Taxonomies are becoming essential in several fields, playing an important role in a large number of applications, particularly for specific domains. Taxonomies provide efficient tools to people by organizing a huge amount of information into a small hierarchical structure. Taxonomies were originally built by hand, but nowadays the technology permits to produce a vast amount of information. Consequently, recent research activities have been focused on automated taxonomy generation. In this paper, we propose a novel approach for automatically build a taxonomy, starting from a set of categories. We deem that, in a hierarchical structure, each node should intuitively be represented with proper meaningful and discriminant features, instead of considering a fixed feature space. Our proposal relies on two metrics able to identify the most meaningful features. Our conjecture is that a feature could significantly change its discriminant power (hence, its role) along the taxonomy levels. Hence, we devise a greedy algorithm able to build a taxonomy by identifying the meaningful terms for each level. We perform preliminary experiments that give rise to the usefulness of the proposed approach.

Automated taxonomy building by adopting discriminant and characteristic capabilities

ARMANO, GIULIANO;GIULIANI, ALESSANDRO;TAMPONI, EMANUELE
2016

Abstract

Taxonomies are becoming essential in several fields, playing an important role in a large number of applications, particularly for specific domains. Taxonomies provide efficient tools to people by organizing a huge amount of information into a small hierarchical structure. Taxonomies were originally built by hand, but nowadays the technology permits to produce a vast amount of information. Consequently, recent research activities have been focused on automated taxonomy generation. In this paper, we propose a novel approach for automatically build a taxonomy, starting from a set of categories. We deem that, in a hierarchical structure, each node should intuitively be represented with proper meaningful and discriminant features, instead of considering a fixed feature space. Our proposal relies on two metrics able to identify the most meaningful features. Our conjecture is that a feature could significantly change its discriminant power (hence, its role) along the taxonomy levels. Hence, we devise a greedy algorithm able to build a taxonomy by identifying the meaningful terms for each level. We perform preliminary experiments that give rise to the usefulness of the proposed approach.
Computer Science (all); IIR filters; Information retrieval; Amount of information; Discriminant power; Feature space; Greedy algorithms; Hierarchical structures; Recent researches; Taxonomy generation; Taxonomies
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11584/197255
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