Toward a Semantic Approach for Automatic Composition of Learning Grid Services

Due to their potential advantages, the use of Grid technologies toward the development of collaborative and personalized learning frameworks has been considerably increased. These learning frameworks can be constructed based on the distributed learning services and resources available in a learning Grid environment. A problem still unsolved is how to use and integrate low-level learning services to compose more complex high-level services or tools that can be useful to both tutors and learners. In that sense, on the one hand, semantic description of Grid learning services appears to be a powerful tool that can be used to discover suitable learning services depending on the system’s semantic capabilities. On the other hand, it can be employed to carry out a matching process among the learning services located in a learning framework to obtain the best fit according to specific functional parameters. These parameters represent significant characteristics of a learning Grid environment. This paper presents an initial effort that integrates schema and ontology matching methods that aim to develop a model to cope with the complex problem of automatic composition of Grid based learning tools and their portals.