Abstract
This paper presents a technique to automatically derive ontologies which is based on hierarchical clustering of document corpora. The procedure applies to a set of texts forming a domain document corpus and creates a hierarchical structure (tree) where at every node is associated a set of terms derived from the document feature vectors. The labeling of the cluster is made by using a new algorithm presented in this work. The derived terms may represent concepts candidate to build a domain taxonomy from which the hierarchical relationships among the classes of the domain ontology can be extracted. To test the technique shown, has been built a propotype tool named (OntoClust).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Cimiano, P., Staab, S.: Learning Concept Hierarchies from Text with a Guided Hierarchical Clustering Algorithm. In: Proceedings of the Workshop on Learning and Extending Lexical Ontologies with Machine Learning Methods (2005)
Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K., Harshman, R.: Indexing by Latent Semantic Analysis. Journal of the Society for Information Science 41(6), 391–407 (1990)
Fortuna, B., Mladenic, D., Grobelnik, M.: Semi-Automatic Construction of Topic Ontology. In: Ackermann, M., Berendt, B., Grobelnik, M., Hotho, A., Mladenič, D., Semeraro, G., Spiliopoulou, M., Stumme, G., Svátek, V., van Someren, M. (eds.) EWMF 2005 and KDO 2005. LNCS (LNAI), vol. 4289, pp. 121–131. Springer, Heidelberg (2006)
Kashyap, V., Ramakrishnan, C., Thomas, C., Sheth, A.: TaxaMiner: An Experimental Framework for Automated Taxonomy Bootstrapping. International Journal of Web and Grid Services 1(2), 240–266 (2005)
Maedche, A., Staab, S.: Ontology Learning for the Semantic Web. Intelligent Systems, IEEE 16(2), 72–79 (2001)
Miller, G.A.: WordNet, a lexical database for the English language, Cognitive Science Laboratory, Princeton University, http://wordnet.princeton.edu/
Porter, M., Boulton, R., Macfarlane, A.: Snowball, a small string processing language designed for creating stemming algorithms for use in Information Retrieval
Ramakrishnan, C., Thomas, C., Kashyap, V., Sheth, A.: TaxaMiner: Improving Taxonomy Label Quality. In: McIlraith, S.A., Plexousakis, D., van Harmelen, F. (eds.) ISWC 2004. LNCS, vol. 3298. Springer, Heidelberg (2004)
Salton, G., Buckley, C.: Term weighting approaches in automatic text retrieval. Technical Report TR 87-881, Cornell University (November 1987)
Studier, J.A., Keppler, K.J.: A note on the Neighbor Joining Algorithm of Saitou and Nei. Mol. Biol. Evol. 5(6), 729–731 (1988)
Zhou, X., Zhang, X., Hu, X.: Dragon Toolkit: Incorporating Auto-learned Semantic Knowledge into Large-Scale Text Retrieval and Mining. In: Proceedings of 19th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2007 (2007)
Hotho, Maedche, A., Stabb, S.: Ontology-based Text Document Clustering. In: KI (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Di Martino, B., Cantiello, P. (2009). Automatic Ontology Extraction with Text Clustering. In: Papadopoulos, G.A., Badica, C. (eds) Intelligent Distributed Computing III. Studies in Computational Intelligence, vol 237. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03214-1_22
Download citation
DOI: https://doi.org/10.1007/978-3-642-03214-1_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03213-4
Online ISBN: 978-3-642-03214-1
eBook Packages: EngineeringEngineering (R0)