Ontologies and E-Learning

What is an ontology? Why is this relevant to a learning environment? It is quite well-established in recent investigations on information systems that formal ontologies area crucial problem to deal with, and in fact, received a lot of attention in several different communities, such as knowledge management, knowledge engineering, natural language processing, intelligent information integration, and so on (Fensel, 2000).Ontologies have been developed in artificial intelligence to facilitate knowledge sharing and reuse. The viewpoint we adopt here is taken from the general considerations on the use of philosophical issues in artificial intelligence: “the systematic, formal, axiomatic development of the logic of all forms and modes of being” (Cocchiarella,1991). Another commonly accepted definition is that an ontology is an explicit specification of a shared conceptualization that holds in a particular context.

Ontology-Based Competency Management

Learning activities can be considered the final outcome of a complex process inside knowledge intensive organizations. This complex process encompasses a dynamic cycle, a loop in which business or organizational needs trigger the necessity of acquiring or enhancing human resource competencies that are essential to the fulfillment of the organizational objectives. This continuous evolution of organizational knowledge requires the management of records of available and required competencies, and the automation of such competency handling thus becomes a key issue for the effective functioning of knowledge management activities. This chapter describes the use of ontologies as the enabling semantic infrastructure of competency management, describing the main aspects and scenarios of the knowledge creation cycle from the perspective of its connection with competency definitions.

Enabling Technologies for the Semantic Web

Before understanding the Semantic Web and its associated benefits, one must first be somewhat familiar with the enabling technologies upon which the Semantic Web is based. The extensible markup language (XML), uniform resource identifiers (URIs), resource definition framework (RDF), ontologies, and intelligent agents are all key tithe realization of the Semantic Web. Understanding these key technologies gives readers a firm foundation before progressing to subsequent chapters. This chapter provides a broad overview of each technology, and readers new to these technologies are provided with references to more detailed explanations.

Knowledge Agents

In this chapter, we approach some significant concepts consistent with knowledge and cognitive processes that are essential for any kind of contemporary organization. Therefore, after citing a generic approach to knowledge management and its facilitating tools, along with a description of software agents and their categories, we indicate precious elements and details for the prerequisites while designing and implementing such intelligent solutions. As prerequisites, we deem organizational context, programming, and developing tools. We then discuss collaborative agent systems, known as agent societies, and present some appealing implementations of complex agent systems. Finally, we portray some of our thoughts regarding the perspective of employing smart agent technology in our everyday life.

The Corporate Learning Environment

Emerging technology has changed the focus of corporate learning systems from task-based, procedural training to knowledge-intensive problem-solving with deep conceptual learning. In addition, the deployment of open systems and distributing processing are adding new stresses to learning systems that can barely keep pace with the current rate of change. Learning environments to address these challenges a reviewed within a framework of the conventional learning curve, in which different learning elements are required to support different levels of expertise. An adaptive development model for creating and sustaining a learning environment is proposed that consists of the iterative application of three phases: (1) analysis and reflection, (2) architecture inception and revision, and (3) alignment. The model relies on the notion that analysis deals as much with synthesis and learning as it does with decomposition. We conclude that the concept of a “learning environment” provides a viable construct for making sense of the array of systems designed to support knowledge management, document management, e-learning, and performance support. A learning environment with a well-defined architecture can guide the convergence of multiple systems into a seamless environment providing access to content, multimedia learning modules, collaborative workspaces, and other forms of learning support. Finally, we see future learning environments consisting of networks of databases housing content objects, elegant access to the content, ubiquitous virtual spaces, and authoring tools that enable content vendors, guilds, and universities to rapidly develop and deliver a wide range of learning artifacts.

Toward an Integrated E-Collaboration Framework for Learning Purposes

This chapter discusses issues to be considered in the development of a framework with advanced e-collaboration features for learning purposes. Having first identified the underlying requirements, we review enabling technologies and propose an approach that seamlessly integrates knowledge management, decision-making, argumentative discourse, and simulation issues. In addition, we comment on the extent to which our approach satisfies the needs of virtual learning communities and supports various learning methods such as learning by doing, conversational learning, and constructive criticism of an issue or an abstract idea. The proposed framework acts as a medium on which diverse knowledge and information sources can be attached, thus aiding people involved in a learning process to widen their perspectives and learn from past experience.

Classroom for the Semantic Web

This chapter emphasizes integration of Semantic Web technologies in intelligent learning systems by giving a proposal for an intelligent learning management system (ILMS) architecture we named Multitutor. This system is a Web-based environment forth development of e-learning courses and for the use of them by the students. Multitutor is designed as a Web-classroom client-server system, ontologically founded, and is built using modern intelligent and Web-related technologies. This system enables the teachers to develop tutoring systems for any course. The teacher has to define the metadata of the course: chapters, the lessons and the tests, the references of the learning materials. We also show how the Multitutor system can be employed to develop learning systems that use ontologically created learning materials as well as Web services. As an illustration we describe a simple Petri net teaching system that is based on the Petrinet infrastructure for the Semantic Web.

Building Integrative Enterprise Knowledge Portals with Semantic Web Technologies

The goal of this chapter is to show how Semantic Web technologies can help build integrative enterprise knowledge portals. Three main areas are identified: content management and metadata, global searching, and the integration of external content and applications. For these three areas the state-of-the-art as well as current research results are discussed. In particular, a metadata-based information retrieval and a context-based port let integration approach are presented. These have been implemented in a research prototype which is introduced in the Internet session at the end of the chapter.

Knowledge Management and Knowledge Management Systems

Organizations are fast realizing that knowledge management (KM) is critical to achieve competitive sustainability. However, mere realization that KM is critical does not ensure a smooth road to success. Fifty to seventy percent of KM initiatives reportedly fail. One of the main reasons of this failure is the lack of understanding of effective dimensions of KM implementation. In this chapter, we propose an integrated framework for knowledge management. Special attention is given to how knowledge management systems should be positioned within organizations. Examples of successful integration are provided by three case studies from different organizations.

Collaboration and Pervasiveness

This chapter attempts to contribute toward exploring fundamental conceptualization on collaboration and pervasiveness in education. An assigned task is to clarify my concepts on collaborative learning based on ubiquitous computation and Semantic Web perspectives by means of more originated ways. Collaborative activities and computer-supported collaborative learning (CSCL) per se consists of various needs to encourage motivation and understandings of each student in more effective learning style and environment. We can recognize that collaborative learning in a ubiquitous environment can provide more interactive, experiential, spatiotemporal, and distributed aspects for anyone who wants to know information and solve educational tasks coordinating with others at any time. Then, I would like to show my design of the ubiquitous jigsaw method and self-organizing networks in the learning community. Further, I concentrate on exploring possibilities of collaborative learning with semantic technologies which allows to inspire and facilitate a more reciprocal exchange among affiliated relationships in a ubiquitous environment. Finally, I will discuss these topics.