DCVE中基于本体的数据管理框架模型

研究互联网信息管理问题,在分布式协同虚拟环境,为有效地管理海量数据,使数据能快速地被使用,提高数据的利用率是一个重要问题。本体最初是一个哲学概念,可以实现知识共享和复用。根据本体的特点,构建相应的数据本体,把数据信息标准化,提出一个基于本体的数据管理框架模型(ODMF)。数据模型有不同的结构,在ODMF中所构建的本体数目也不相同,对ODMF共有三种不同的结构。在实验中,以口腔疾病的相关信息为依据,构建一个ODMF的实例。实验结果表明ODMF能解决数据共享问题,并且能提高数据的利用效率。     

SEDAF: AN INTELLIGENT EDUCATIONAL SYSTEM FOR MATHEMATICS

The field of artificial intelligence and education, in which AI techniques and methodologies are used to build sophisticated intelligent educational systems, is developing rapidly. In this paper we present an intelligent educational system for teaching high school and college students how to analyze and draw graphs of mathematical functions. The system, named SEDAF, has been developed in a knowledge engineering environment and runs on a Lisp-machine workstation. We illustrate the various modules constituting SEDAF: the user interface; an expert module, capable of solving problems in the subject domain; a diagnosis module, which points out possible reasons for students' errors; a student modeling module, capable of building an explicit representation of the learning status of the student; and a remedial subsystem, called a therapy module, constituted by means-ends tutorial rules that execute teaching actions on the base of the status of the student model. The goal of the presentation is to stress the innovative aspects of the architecture of SEDAF, in particular the use of metalevel knowledge to embed in the system the teaching expertise that allows the system to personalize its behavior to the specific student and to pursue a didactic plan.     

建筑供应链信息共享平台设计与应用

针对建筑供应链异构信息无法充分共享的问题,分析异构信息对信息共享平台的需求,设计基于本体集成的建筑供应链信息共享模型和共享过程模型,提出基于本体集成的建筑供应链信息共享平台,并且描述其中的关键功能模块。论述基于本体集成的信息共享平台如何支持建筑供应链的信息共享。结果证明,基于本体集成的建筑供应链信息共享平台能够更有效地检索信息、学习领域知识及支持决策。     

Effects of an ontology display with history representation on organizational memory information systems

Ontologies, as a possible element of organizational memory information systems, appear to support organizational learning. Ontology tools can be used to share knowledge among the members of an organization. However, current ontology-viewing user interfaces of ontology tools do not fully support organizational learning, because most of them lack proper history representation in their display. In this study, a conceptual model was developed that emphasized the role of ontology in the organizational learning cycle and explored the integration of history representation in the ontology display. Based on the experimental results from a split-plot design with 30 participants, two conclusions were derived: first, appropriately selected history representations in the ontology display help users to identify changes in the ontologies; and second, compatibility between types of ontology display and history representation is more important than ontology display and history representation in themselves.     

“虚拟导师环境”下的计算机实践教学体系研究

本文研究了"虚拟导师环境"下的计算机实践教学体系结构,把计算机实践教学体系分成三个模块来研究:课程实验、课程实训和创新实验。在每一个模块中结合高校在实践教学中的探索,构建了"虚拟导师环境"下的实践教学体系。最后,结合实例,讲述了在"虚拟导师环境"下,计算机实践课程的教学过程。     

Learning adaptivity in support of flipped learning: An ontological problem‐solving approach

In recent years, flipped learning has received tremendous attention from educational practitioners and researchers. However, this study argues that existing e‐learning systems mainly serve for learning management and content delivery purposes, although they lack support for flipped learning. As an innovative educational approach, flipped learning requires more pedagogical elements, such as integrated instructional design and adaptive content delivery, to achieve effective direct instruction. This study aims to create a learning adaptivity design to support effective learning in the flipped individual learning space in which the teacher is absent. Because teaching involves various pedagogical and content knowledge sources, we propose a conceptual model of teaching as a function of the knowledge triad of Guideline (G), Teaching Activity (T), and Material (M). To realize this conceptualization, an ontological problem‐solving approach is used for knowledge‐based systems development to integrate the relevant knowledge sources. The knowledge model is created using the Protégé platform to develop OWL‐based domain ontology, task ontology, and SWRL‐based semantic rules to enable inference in the GTM triad for learning adaptivity. The case illustration shows that the knowledge‐based system prototype can adaptively guide student learning in the flipped individual learning space with the knowledge sources integrated.     

Effects of an ontology display with history representation on organizational memory information systems

Ontologies, as a possible element of organizational memory information systems, appear to support organizational learning. Ontology tools can be used to share knowledge among the members of an organization. However, current ontology-viewing user interfaces of ontology tools do not fully support organizational learning, because most of them lack proper history representation in their display. In this study, a conceptual model was developed that emphasized the role of ontology in the organizational learning cycle and explored the integration of history representation in the ontology display. Based on the experimental results from a split-plot design with 30 participants, two conclusions were derived: first, appropriately selected history representations in the ontology display help users to identify changes in the ontologies; and second, compatibility between types of ontology display and history representation is more important than ontology display and history representation in themselves.     

