Modelling Learning Subjects as Relationships [chapter]

Martin Doerr
<span title="">2005</span> <i title="Springer Berlin Heidelberg"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
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
more &raquo; ... ucture 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 userformulated 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.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-540-32279-5_14">doi:10.1007/978-3-540-32279-5_14</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/3btgq7pecvfsljnaebaammie7y">fatcat:3btgq7pecvfsljnaebaammie7y</a> </span>
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