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 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

 Grazyna Szostek and Marek Jaszuk. Automatic Supply of a Medical Knowledge Base Using Linguistic Methods. Emerging Intelligent Technologies in Industry. Studies in Computational Intelligence, 2011, Volume 369/2011, 143-155.

 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

 Antonio Lieto. Manually vs semiautomatic domain specific ontology building. MSc Thesis. University of Salerno. Italy. 2008.

Year 2006 : 1 citations

 Alireza Kashian, Mehdi Milanifard, Hashem Tatari. Collimator " Collaborative Image Annotator & Visual Concept Map Generator. In Proceedings of the 5th International Semantic Web Conference. 2006

Year 2004 : 1 citations

 ASUNCIÿN GÿMEZ-PÿREZ and DAVID MANZANO-MACHO. "An overview of methods and tools for ontology learning from texts".The Knowledge Engineering Review (2004), 19: 187-212 Cambridge University Press

Year 2003 : 1 citations

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