ECAI 2004 Conference Paper

Comparing Conceptual, Partitional and Agglomerative Clustering for Learning Taxonomies from Text

Philipp Cimiano, Andreas Hotho, Steffen Staab

The application of methods for automatic taxonomy construction from text requires knowledge about the trade-off between, (i), their effectiveness (quality of result), (ii), efficiency (run-time behaviour), and,(iii), traceability of the taxonomy construction by the ontology engineer. To offer such a trade-off, we define an original method to use conceptual clustering based on Formal Concept Analysis for automatic taxonomy construction and we compare it against hierarchical agglomerative clustering and hierarchical partitional clustering.

Keywords: knowledge acquisition, ontology learning, term clustering, text mining

Citation: Philipp Cimiano, Andreas Hotho, Steffen Staab: Comparing Conceptual, Partitional and Agglomerative Clustering for Learning Taxonomies from Text. In R.López de Mántaras and L.Saitta (eds.): ECAI2004, Proceedings of the 16th European Conference on Artificial Intelligence, IOS Press, Amsterdam, 2004, pp.435-439.

ECAI-2004 is organised by the European Coordinating Committee for Artificial Intelligence (ECCAI) and hosted by the Universitat Politècnica de València on behalf of Asociación Española de Inteligencia Artificial (AEPIA) and Associació Catalana d'Intel-ligència Artificial (ACIA).