被提出。该文提出了容差近似空间的一种格模型,称为广义概念格,给出了定义,描述了建立方法和由它产生规则的原则,讨论了空间复杂性问题,并且与其它相近方法做了比较     

GSIES-TOOL中的知识表示及知识库的构造

在开发岩土工程安全监测专家系统开发工具(GSIES-TOOL)的过程中,针对岩土工程安全监测领域的具体特点,将传统的产生式规则和推理树、方法、实例、解释机制封装在一起,设计了一种新的知识表示方法OORL。在用OORL来描述该领域的知识后,根据对该领域知识的分类,进一步采用树形结构对该系统的知识库进行了分级构造,并通过双向链条机制对知识库中的相邻层次进行约束,以确保对知识库维护的安全性。由此而建立起来的知识库层次性好、推理效率高、易于管理。     

基于本体论的数据挖掘方法

数据挖掘是一个人机交互的过程,领域知识对数据挖掘起着重要作用,提出一种基于本体的数据挖掘算法,使领域知识和数据库无缝连接,该算法能更有效地发现有意义的多层次规则。     

SEMANTICAL AND ONTOLOGICAL CONSIDERATIONS IN TELOS: A LANGUAGE FOR KNOWLEDGE REPRESENTATION DIMITRIS PLEXOUSAKIS

This paper focuses on the semantics of Telos, a language for representing knowledge about information systems. Telos is intended to support the development of information systems, especially in the requirements modeling phase. An object-oriented representational framework is supported by Telos. Its features include aggregation, generalization, and classification, the treatment of attributes as first-class objects and the explicit representation of time. Telos also provides an assertion sublanguage for expressing deductive rules and integrity constraints. A possible-worlds semantics is defined for Telos knowledge bases. This semantics is intended to capture the peculiarities involved in the interpretation of temporal expressions. The integration of time has also inspired the treatment of existence in Telos. An ontology of objects based on the property of existence is proposed. In the spirit of KRYPTON, Telos knowledge bases are specified functionally, in terms of the operations provided for querying and updating them. This knowledge-level analysis will allow us to specify exactly what a knowledge base can be ASK-ed or TELL-ed about the domain of discourse. Soundness, consistency, and completeness results have also been proven to complete the specification of Telos knowledge bases. This formal account of the language provides a logical framework that can be used to verify the correctness of any proposed implementation of the system.     

From decision tables to expert system shells

Building and maintaining high quality knowledge based systems is not a trivial task. Decision tables have sometimes been recommended in this process, mainly in verification and validation. In this paper, however, it is shown how decision tables can also be used to generate, and not just to validate, knowledge bases and how the transformation process from decision tables to knowledge bases can be organized. Several options to generate rules or other knowledge representation from decision tables are described and evauluated.

The proposed generation strategy enables the knowledge engineer to concentrate on the acquisition and modelling issues and allows him to isolate the knowledge body from its implementation. The generation process has been implemented for two commercial tools, AionDS and KBMS and has been applied to real world applications.     

一种知识库校验工具PKBV的设计与实现

以Petri网建模基于规则的知识库,并据此开发出知识库校验工具PKBV,该工具通过对Petri网可达性及不变量的分析计算,来检查知识库中常见的完整性与一致性错误,针对具有多领域知识库的复杂系统,PKBV具有抽取多领域知识库之间的关联规则并进行校验的功能,满足了复杂知识系统的校验需求。     

基于知识Petri网的确定性和不确定性联合推理

知识推理是人工智能的核心领域,旨在研究如何从已知(知识库和推理规则)推理出未知,以帮助智能体做出科学决策.而智能体所处的环境存在不可观性和不确定性,因此知识库通常不仅包含确定性知识,还包含不确定性知识,而且推理过程需要两类知识紧密协作.然而,目前的推理方法无法将两类知识统一表示,常常将两者对应的推理过程割裂进行.基于此,为了实现在统一的模型架构下完成确定性和不确定性联合推理,给出了一种知识Petri网推理方法.首先,定义了一种新的知识Petri网,使其不仅能够描述确定性的知识规范,也可以描述先验概率知识;其次,根据知识Petri网的网结构,给出了一种知识Petri网概率独立剪枝算法,能够指数级地降低不确定性推理的计算复杂性;最后,利用知识Petri网及其概率独立剪枝算法,给出了一种新型推理算法,实现了确定性和不确定性的联合推理,并利用Wumpus世界进行了演示和验证.