面向对象的知识表示方法研究

随着专家系统技术的广泛应用,传统的知识表示方法的缺陷越来越明显。本文在研究典型故障诊断专家系统知识表示基础上,介绍了采用面向对象知识表示的专家系统的设计思路,并对涉及到的关键技术做了阐述。     

基于SharePoint平台的知识协同管理系统的设计

本文从湖北广播电视总台技术管理服务中心知识协同管理系统建设的实践出发,阐述了团队协同的重要性,介绍了基于SharePoint开发的知识协同管理系统,包括系统技术架构和知识存储、知识发现和测量、团队协作、权限管理以及系统管理五大功能模块。     

Dominance-based rough set approach and knowledge reductions in incomplete ordered information system

Many methods based on the rough set to deal with incomplete information systems have been proposed in recent years. However, they are only suitable for the incomplete systems with regular attributes whose domains are not preference-ordered. This paper thus attempts to present research focusing on a complex incomplete information system—the incomplete ordered information system. In such incomplete information systems, all attributes are considered as criterions. A criterion indicates an attribute with preference-ordered domain. To conduct classification analysis in the incomplete ordered information system, the concept of similarity dominance relation is first proposed. Two types of knowledge reductions are then formed for preserving two different notions of similarity dominance relations. With introduction of the approximate distribution reduct into the incomplete ordered decision system, the judgment theorems and discernibility matrixes associated with four novel approximate distribution reducts are obtained. A numerical example is employed to substantiate the conceptual arguments.     

On discovery of soft associations with “most” fuzzy quantifier for item promotion applications

In item promotion applications, there is a strong need for tools that can help to unlock the hidden profit within each individual customer’s transaction history. Discovering association patterns based on the data mining technique is helpful for this purpose. However, the conventional association mining approach, while generating “strong” association rules, cannot detect potential profit-building opportunities that can be exposed by “soft” association rules, which recommend items with looser but significant enough associations. This paper proposes a novel mining method that automatically detects hidden profit-building opportunities through discovering soft associations among items from historical transactions. Specifically, this paper proposes a relaxation method of association mining with a new support measurement, called soft support, that can be used for mining soft association patterns expressed with the “most” fuzzy quantifier. In addition, a novel measure for validating the soft-associated rules is proposed based on the estimated possibility of a conditioned quantified fuzzy event. The new measure is shown to be effective by comparison with several existing measures. A new association mining algorithm based on modification of the FT-Tree algorithm is proposed to accommodate this new support measure. Finally, the mining algorithm is applied to several data sets to investigate its effectiveness in finding soft patterns and content recommendation.     

Document recommendation for knowledge sharing in personal folder environments

Sharing sustainable and valuable knowledge among knowledge workers is a fundamental aspect of knowledge management. In organizations, knowledge workers usually have personal folders in which they organize and store needed codified knowledge (textual documents) in categories. In such personal folder environments, providing knowledge workers with needed knowledge from other workers’ folders is important because it increases the workers’ productivity and the possibility of reusing and sharing knowledge. Conventional recommendation methods can be used to recommend relevant documents to workers; however, those methods recommend knowledge items without considering whether the items are assigned to the appropriate category in the target user’s personal folders. In this paper, we propose novel document recommendation methods, including content-based filtering and categorization, collaborative filtering and categorization, and hybrid methods, which integrate text categorization techniques, to recommend documents to target worker’s personalized categories. Our experiment results show that the hybrid methods outperform the pure content-based and the collaborative filtering and categorization methods. The proposed methods not only proactively notify knowledge workers about relevant documents held by their peers, but also facilitate push-mode knowledge sharing.     

Avoiding semantic and temporal gaps in developing software intensive systems

Development of software intensive systems (systems) in practice involves a series of self-contained phases for the lifecycle of a system. Semantic and temporal gaps, which occur among phases and among developer disciplines within and across phases, hinder the ongoing development of a system because of the interdependencies among phases and among disciplines. Such gaps are magnified among systems that are developed at different times by different development teams, which may limit reuse of artifacts of systems development and interoperability among the systems. This article discusses such gaps and a systems development process for avoiding them.     

