Comparing Conceptual, Divise and Agglomerative Clustering for Learning Taxonomies from Text
P. Cimiano, A. Hotho, und S. Staab.
ECAI 2004 Proceedings of the 16th European Conference on Artificial Intelligence, 22 - 27 August, Valencia, Spain, Seite 435-439. IOS Press, (2004)

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.

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@inproceedings{cimiano2004comparing,
  abstract = {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.},
  added-at = {2011-02-04T16:09:24.000+0100},
  author = {Cimiano, Philipp and Hotho, Andreas and Staab, Steffen},
  biburl = {https://puma.uni-kassel.de/bibtex/24e2f4ba3e051f120c2bc8216aad7cdaa/benz},
  booktitle = {ECAI 2004 Proceedings of the 16th European Conference on Artificial Intelligence, 22 - 27 August, Valencia, Spain},
  editor = {de M\'{a}ntaras, R. L\'{o}pez and Saitta, L.},
  file = {cimiano2004comparing.pdf:cimiano2004comparing.pdf:PDF},
  groups = {public},
  interhash = {5ebc73142f0c4d51a1037432435bab94},
  intrahash = {4e2f4ba3e051f120c2bc8216aad7cdaa},
  keywords = {clustering ol_web2.0 ontology_learning taxonomy_learning methods_from_text},
  pages = {435-439},
  publisher = {IOS Press},
  timestamp = {2011-02-17T17:41:56.000+0100},
  title = {Comparing Conceptual, Divise and Agglomerative Clustering for Learning Taxonomies from Text},
  username = {dbenz},
  year = 2004
}

%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.

PUMA

Comparing Conceptual, Divise and Agglomerative Clustering for Learning Taxonomies from Text
P. Cimiano, A. Hotho, und S. Staab.
ECAI 2004 Proceedings of the 16th European Conference on Artificial Intelligence, 22 - 27 August, Valencia, Spain, Seite 435-439. IOS Press, (2004)

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Comparing Conceptual, Divise and Agglomerative Clustering for Learning Taxonomies from TextP. Cimiano, A. Hotho, und S. Staab. ECAI 2004 Proceedings of the 16th European Conference on Artificial Intelligence, 22 - 27 August, Valencia, Spain, Seite 435-439. IOS Press, (2004)

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Comparing Conceptual, Divise and Agglomerative Clustering for Learning Taxonomies from Text
P. Cimiano, A. Hotho, und S. Staab.
ECAI 2004 Proceedings of the 16th European Conference on Artificial Intelligence, 22 - 27 August, Valencia, Spain, Seite 435-439. IOS Press, (2004)