CISUC

Extracting Concept Maps with Clouds

Authors

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 2017 : 1 citations

 AL-Aswadi, F. N., & Yong, C. H. (2017). A Study of Various Ontology Learning Systems from Text and a Look into Future. work, 2(5), 9-10.

Year 2012 : 1 citations

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Year 2011 : 3 citations

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 Jorge J. Villalón, Rafael A. Calvo. Concept Maps as Cognitive Visualizations of Writing Assignments. Educational Technology & Society 14(3): 16-27 (2011).

 Marco Tawfik, Mostafa Aref and Abdel-Badeeh Salem. An Overview of Ontology Learning From Unstructured Texts. INFORMATICS’2011 - International Scientific Conference on Informatics, Ro??ava, Slovakia, 2011.

Year 2010 : 2 citations

 Yuen-Hsien Tseng, Chun-Yen Chang, Shu-Nu Chang Rundgren, Carl-Johan Rundgren. Mining concept maps from news stories for measuring civic scientific literacy in media, Computers & Education, Volume 55, Issue 1, August 2010, Pages 165-177, ISSN 0360-1315.

 Hsin-fu Chen. Dynamic Hierarchical Clustering Based on Taxonomy. MSc Thesis. National CentralUniversity. Taiwan. 2010.

Year 2009 : 1 citations

 Wong, W. (2009)/ Learning Lightweight Ontologies from Text across Different Domains using the Web as Background Knowledge/. In: Doctor of Philosophy; University of Western Australia.

Year 2008 : 1 citations

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Year 2006 : 1 citations

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Year 2004 : 1 citations

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Year 2003 : 1 citations

 Gomez-Perez A., Manzano-Macho D.: A Survey of Ontology Learning Methods and Techniques. Deliverable 1.5, OntoWeb Project, 2003.