基于知识的信息系统开发环境

本文按时序演进方式阐述两个生成集成化的信息系统开发环境的计划Ｔａｘｉｓ和ＤＡＩＤＡ。读者可从中发现一些研制信息系统开发环境的有益的概念，方法，技术和工具。     

A Knowledge-Based Software Engineering Environment for reusable software requirements and architectures

This paper describes a prototype Knowledge-Based Software Engineering Environment used to demonstrate the concepts of reuse of software requirements and software architectures. The prototype environment, which is application-domain independent, is used to support the development of domain models and to generate target system specifications from them. The prototype environment consists of an integrated set of commercial-off-the-shelf software tools and custom developed software tools.The concept of reuse is prevalent at several levels of the domain modeling method and prototype environment. The environment itself is domain-independent thereby supporting the specification of diverse application domain models. The domain modeling method specifies a family of systems rather than a single system; features characterize the variations in functional requirements supported by the family and individual family members are specified by the features they are to support. The knowledge-based approach to target system generation provides the rules for generating target system specifications from the domain model; target system specifications, themselves, may be stored in an object repository for subsequent retrieval and reuse.     

Personal Document Management and Retrieval: A Knowledge-Based Approach

This paper presents a knowledge-based approach to managing and retrieving personal documents. The dual document models consist of a document type hierarchy and a folder organization. The document type hierarchy is used to capture the layout, logical and conceptual structures of documents. The folder organization mimics the user's real-world document filing system for organizing and storing documents in an office environment. Predicate-based representation of documents is formalized for specifying knowledge about documents. Document filing and retrieval are predicate-driven. The filing criteria for the folders, which are specified in terms of predicates, govern the grouping of frame instances, regardless of their document types. We incorporated the notions of document type hierarchy and folder organization into the multilevel architecture of document storage. This architecture supports various text-based information retrieval techniques and content-based multimedia information retrieval techniques. The paper also proposes a knowledge-based query-preprocessing algorithm, which reduces the search space. For automating the document filing and retrieval, a predicate evaluation engine with a knowledge base is proposed. The learning agent is responsible for acquiring the knowledge needed by the evaluation engine.     

mwKAT: A meta-tool-generated knowledge acquisition tool for multimedia workstations development

mwKAT is an interactive knowledge acquisition tool for acquiring domain knowledge about multimedia components. It constructs knowledge bases for a consulting system that produces the design specification for a multimedia workstation according to the user requirements.mwKAT is generated from and executed inGAS, a primitives-based generic knowledge acquisition meta-tool. It contains three acquisition primitives, namely, parameter proposing, constraint proposing, and fix proposing to construct an intermediate knowledge base represented by a dependency model. These primitives identify necessary domain knowledge and guide users to propose significant components, constraints, and fix methods into the dependency model.mwKAT also invokes knowledge verification and validation primitives to verify the completeness, consistency, compilability, and correctness of the intermediate knowledge base.     

Feature Selection for Reduction of Tabular Knowledge-Based Systems

Tabular knowledge-based systems are known to be extremely versatile for verification and validation of knowledge bases. However, a major disadvantage of these systems is the combinatorial explosion that accompanies addition of new attributes or condition entries in the table. One of the means of alleviating this problem in tabular knowledge-based systems is through modularization, which is the process of breaking a big comprehensive table into smaller tables that are easy to deal with. In this study, we propose and illustrate another means to deal with this problem through use of feature selection methodology. The proposed method can be used synergistically with modularization to alleviate problems associated with combinatorial explosion in tabular knowledge bases.     

Principled Modeling and Automatic Classification for Enhancing the Reusability of Problem Solving Methods of Expert Systems

Software reuse is widely believed to be a key to improving software productivity and quality in conventional software. In expert systems, much of the knowledge has been compiled (i.e., compressed and restricted into effective procedures) and this makes reusability difficult. One of the issues in modeling expert systems for enhanced reusability is capturing explicity the underlying problem solving designs. Principled knowledge representation schemes have been used to model components of complex software systems. However, the potential for applying these principled modeling techniques for explicitly capturing the problem solving designs of expert systems has not been fully explored. To overcome this omission, we use an Artificial Intelligence knowledge representation scheme for developing an ontology of the software components to facilitate their classification and retrieval. The application of our ontological approach is of both theoretical and practical significance. This method facilitates the reuse of high-level design. We illustrate the application of principled domain modeling using two real world applications of knowledge-based systems.     

