CISUC

Extracting Concept Maps with Clouds

Authors

Francisco Câmara Pereira
Ana Cristina da Costa Oliveira Alves
F. Amilcar Cardoso

Abstract

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.

Keywords

Machine Learning, Concept Mapping

Subject

Cognitive Modelling

Conference

ASAI'00, October 2000

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Cited by

Year 2019 : 3 citations

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Year 2018 : 1 citations

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Year 2017 : 2 citations

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Year 2012 : 1 citations

 Wilson Wong, Wei Liu, and Mohammed Bennamoun. 2012. Ontology learning from text: A look back and into the future. ACM Comput. Surv. 44, 4, Article 20 (September 2012), 36 pages.

Year 2011 : 3 citations

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Year 2010 : 2 citations

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Year 2009 : 1 citations

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Year 2008 : 1 citations

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Year 2007 : 1 citations

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Year 2004 : 1 citations

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Year 2003 : 1 citations

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