Implementing an open source knowledge base

The concept of knowledge bases originated in artificial intelligence as one side of expert systems-namely, the fundamental body of knowledge available to a domain. KBs are particularly appropriate in knowledge-intensive activities like software development. They offer context-based access to complex information, including informal documents and multimedia, as well as a centralized means of storing and preserving digital assets. KBs can help software engineers with many tasks-from project management and design rationale to version control, defect tracking, code reuse, and staff training and development. We recently implemented an open source KB to support the Consortium for Studying Open Source in Public Administrations. COSPA originated in a EU initiative to study the use of open source software to reduce public administrative software and system support costs. The KB project aimed to build a multilingual knowledge base for comparing and pooling knowledge and experience.     

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.     

Development of two-level decision tree-based real-time scheduling system under product mix variety environment

Most of the research on machine learning-based real-time scheduling (RTS) systems has been aimed toward product constant mix environments. However, in a product mix variety manufacturing environment, the scheduling knowledge base (KB) is dynamic; therefore, it would be interesting to develop a procedure that would automatically modify the scheduling knowledge when important changes occur in the manufacturing system. All of the machine learning-based RTS systems (including a KB refinement mechanism) proposed in earlier studies periodically require the addition of new training samples and regeneration of new KBs. Hence, previous approaches investigating machine learning-based RTS systems have been confronted with the training data overflow problem and an increase in the scheduling KB building time, which are unsuitable for RTS control. The objective of this paper is to develop a KB class selection mechanism that can be supported in various product mix ratio environments. Hence, the RTS KB is developed by a two-level decision tree (DT) learning approach. First, a suitable scheduling KB class is selected. Then, for each KB class, the best (proper) dispatching rule is selected for the next scheduling period. Here, the proposed two-level DT RTS system comprises five key components: (1) training samples generation mechanism, (2) GA/DT-based feature selection mechanism, (3) building a KB class label by a two-level self-organizing map, (4) DT-based KB class selection module, and (5) DT-based dynamic dispatching rule selection module. The proposed two-level DT-based KB RTS system yields better system performance than that by a one-level DT-based RTS system and heuristic individual dispatching rules in a flexible manufacturing system under various performance criteria over a long period.     

ABE: an environment for engineering intelligent systems

The ABE multilevel architecture for developing intelligent systems addresses the key problems of intelligent systems engineering: large-scale applications and the reuse and integration of software components. ABE defines a virtual machine for module-oriented programming and a cooperative operating system that provides access to the capabilities of that virtual machine. On top of the virtual machine, ABE provides a number of system design and development frameworks, which embody such programming metaphors as control flow, blackboards, and dataflow. These frameworks support the construction of capabilities, including knowledge processing tools, which span a range from primitive modules to skeletal systems. Finally, applications can be built on skeletal systems. In addition, ABE supports the importation of existing software, including both conventional and knowledge processing tools     

Ontological versus knowledge engineering

The author first discusses the difference between a knowledge base (KB) and a database (DB), which seems to hinge on the `gray box' verses `black box' nature of the entries. He then discusses the need for a huge KB to break today's bottleneck in intelligent systems, i.e. their brittleness when confronted by unforeseen problems. That same brittleness-the representation trap-is what prevents multiple expert systems from cooperating or even sharing rules. The author then considers the central question of the present work: How is the task of building a huge KB different from that of building n small KBs? It is shown that this leads into the realm of ontological engineering, and it is found that there is no single, elegant `use-neutral' solution to the problem, at least not at present, but that a kind of variegated `tool-box' approach might succeed     

一个面向软件过程的知识挖掘与管理系统

软件过程的知识密集性特征要求组织人员的密切协作、交流，并能够有效地重用组织的实践经验。数据挖掘技术能够从大规模数据中发现有意义的知识，能够为知识管理提供有益的支持，从而得到更好的知识共享和重用效果。在对软件过程进行分析的基础上，提出了协同知识管理模型。介绍了一个集成的面向软件过程的知识挖掘与管理系统iKnow。对iKnow的知识表示、知识挖掘、知识服务和个性化知识检索进行了论述。系统使软件组织的实践经验成为可视、可控和可重用的知识资源。     