Developing Knowledge-Based Intelligent Multimedia Tutoring Systems Using Semantic Content-Based Modelling

The complex nature of semantic video and audio content has made multimedia difficult to use within the traditional architecture of a knowledge-based intelligent tutoring system (ITS). This paper investigates how multimedia syntax and semantics may be integrated into the confines of the knowledge-based ITS architecture so as to develop a knowledge-based intelligent multimedia tutoring system (IMTS). The paper contributes a semantic content-based model that can be used for this purpose. Multimedia frames (m-frames) are used as the framework by which the syntactic and semantic information is represented within the model. The paper discusses how IMTSs may be developed using the model, and then demonstrates the use of our approach in the development of ARISTOTLE, an IMTS for teaching young children basic zoology.     

GDKAT: A goal-driven knowledge acquisition tool for knowledge base development

While knowledge-based systems are being used extensively to assist in making decisions, a critical factor that affects their performance and reliability is the quantity and quality of the knowledge bases. Knowledge acquisition requires the design and development of an in-depth comprehension of knowledge modeling and of applicable domain. Many knowledge acquisition tools have been developed to support knowledge base development. However, a weakness that is revealed in these tools is the domain-dependent and complex acquisition process. Domain dependence limits the applicable areas and the complex acquisition process makes the tool difficult to use. In this paper, we present a goal-driven knowledge acquisition tool (GDKAT) that helps elicit and store experts' declarative and procedural knowledge in knowledge bases for a user-defined domain. The designed tool is implemented using the object-oriented design methodology under C++ Windows environment. An example that is used to demonstrate the GDKAT is also delineated. While the application domain for the example presented is reflow soldering in surface mount printed circuit board assembly, the GDKAT can be used to develop knowledge bases for other domains also.     

Interactive Management of Human Factors Knowledge for Human Systems Integration Using Systems Modeling Language

Abstract

The field of Human Factors in Knowledge Management is often seen as a problem of capturing, organizing, and retrieving information to build knowledge. This process is inextricably bound up with human cognition and, as such, the management of knowledge occurs within an intricately structured behavioral, cultural, and social context. This paper emphasizes the importance of interactive human factors in knowledge management and introduces a model-based human systems integration framework based on systems modeling language (SysML).     

知识系统的知识重用方法

KADS方法的主要贡献是提出了层次化的知识模型,这有助于领域知识和PSM的重用。但是,要真正实现领域知识和PSM的重用,还需要引入本体来实现各层次间的灵活配置,将相互独立的层次紧密地联系在一起,共同组成一个完整的系统。文中介绍了知识系统的本体的种类及其可重用性,讨论了在知识系统中引入本体以实现系统知识重用的方法。     

基于知识的参数化设计层次模型

提出一种融合知识的参数化设计层次模型,以解决系列化产品大量重复设计问题．首先,在分析计算机辅助产品设计现状的基础上,提出以行业知识表示为核心的模型框架;然后,对构成模型的各个层次模型及层次间的结合等问题进行详细讨论,提出融合行业知识的设计特征、行业知识的三元组统一描述方法以及基于知识的装配等概念;最后,结合压力机产品主传动系统的设计,给出了模型实现的实例．     

Average-case improvements when integrating ML and KA

In this paper we develop a mathematical analysis, based on empirical measurements, of expected average improvements when integrating Machine Learning and Knowledge Acquisition systems in real-life domains. The analysis is based on the characteristics of component systems and combining techniques. Important characteristics include the accuracy of component systems, the degree to which component systems complement each other's weaknesses, and the ability of the combining mechanism to make good choices among competing component systems. Empirical measurements in a real-life application, in the Sendzimir rolling mill, have shown that integrating both approaches enables significant improvements. Improvements when combining systems in two oncological domains were smaller, yet positive again. Analytical average-case integrated models consisting of two systems are introduced. Conditions for improvements over the best, average and the worst system are established and the expected gains are analytically computed based on expected performances. Models strongly suggest that a reasonable integration of two systems offers significant improvements over the best single system in many or even most real-life domains.     