Modelling the student in Pitagora 2.0

With the aim to individualise human-computer interaction, an Intelligent Tutoring System (ITS) has to keep track of what and how the student has learned. Hence, it is necessary to maintain a Student Model (SM) dealing with complex knowledge representation, such as incomplete and inconsistent knowledge and belief revision. With this in view, the main objective of this paper is to present and discuss the student modelling approach we have adopted to implement Pitagora 2.0, an ITS based on a co-operative learning model, and designed to support teaching-learning activities in a Euclidean Geometry context. In particular, this approach has led us to develop two distinct modules that cooperate to implement the SM of Pitagora 2.0. The first module resembles a classical student model, in the sense that it maintains a representation of the current student knowledge level, which can be used by the teacher in order to tune its teaching strategies to the specific student needs. In addition, our system contains a second module that implements a virtual partner, called companion. This module consists of a computational model of an average student which cooperates with the student during the learning process. The above mentioned module calls for the use of machine learning algorithms that allow the companion to improve in parallel with the real student. Computational results obtained when testing this module in simulation experiments are also presented.     

Ontology based approach to Bayesian student model design

Probabilistic student model based on Bayesian network enables making conclusions about the state of student’s knowledge and further learning and teaching process depends on these conclusions. To implement the Bayesian network into a student model, it is necessary to determine “a priori” probability of the root nodes, as well as, the conditional probabilities of all other nodes. In our approach, we enable non-empirical mathematical determination of conditional probabilities, while “a priory” probabilities are empirically determined based on the knowledge test results. The concepts that are believed to have been learned or not learned represent the evidence. Based on the evidence, it is concluded which concepts need to be re-learned, and which not. The study described in this paper has examined 15 ontologically based Bayesian student models. In each model, special attention has been devoted to defining “a priori” probabilities, conditional probabilities and the way the evidences are set in order to test the successfulness of student knowledge prediction. Finally, the obtained results are analyzed and the guidelines for ontology based Bayesian student model design are presented.     

模块化本体的Tableau算法及其性能优化

针对现有模块化本体推理方法通用性低、控制复杂等不足,提出一种基于服务的分布式Tableau算法。模块在进行一致性推理时,对关于外部概念的断言,将调用相应模块的服务进行推理,同一推理中的矛盾在定义相应概念的模块中得到捕获,采用优化技术改进算法的时间性能。实验结果表明,该算法使得模块在表述知识时能灵活引用外部概念,支持复杂的推理任务,具有较好的可伸缩性。     

Learning through playing Virtual Age: Exploring the interactions among student concept learning,gaming performance,in-game behaviors,and the use of in-game characters

Video games possess many unique features that facilitate learning. Meanwhile, teaching about evolution is never an easy task due to the existence of some barriers to its learning. Virtual Age, therefore, has been developed in an attempt to harness the power of gaming to increase student understanding of biological evolution. The aim of this study was to examine whether Virtual Age is effective for learning about evolution and to further explore the interplay of student concept learning, gaming performance, and in-game behaviors. A total of 62 7th graders took part in the study, and significant findings were revealed. The students did learn by playing Virtual Age, and their long-term knowledge retention was promising. The in-game behaviors, such as times and duration of viewing the relevant information embedded in Virtual Age, were significantly related to gaming performance (game score), which subsequently influenced learning outcomes. Moreover, the results of cluster analysis indicated that three clusters of low learning outcomes/low gaming performance, high learning outcomes, and high gaming performance emerged. Overall, Virtual Age is an effective game for learning about evolution based on its sound and sophisticated design. Implications derived from the study and suggestions for future work are proposed.     

Virtual reality for collaborative e-learning

In the past, the term e-learning referred to any method of learning that used electronic delivery methods. With the advent of the Internet however, e-learning has evolved and the term is now most commonly used to refer to online courses. A multitude of systems are now available to manage and deliver learning content online. While these have proved popular, they are often single-user learning environments which provide little in the way of interaction or stimulation for the student. As the concept of lifelong learning now becomes a reality and thus more and more people are partaking in online courses, researchers are constantly exploring innovative techniques to motivate online students and enhance the e-learning experience. This article presents our research in this area and the resulting development of CLEV-R, a Collaborative Learning Environment with Virtual Reality. This web-based system uses Virtual Reality (VR) and multimedia and provides communication tools to support collaboration among students. In this article, we describe the features of CLEV-R, its adaptation for mobile devices and present the findings from an initial evaluation.     