一种MAS构件知识复用动态演化模型

为提高企业的知识利用效率,增强企业创新能力,针对企业现有知识和系统,提出将企业知识管理的业务逻辑与知识处理事务分开,建立了基于多智能体和构件知识的知识复用模型,设计了知识管理业务逻辑的规则模型和智能体的活动行为模型,讨论了基于多智能体的规则协调模式,有效地支持知识的动态复用和知识使用过程的动态重组,增强知识管理系统的分布式处理能力和规模可扩展能力.在分布式构件库系统中,智能体通过协作联合完成任务要求.每个智能体拥有自己的知识库,并且具备学习能力,能够更新其知识库以保持执行结果的有效性.     

基于概念层次网络的知识表示与本体建模

知识表示是自然语言理解的重要基础。知识表示不统一、语义信息无法系统化利用是目前存在的亟待解决的问题。要解决这个问题,就要解决语义知识表示的问题。该文基于概念层次网络,描述了词语、句子和篇章层面的语义知识表示方法。基于文中描述的词汇层面的表示方法,构建了一个多语言本体知识库。该知识库的知识表示方法不仅可以为知识表示理论研究提供基础,还可以为自然语言处理相关领域的应用提供资源支持。     

基于本体的构件描述与检索

软件复用技术是近年来国内外软件界研究的热点之一.以网络教育应用为背景,提出了基于OWL的构件服务描述方法,对构件的静态特性和动态行为作出了较为准确、全面的描述,构建了一个基于本体的构件知识库,提供了良好的检索方法支持,达到了教学资源的重用和共享,具备了以语义为目标的智能化查找,显示出基于本体技术的构件描述与检索的优越性与可行性.     

Building Linear Programming Models from Structural Model Components

Model management research investigates the formulation, analysis and interpretation of models. This paper focuses on the formulation aspects of modeling so that the task can be supported by decision support systems (DSS) environments. Given the knowledge intensive nature of the formulation process, the development of a modeling tool requires explicating the knowledge pertaining to modeling. This involves comprehending not only the static knowledge about model components (e.g. decision variables, coefficients, associated indices and constants), but also the process knowledge required to construct models from model pieces. The proposed top-down approach configures equations by exploiting the structural modeling knowledge inherent in equation components. The possible representation of equations at various abstraction levels is introduced, the aim being to uncover the structural model components together with the process knowledge required for their appropriate configuration. As part of developing this conceptual model, the role of semantic and syntactic information in model building is investigated. The paper proposes an approach where the formulation semantics are captured as a simple 'action-resource' view which composes models by identifying and piecing together the equation components. The process of equation construction is illustrated with examples from the linear programming (LP) modeling domain. The proposed top-down approach is contrasted with a bottom-up method.     

Causal design knowledge: Alternative representation method for product development knowledge management

This paper presents a mathematical comparison of procedural knowledge and causal knowledge, and discusses the potential roles and feasibility of causal knowledge across product development knowledge management. Since reuse of knowledge is so important in product development, various knowledge management approaches have been introduced. Most of the product design knowledge is represented by procedural knowledge, which unfortunately requires cumbersome processes to define, and is typically inadequate for representing the kind of knowledge generated during the product development process. A causal knowledge representation, however, can help us to overcome this limitation and is an alternative formalism for representing product design knowledge. In this paper we compare the procedural and causal knowledge representations. We present the mathematical definitions of two knowledge paradigms, then mathematically describe the relationship between the two. Both knowledge paradigms are then compared based on the perspective of knowledge expression, decision alternative representation, reasoning capability, and knowledge cultivation. This paper concludes that causal knowledge representation is superior to procedural knowledge representation based on the four perspectives. Finally, the knowledge systems are modeled using Systems Modeling Language (SysML), and we present a case study that demonstrates the causal knowledge features using a realistic example from industry.     

