2 results for Agrawal, R

  • Building rich, semantic descriptions of learning activities to facilitate reuse in digital libraries

    Gahegan, Mark; Agrawal, R; Banchuen, Tawan; DiBiase, D (2007)

    Journal article
    The University of Auckland Library

    This paper describes efforts to extend educational descriptions of learning objects to enable semantic search for suitable resources held within digital libraries and cyberinfrastructure, and describes some further advantages that accrue from the use of formal description languages (ontologies) to describe both pedagogy and domain content. These advantages include: semantic browsing and visualization of learning object contents, advanced search capabilities linking to several different online collections, easy extension of learning objects with external content added by learners and educators, and utilization of the many rich models of education and educational domains now available as ontologies. As well as conceptual justifications and descriptions of our work, we provide examples throughout to concretize the ideas presented, using learning objects developed for college-level education in geography and the geosciences. We conclude with some thoughts on the further possibilities that arise from the application of detailed semantics, and associated reasoning, in the pursuit of genuinely reusable educational content that integrates more closely with community research activities such as exemplified by e-science.

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  • A platform for visualizing and experimenting with measures of semantic similarity in ontologies and concept maps

    Gahegan, Mark; Agrawal, R; Jaiswal, A; Luo, J; Soon, K (2008)

    Journal article
    The University of Auckland Library

    This article describes research in the ongoing search for better semantic similarity tools: such methods are important when attempting to reconcile or integrate knowledge, or knowledge-related resources such as ontologies and database schemas. We describe an extensible, open platform for experimenting with different measures of similarity for ontologies and concept maps. The platform is based around three different types of similarity, that we ground in cognitive principles and provide a taxonomy and structure by which new similarity methods can be integrated and used. The platform supports a variety of specific similarity methods, to which researchers can add others of their own. It also provides flexible ways to combine the results from multiple methods, and some graphic tools for visualizing and communicating multi-part similarity scores. Details of the system, which forms part of the ConceptVista open codebase, are described, along with associated details of the interfaces by which users can add new methods, choose which methods are used and select how multiple similarity scores are aggregated. We offer this as a community resource, since many similarity methods have been proposed but there is still much confusion about which one(s) might work well for different geographical problems; hence a test environment that all can access and extend would seem to be of practical use. We also provide some examples of the platform in use.

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