Abstract

The paper focuses on the task of approximate classification of semantically annotated individual resources in ontological knowledge bases. The method is based on classification models built through kernel methods, a well-known class of effective statistical learning algorithms. Kernel functions encode a notion of similarity among elements of some input space. The definition of a family of parametric language- independent kernel functions for individuals occurring in an ontology allows the application of these statistical learning methods on Semantic Web knowledge bases. The classification models induced by kernel methods offer an alternative way to classify individuals with respect to the typical exact and approximate deductive reasoning procedures. The proposed statistical setting enables further inductive approaches to a variety of other tasks that can better cope with the inherent incompleteness of the knowledge bases in the Semantic Web and with their potential incoherence due to their distributed nature. The effectiveness of the proposed method is empirically proved through experiments on the task of approximate classification with real ontologies collected from standard repositories.

Autore Pugliese

• D'amato Claudia , Fanizzi Nicola

Tutti gli autori

• D'AMATO C.;ESPOSITO F.;FANIZZI N.

Titolo volume/Rivista

Non Disponibile

Anno di pubblicazione

2012

ISSN

1570-8268

ISBN

Non Disponibile

Numero di citazioni Wos

6

Ultimo Aggiornamento Citazioni

Non Disponibile

Numero di citazioni Scopus

11

Ultimo Aggiornamento Citazioni

Non Disponibile

Settori ERC

Non Disponibile

Codici ASJC

Non Disponibile