A design methodology for form-based knowledge reuse and representation

Paper forms are regularly used for collecting and disseminating knowledge in offices; they are a natural way of eliciting requirements of knowledge workers. Many organizations have implemented a groupware system to integrate the organizational knowledge and support knowledge creation. However, design methods for flexible form-based knowledge reuse and representation are limited. We developed a methodology based on the enhanced cognitive fit theory; it utilizes factoring and synthesis principles to manipulate form-based knowledge. The methodology was articulated using the design science research methodology. A prototype embedded methodology was built to support a knowledge worker in knowledge creation and reuse in a high tech firm. The resulting system allowed flexible form-based knowledge creation that was useful for problem solving and exploiting opportunities. Implications and conclusions are discussed.     

Core techniques of question answering systems over knowledge bases: a survey

The Semantic Web contains an enormous amount of information in the form of knowledge bases (KB). To make this information available, many question answering (QA) systems over KBs were created in the last years. Building a QA system over KBs is difficult because there are many different challenges to be solved. In order to address these challenges, QA systems generally combine techniques from natural language processing, information retrieval, machine learning and Semantic Web. The aim of this survey is to give an overview of the techniques used in current QA systems over KBs. We present the techniques used by the QA systems which were evaluated on a popular series of benchmarks: Question Answering over Linked Data. Techniques that solve the same task are first grouped together and then described. The advantages and disadvantages are discussed for each technique. This allows a direct comparison of similar techniques. Additionally, we point to techniques that are used over WebQuestions and SimpleQuestions, which are two other popular benchmarks for QA systems.     

Knowledge base grid: A generic grid architecture for semantic web

The emergence of semantic web will result in an enormous amount of knowledge base resources on the web. In this paper, a generic Knowledge Base Grid Architecture (KB-Grid) for building large-scale knowledge systems on the semantic web is presented. KB-Grid suggests a paradigm that emphasizes how to organize, discover, utilize, and manage web knowledge base resources. Four principal components are under development: a semantic browser for retrieving and browsing semantically enriched information, a knowledge server acting as the web container for knowledge, an ontology server for managing web ontologies, and a knowledge base directory server acting as the registry and catalog of KBs. Also a referential model of knowledge service and the mechanisms required for semantic communication within KB-Grid are defined. To verify the design rationale underlying the KB-Grid, an implementation of Traditional Chinese Medicine (TCM) is described.     

Enhancing students’ knowledge building through utilising social interactions in an online learning environment

ABSTRACT

In the new “open world” of information, educational systems should involve students in constructing new knowledge of value to a community out of fragmentary information. The already proposed Knowledge Building (KB) approaches typically support only a few general-purpose activities due to the constraints of the utilised web-based environments. To organise and facilitate students’ KB during course activities, this study incorporated services provided by DoosMooc social learning environment into a knowledge transformation model. This approach is completely adapted to an educational context and allows time for iterations, helping students to both contribute to social KB processes and take collective responsibility for improving their understanding of authentic problems. The features provided by the introduced environment support and assess students’ KB activities and facilitate processes of creating, representing, organising, and reviewing different types of knowledge artefacts. The results of a semester-long experiment indicate that the approach and the corresponding instructional design thereof could successfully organise students’ KB activities and facilitate the required interactions. This study reports the impacts of parameters such as learner expertise and quality of shared knowledge on the planned KB processes, and investigates the relationships between students' KB activities and learning achievements.     

大规模定制环境下的决策案例库系统

针对大规模定制决策的特点,为实现决策案例的重用,提出了一种面向大规模定制决策问题的案例库系统,设计了基于框架结构的案例知识表示模型,介绍了基于模糊加权的案例相似度计算和匹配算法,该算法解决了大规模定制决策问题的结构化表达、检索匹配和重用问题,开发了案例库的原型系统以支持案例管理、推理和基于案例的规则发现。