ReduCE: A reduced coulomb energy network method for approximate classification

10Citations
Citations of this article
20Readers
Mendeley users who have this article in their library.

Abstract

In order to overcome the limitations of purely deductive approaches to the tasks of classification and retrieval from ontologies, inductive (instance-based) methods have been proposed as efficient and noise-tolerant alternative. In this paper we propose an original method based on non-parametric learning: the Reduced Coulomb Energy (RCE) Network. The method requires a limited training effort but it turns out to be very effective during the classification phase. Casting retrieval as the problem of assessing the class-membership of individuals w.r.t. the query concepts, we propose an extension of a classification algorithm using RCE networks based on an entropic similarity measure for OWL. Experimentally we show that the performance of the resulting inductive classifier is comparable with the one of a standard reasoner and often more efficient than with other inductive approaches. Moreover, we show that new knowledge (not logically derivable) is induced and the likelihood of the answers may be provided. © 2009 Springer Berlin Heidelberg.

References Powered by Scopus

208Citations
109Readers

This article is free to access.

A framework for handling inconsistency in changing ontologies

142Citations
89Readers

DL-FOIL concept learning in description logics

134Citations
36Readers
Get full text

Cited by Powered by Scopus

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Cite

CITATION STYLE

APA

Fanizzi, N., D’Amato, C., & Esposito, F. (2009). ReduCE: A reduced coulomb energy network method for approximate classification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5554 LNCS, pp. 323–337). https://doi.org/10.1007/978-3-642-02121-3_26

Readers over time

‘10‘11‘12‘13‘16‘18‘20‘22‘2302468

Readers' Seniority

Tooltip

PhD / Post grad / Masters / Doc 11

65%

Researcher 4

24%

Professor / Associate Prof. 2

12%

Readers' Discipline

Tooltip

Computer Science 13

76%

Engineering 2

12%

Biochemistry, Genetics and Molecular Bi... 1

6%

Economics, Econometrics and Finance 1

6%

Save time finding and organizing research with Mendeley

Sign up for free
0