%0 Conference Paper
%1 cimiano2004comparing
%A Cimiano, Philipp
%A Hotho, Andreas
%A Staab, Steffen
%B ECAI 2004 Proceedings of the 16th European Conference on Artificial Intelligence, 22 - 27 August, Valencia, Spain
%D 2004
%E de Mántaras, R. López
%E Saitta, L.
%I IOS Press
%K clustering ol_web2.0 ontology_learning taxonomy_learning methods_from_text
%P 435-439
%T Comparing Conceptual, Divise and Agglomerative Clustering for Learning Taxonomies from Text
%X The application of clustering methods for automatic taxonomy construction from text requires knowledge about the tradeoff between, (i), their effectiveness (quality of result), (ii), efficiency (run-time behaviour), and, (iii), traceability of the taxonomy construction by the ontology engineer. In this line, we present an original conceptual clustering method based on Formal Concept Analysis for automatic taxonomy construction and compare it with hierarchical agglomerative clustering and hierarchical divisive clustering.