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Modelling Learning Subjects as Relationships

  • Martin Doerr
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3359)

Abstract

This paper describes a novel intellectual structure for the subject space of material designed for selective autodidactic learning in a large knowledge base. This structure is based on a systematic theory-driven connection of categorical and factual knowledge. It is further based on the idea, that relevant subjects in such a system should be propositions, represented as categorical relationships. Informal inferences between concept hierarchies and categorical relationships are presented. The structure is implemented in a system for training of art conservators in diagnostic knowledge. Multiple systematic ways are implemented to give the user access and overview over large amounts of such subject propositions, that try to overcome the typical mismatch between a user-formulated request and the terms understood by the system, and the general disorientation of users in larger electronic media. The system is right now being presented to users with a first small population of cases.

Keywords

Semantic Network Factual Knowledge Individual Concept Categorical Knowledge Knowledge Element 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Martin Doerr
    • 1
  1. 1.Institute of Computer ScienceFoundation for Research and Technology – Hellas, Science and Technology Park of Crete, Vassilika VoutonHeraklion, CreteGreece

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