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.     

DIDS: rapidly prototyping configuration design systems

The domain independent design system (DIDS) provides a set of tools for rapidly constructing new configuration design systems from a library of reusable software elements called mechanisms. A DIDS user begins by creating a model of the problem domain and the task to be automated. This includes describing a library of parts from which new artifacts could be configured, optimization and preference criteria, and functionality constraints. DIDS analyzes this input and automatically builds an operational prototype system by selecting and combining mechanisms. DIDS' ability to automate this process is derived from its model of configuration design, which enables reusable mechanisms to be identified and automatically selected based on a problem's characteristics. The use of DIDS is illustrated by showing how DIDS solved an elevator-configuration problem.     

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.     

A Knowledge-Based Mesh Generation System for Forging Simulation

We developed a knowledge-based system GENMAI (Artificial Intelligence Mesh GENerator) to auto-generate two-dimensional structured meshes. GENMAI is easily applicable to various kinds of application domains. Mesh generation is one of the major tasks confronted in computational simulation. The quality of generated meshes affects computational accuracy and computing time. Since various kinds of domain knowledge are needed to generate high quality structured meshes, the knowledge-based approach has been found effective and successful. Before designing GENMAI, we analyzed mesh generation jobs in plastic deformation analysis and computational fluid dynamics. Then, we formulate GENMAI so that it searches feasible plural divided patterns combinatorially and selects the best pattern. The characteristics of GENMAI are as follows: the meta-inference mechanism and its knowledge representation are widely applicable to various kinds of application domains; and plural patterns can be efficiently obtained at the same time by a search technique based on global dependency and local dependency. We applied GENMAI to forging simulation and developed AI-FESTE, which integrated a rigid-plastic deformation analysis program and GENMAI. Forging designers can easily decide shapes of a forging product and dies and also plan the forming sequence using AI-FESTE. AI-FESTE automates a series of forging analysis operations and shortens the execution time from 1 or 2 day(s) to a few hours. As a result, not only can AI-FESTE shorten the turn-around time, but it can improve the quality of product and die design.     

Using Rule Quality Measures for Rule Base Refinement in Knowledge-Based Predictive Maintenance Systems

Abstract

As today’s manufacturing domain is becoming more and more knowledge-intensive, knowledge-based systems (KBS) are widely applied in the predictive maintenance domain to detect and predict anomalies in machines and machine components. Within a KBS, decision rules are a comprehensive and interpretable tool for classification and knowledge discovery from data. However, when the decision rules incorporated in a KBS are extracted from heterogeneous sources, they may suffer from several rule quality issues, which weakens the performance of a KBS. To address this issue, in this paper, we propose a rule base refinement approach with considering rule quality measures. The proposed approach is based on a rule integration method for integrating the expert rules and the rules obtained from data mining. Within the integration process, rule accuracy, coverage, redundancy, conflict, and subsumption are the quality measures that we use to refine the rule base. A case study on a real-world data set shows the approach in detail.     

Knowledge Discovery in Texts for Constructing Decision Support Systems

This paper presents a Text Mining approach for discovering knowledge in texts to later construct decision support systems. Text mining can take advantage of knowledge stored in textual documents, reducing the effort for knowledge acquisition. The approach consists in performing a mining process on concepts present in texts instead of working with words. The assumption is that concepts represent real world events and characteristics better than words, allowing the understanding and the explanation of the reasoning used in decision processes. The proposed approach extracts concepts expressed in natural phrases, and then analyzes their distributions and associations. Concepts distributions and associations are used to characterize classes or situations. After the discovery process, the obtained knowledge can be embedded in automated systems to classify elements or to suggest actions or solutions to problems. In this paper, experiments using the approach in a psychiatric domain are discussed. Concepts extracted from textual medical records represent patients' symptoms, signals and social/behavior characteristics. An automatic system was constructed with the approach: a classifier whose goal is to help physicians in disease diagnoses. Results from this system show that the approach is feasible for constructing decision support systems with satisfactory performance.