Week 3: Semantic Web, Linked Data, & Intelligent Curriculum

Juanma:
Another issue is that we need to formalize the concepts and linkage relationships, i.e. the knowledge about educational concepts, so that they can be computer processed. We have to agree on the 'model' underlying that knowledge. But some people can be reluctant to an uniform, machine-understandable way of formalizing and expliciting the knowledge on education and educational research.

Formalization requires agreement about definitions. Not only about education, but epistemology as a whole. What can we know? What is expertise? How should we define and structure knowledge? There is a bottom-up approach to this which becomes more feasible if the definitions and the structures are making sense and are already adopted by the community using them. An imposed ontology is less likely to become accepted than an emerged ontology. That leads to the question: how can we influence the emergence of ontologies? We have seen that folksonomies tend to expand and therefore more difficult to manage. If three different words in a folksonomy have the same meaning, how can we make people want to use only one?

My assumption is that reputation is an instrument that can be used for this matter: Suppose that people's reputation in a community is expressed using the keywords/definitions of the community ontology. Then these keywords all have a relative value that decreases with the number of keywords that mean the same. It is therefore wise for people who find their reputation important to agree upon a set of keywords that express their knowledge, and not use too many keywords for that.

PS. I am doing research in this direction, and wrote an algorithm that supports this reputation mechanism.