Abstract. In this paper we discuss the design of the SCAM framework, which aims to simplify the storage and access of metadata for a variety of different applications that can be built on top of it. A basic design principle of SCAM is the aggregation of metadata into two kinds of sets of different granularity (SCAM records and SCAM contexts). These sets correspond to the typical access needs of an application with regard to metadata, and they constitute the foundation upon which access control is provided. 1

This paper introduces the Human Semantic Web (HSW) as a conceptual interface, providing human-understandable semantics on top of the ordinary (machine) Semantic Web, which provides machine-readable semantics based on RDF. The HSW is structured in the form of a Knowledge Manifold and makes use of Unified Language Modeling (based on the Unified Modeling Language) combined with conceptual browsing to present its information to the user in a way that creates substantial benefits in terms of overview, clarity and flexibility. The HSW-browser Conzilla combines the semantics of RDF with the human-understandable semantics of UML in order to enable more powerful forms of human-computer interaction – such as querying the Semantic Web through Edutella and supporting the concept-incontext methodology. The Semantic Web is discussed in terms of three levels of semantic interoperability: isolation, coexistence and collaboration. Collaboration, as the highest goal, can be achieved by conceptual calibration, which builds bridges between different

words) While E-Learning is increasingly influencing university and workplace education in Europe, several critical issues still have to be solved in order to achieve the full potential of technology enhanced learning in many of these learning scenarios. The EU/IST FP6 PROLEARN Network of Excellence in Technology Enhanced Learning is focussing on these issues, to advance the state of the art in this area, through a large concerted effort of more than 150 research institutions and companies working together in the PROLEARN Consortium and as PROLEARN Associated Partners. An extended version of this paper has been published as an ED-Media 2004 Invited Talk [22]. Keywords Professional education, technology enhanced learning, personalized learning, interactive media, learning resources 1. Restructuring and Integrating European Research on E-Learning In the United States, enterprises invest 20 percent of their budget (13 Billion US-$ out of 66 Billion US-$) for professional education in E-Learning activities (Corporate University Exchange 2000, http://www.corpu.com/). A similar trend is now becoming apparent for Europe as well. According to research by the Gartner Group, the European market of corporate E-Learning had a capacity of 829 million $ in 2001, and it will grow to 7.4 billion $ by the year 2004 (Scienter, Bologna 2001). Despite these impressive figures, it has repeatedly been reported that the actual impact of R&D on learning technologies remains limited so far, indicating that too few R&D results are picked up in actual practice. One of the reasons for this is that technology enhanced learning research and researchers are scattered across quite a few distinct communities, with often very limited awareness of the other communities. Different communities focus on...

Abstract. Self-managing systems will be highly dependent upon information acquired from disparate applications, devices, components and subsystems. To be effectively managed, such information will need to conform to a common model. One standard that provides a common model for describing disparate computer and network information is the Common Information Model (CIM). Although CIM defines the models necessary for inferring properties about distributed systems, its specification as a semi-formal ontology limits its ability to support important requirements of a self-managing distributed system including knowledge interoperability and aggregation, as well as reasoning. To support these requirements, there is a need to model, represent and share CIM as a formal ontology. In this paper, we propose a framework for constructing a CIM ontology based upon previous research that identified mappings from Unified Modeling Language (UML) constructs to ontology language constructs. We extend and apply these mappings to a UML representation of the CIM Schema in order to derive a semantically valid and consistent formal CIM ontology. 1

2/10 The application of “Semantic Web ” technologies to learning processes is receiving an increasing attention from the perspective of facilitating the selection, delivery and tailoring of learning experiences. But most of the current approaches are centered on the final interaction of the learner with the “learning objects ” provided for him/her, neglecting the organizational perspective. From the viewpoint of an organization, the application of Semantic Web technologies should be motivated by the improvement of learning-oriented mechanisms, including both cultural and structural aspects, and considering the ideal of achieving a state of continuous improvement in learning behavior. Such an approach to achieving a “semantic learning organization ” gives a complementary perspective to existing “educational Semantic Web ” propositions. In this paper, the potential role of the Semantic Web as a driver for enhanced learning organizations is analyzed, and a conceptual framework for the notion of a semantic learning organization is provided. Keywords: E-learning, ontologies, Semantic Web, organizational learning, learning organization. The “Semantic ” Learning Organization

The integration of learning objects with Semantic Web technologies requires the representation of learning object metadata in ontological databases. In this paper, some of the issues regarding the expression of learning object specifications as part of the OpenCyc terminological knowledge base are discussed, illustrating some of the advanced behaviors that are enabled by such integration. 1.

Life-long learning is a key issue for our knowledge society. With social software systems new heterogeneous kinds of technology enhanced informal learning are now available to the life-long learner. Learners outside of learning institutions now have access to powerful social communities of experts and peers who are together forging a new web 2.0. This paper reviews current work in pan-European initiatives that impact upon life-long learning via views of professional learning, learner competence and social networking. It seeks to provide an overview of some of the critical research questions for the interdisciplinary field of social software research.

In order to exchange learning resources via educational mediators, resources need to be described with metadata in a coherent manner. Metadata for learning resources has become a widely discussed research topic, but the concept is still too loosely defined to provide guidelines for its use. With this paper we aim to contribute to the design of metadata models, which serve as vehicles for defining educational offers in exchange environments. In the context of educational mediators, the learning object notion is clarified using a taxonomy, which differentiates between educational activities and educational material. The model is derived from two exemplifying use cases of educational mediators implemented in the EducaNext portal: “exchange of educational material ” and “mediation of cross-institutional educational activities“. The paper targets developers and stakeholders of educational mediators as well as educators and technologists dealing with metadata standards in general.

(based on the 2001/2002 proposal)

This paper describes a set of new technologies, tools, paradigms and infrastructures that have been developed and studied by the KMR group. Based on the emerging nextgeneration World Wide Web, the so-called Semantic Web, these developments rely on international learning technology standards to support the sharing of digital content for learning and the building of virtual learning communities, especially in mathematics. 1.