Extracting Concept Maps with Clouds



This paper presents Clouds, a program that aims to extract structural domain
knowledge from the user. This extraction consists of the use of three different
algorithms: one for choosing the concept to work with; and two, based on inductive
learning, for suggesting new concepts and relations. In a first phase, the user must
?teach? the computer with some important concepts from the domain that he wants to
transmit. Then, gradually, in a simple dialogue, Clouds asks questions resulting from
the learnt hypothesis.
Each concept is an element of a map, called concept map, which consists of a graph
with concepts on nodes, and relations on arcs.
This work is an essay on the application of machine learning on knowledge extraction
and is part of a wider project, named Dr. Divago, which will get from Clouds the help
to build complete and coherent concept maps that it needs to work efficiently. Apart
from this, we believe Clouds? scope can be extended to many different areas, namely
natural language processing, human-computer dialogue and intelligent tutoring.


Machine Learning, Concept Mapping


Cognitive Modelling


ASAI'00, October 2000

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Year 2018 : 1 citations

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