by Nicola Fanizzi, Claudia D’Amato, F Esposito
Abstract:
We propose semantic distance measures based on the criterion of approximate discernibility and on evidence combination. In the presence of incomplete knowledge, the distance measures the degree of belief in the discernibility of two individuals by combining estimates of basic probability masses related to a set of discriminating features. We also suggest ways to extend this distance for comparing individuals to concepts and concepts to other concepts.
Reference:
Approximate measures of semantic dissimilarity under uncertainty (Nicola Fanizzi, Claudia D’Amato, F Esposito), In Uncertainty Reasoning for the Semantic Web I, Springer, 2008.
Bibtex Entry:
@article{fanizzi2008approximate,
abstract = {We propose semantic distance measures based on the criterion of approximate discernibility and on evidence combination. In the presence of incomplete knowledge, the distance measures the degree of belief in the discernibility of two individuals by combining estimates of basic probability masses related to a set of discriminating features. We also suggest ways to extend this distance for comparing individuals to concepts and concepts to other concepts.},
author = {Fanizzi, Nicola and D’Amato, Claudia and Esposito, F},
journal = {Uncertainty Reasoning for the Semantic Web I},
keywords = {SML-LIB-BIBLIO,lang:ENG},
mendeley-tags = {SML-LIB-BIBLIO,lang:ENG},
pages = {348--365},
publisher = {Springer},
title = {{Approximate measures of semantic dissimilarity under uncertainty}},
year = {2008}
}