On technology for functional requirement-based reference design retrieval in engineering knowledge management

Engineering design is a knowledge-intensive process, and includes conceptual design, detailed design, engineering analysis, assembly design, process design, and performance evaluation. Each of these tasks involves various aspects of technical knowledge and experience. Whether this technical knowledge and experience can be effectively shared is key to increasing product development capability and quality, and also to reducing the duration and cost of the development cycle. Consequently, providing engineering designers various query methods for retrieving engineering knowledge is one of the most important tasks in engineering knowledge management.The study develops a technology for functional requirement-based reference design retrieval as a decision support mechanism, which can assist engineering designers to retrieve relevant design and associated knowledge for reference in conducting functional requirements of a product. This study involves the following tasks: (i) designing a functional requirement-based reference design retrieval process, (ii) developing techniques related to the technology for functional requirement-based reference design retrieval, and (iii) implementing a functional requirement-based reference design retrieval mechanism. The retrieval process includes the steps of functional requirement-based query, case searching and matching, and case ranking. The technology involves (i) a structured query model for functional requirements, (ii) an index structures for historical design cases, (iii) functional requirement-based case searching and matching mechanisms, (iv) a functional requirement-based case ranking mechanism, and (v) a case-based representation of designed entities. Finally, the experimental example with indexing and retrieving similar designed entities is conducted to demonstrate the proposed techniques worked efficiently.     

Conceptual equivalence for contrast mining in classification learning

Learning often occurs through comparing. In classification learning, in order to compare data groups, most existing methods compare either raw instances or learned classification rules against each other. This paper takes a different approach, namely conceptual equivalence, that is, groups are equivalent if their underlying concepts are equivalent while their instance spaces do not necessarily overlap and their rule sets do not necessarily present the same appearance. A new methodology of comparing is proposed that learns a representation of each group’s underlying concept and respectively cross-exams one group’s instances by the other group’s concept representation. The innovation is fivefold. First, it is able to quantify the degree of conceptual equivalence between two groups. Second, it is able to retrace the source of discrepancy at two levels: an abstract level of underlying concepts and a specific level of instances. Third, it applies to numeric data as well as categorical data. Fourth, it circumvents direct comparisons between (possibly a large number of) rules that demand substantial effort. Fifth, it reduces dependency on the accuracy of employed classification algorithms. Empirical evidence suggests that this new methodology is effective and yet simple to use in scenarios such as noise cleansing and concept-change learning.     

A knowledge-based approach to manage information systems interoperability

Interoperability is a key property of enterprise applications, which is hard to achieve due to the large number of interoperating components and semantic heterogeneity. The inherent complexity of interoperability problems implies that there exists no silver bullet to solve them. Rather, the knowledge about how to solve wicked interoperability problems is hidden in the application cases that expose those problems. The paper addresses the question of how to organise and use method knowledge to resolve interoperability problems. We propose the structure of a knowledge-based system that can deliver situation-specific solutions, called method chunks. Situational Method Engineering promotes modularisation and formalisation of method knowledge in the form of reusable method chunks, which can be combined to compose a situation-specific method. The method chunks are stored in a method chunk repository. In order to cater for management and retrieval, we introduce an Interoperability Classification Framework, which is used to classify and tag method chunks and to assess the project situation in which they are to be used. The classification framework incorporates technical as well as business and organisational aspects of interoperability. This is an important feature as interoperability problems typically are multifaceted spanning multiple aspects. We have applied the approach to analyse an industry case from the insurance sector to identify and classify a set of method chunks.     

Mining e-Learning domain concept map from academic articles

Recent researches have demonstrated the importance of concept map and its versatile applications especially in e-Learning. For example, while designing adaptive learning materials, designers need to refer to the concept map of a subject domain. Moreover, concept maps can show the whole picture and core knowledge about a subject domain. Research from literature also suggests that graphical representation of domain knowledge can reduce the problems of information overload and learning disorientation for learners. However, construction of concept maps typically relied upon domain experts in the past; it is a time consuming and high cost task. Concept maps creation for emerging new domains such as e-Learning is even more challenging due to its ongoing development nature. The aim of this paper is to construct e-Learning domain concept maps from academic articles. We adopt some relevant journal articles and conference papers in e-Learning domain as data sources, and apply text-mining techniques to automatically construct concept maps for e-Learning domain. The constructed concept maps can provide a useful reference for researchers, who are new to the e-Leaning field, to study related issues, for teachers to design adaptive learning materials, and for learners to understand the whole picture of e-Learning domain knowledge.