《数据结构》课程知识本体构建研究

介绍当代移动学习的特点和语义网本体技术,提出利用本体技术来适应移动学习的片段式学习方式要求,并以《数据结构》课程为例阐述课程知识本体库的具体构建过程．为其在移动学习系统中的应用打下研究基础。     

Knowledge-driven intelligent quality problem-solving system in the automotive industry

In the current automotive industry, quality management, especially quality problem-solving (QPS), plays an important role in fulfilling the expectations of demanding customers who seek high-quality products at low-cost. During the problem-solving process, various real-time and historical quality data are often not fully used, yet these data are of high value. This paper provides a comprehensive quality data mining process and method, as well as an intelligent quality problem-solving system (IQPSS). First, based on original quality problem data, an ontology library is constructed using the ontology generating module (OGM). Second, based on the generated ontology and the textual data of the original quality problem, this study builds a quality problem-solving knowledge base (QPSKB) by employing relevant algorithms in the knowledge transformation module (KTM). The component and fault relational matrix mining (CFRMM) algorithm is designed to extract the relationship matrix between the components and faults. The semi-supervised classification algorithm based on the K-nearest neighbor algorithm (KNN) is used to classify the immediate measures, causes and long-term measures into the corresponding ontology and express the ontology as their knowledge. Furthermore, the binary tree-based support vector machine (SVM) approach is applied to classify the cause texts into the factors of Man, Machine, Material, Method, and Environment (4M1E), which are the five factors in a fishbone diagram. In particular, the digital fishbone diagram is a brand-new type of fishbone diagram that subverts the traditional method of fishbone diagram analysis through brainstorming. A pilot run of the IQPSS has been undertaken in an automotive manufacturing company to demonstrate how quality management employees obtain this knowledge by searching in the IQPSS. The results show that the IQPSS contributes appreciably to the quality problem-solving in the manufacturing industry.     

本体知识库的模块与保守扩充

模块化是软件工程的一种方法,近年来被引入到本体领域,用以支持本体的重用和本体的整合。已有的工作没有讨论同时含有TBox和ABox的本体知识库的模块化的相关问题。在定义本体知识库的模块和知识库的保守扩充的基础之上,给出了知识库保守扩充的验证算法,并且从理论上证明可用知识库的保守扩充的验证算法来验证一个知识库是否为另外一个知识库的模块。     

Coalescing individual and collaborative learning to model user linguistic competences

A linguistic, pedagogic and technological framework for an ICALL system called COPPER is presented here, where individual and collaborative learning are combined within a constructivist approach to facilitate second language learning. Based upon the Common European Framework of Reference for Languages, the ability to use language is viewed as one of several cognitive competences that are mobilised and modified when individuals communicate. To combine the different types of learning underlying the European Framework, a student model has been developed for COPPER that represents linguistic competences in a detailed way, combining high granularity expert-centric Bayesian networks with multidimensional stereotypes, and is updated following student activities semi-automatically. Instances of this model are used by an adaptive group formation algorithm that dynamically generates communicative groups based upon the linguistic capabilities of available students, and a collection of collaborative activity templates. As well as the student model, which is a representation of individual linguistic knowledge, preferences, etc., there is a group model, which is a representation of how a set of students works together. The results of a student’s activity within a group are evaluated by a student monitor, with more advanced linguistic competences, thereby sidestepping the difficulties present when using NLP techniques to automatically analyse non-restricted linguistic production. The monitor role empowers students and further consolidates what has been previously learnt. Students therefore initially work individually in this framework on certain linguistic concepts, and subsequently participate in authentic collaborative communicative activities, where their linguistic competences can develop approximately as they would in ‘real foreign language immersion experiences’.     

Ontology learning: state of the art and open issues

Ontology is one of the fundamental cornerstones of the semantic Web. The pervasive use of ontologies in information sharing and knowledge management calls for efficient and effective approaches to ontology development. Ontology learning, which seeks to discover ontological knowledge from various forms of data automatically or semi-automatically, can overcome the bottleneck of ontology acquisition in ontology development. Despite the significant progress in ontology learning research over the past decade, there remain a number of open problems in this field. This paper provides a comprehensive review and discussion of major issues, challenges, and opportunities in ontology learning. We propose a new learning-oriented model for ontology development and a framework for ontology learning. Moreover, we identify and discuss important dimensions for classifying ontology learning approaches and techniques. In light of the impact of domain on choosing ontology learning approaches, we summarize domain characteristics that can facilitate future ontology learning effort. The paper offers a road map and a variety of insights about this fast-growing field.     

基于本体的多主体网络协作学习模型研究

协作学习是一种组学习方式,强调的是组成员之间的交流与合作,因此成员间的信息理解对协作的效率影响很大,本体论是概念化的明确表示和描述,因此本体可对学习资源进行语义标注.采用本体提供交流资源的统一语义描述,使学习者在语义基础上进行交流和查询,本体的引入保证了网络协作学习中知识理解和运用的一致性、精确性、不变性、可重用性和共享性.设计了基于本体的多主体网络协作学习模型,该模型不仅具有语义互操作性而且能有效支持知识的重用和共享,从而提高协作学习效率.     

启发式知识获取方法研究

归纳学习是解决知识自动获取的有效方法，针对ID3算法、基于粗集的归纳学习以及其它一些归纳学习方法存在的问题，提出了一种新的归纳学习算法ITIL。此算法用信息增益为启发式，选择尽量少的重要属性或组合，以可分辨性为依据提取规则，许多实例表明，这些规则不仅简单，而且冗余小，作为知识获取模块的一部分，ITIL已被集成到一个“基于知识发现的医疗诊断辅助系统”动态知识库子系统中。