In 2004 we published in this journal an article describing OntoLearn, one of the first systems to automatically induce a taxonomy from documents and Web sites. Since then, OntoLearn has continued to be an active area of research in our group and has become a reference work within the community. In this paper we describe our next-generation taxonomy learning methodology, which we name OntoLearn Reloaded. Unlike many taxonomy learning approaches in the literature, our novel algorithm learns both concepts and relations entirely from scratch via the automated extraction of terms, definitions, and hypernyms. This results in a very dense, cyclic and potentially disconnected hypernym graph. The algorithm then induces a taxonomy from this graph via optimal branching and a novel weighting policy. Our experiments show that we obtain high-quality results, both when building brand-new taxonomies and when reconstructing sub-hierarchies of existing taxonomies. © 2013 Association for Computational Linguistics.

Ontolearn reloaded: A graph-based algorithm for taxonomy induction / Velardi, Paola; Faralli, Stefano; Navigli, Roberto. - In: COMPUTATIONAL LINGUISTICS. - ISSN 1530-9312. - STAMPA. - 39:3(2013), pp. 665-700. [10.1162/coli_a_00146]

Ontolearn reloaded: A graph-based algorithm for taxonomy induction

VELARDI, Paola;FARALLI, Stefano;NAVIGLI, ROBERTO
2013

Abstract

In 2004 we published in this journal an article describing OntoLearn, one of the first systems to automatically induce a taxonomy from documents and Web sites. Since then, OntoLearn has continued to be an active area of research in our group and has become a reference work within the community. In this paper we describe our next-generation taxonomy learning methodology, which we name OntoLearn Reloaded. Unlike many taxonomy learning approaches in the literature, our novel algorithm learns both concepts and relations entirely from scratch via the automated extraction of terms, definitions, and hypernyms. This results in a very dense, cyclic and potentially disconnected hypernym graph. The algorithm then induces a taxonomy from this graph via optimal branching and a novel weighting policy. Our experiments show that we obtain high-quality results, both when building brand-new taxonomies and when reconstructing sub-hierarchies of existing taxonomies. © 2013 Association for Computational Linguistics.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11573/486922
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