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 functions measure 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. Integrated within a k-Nearest Neighbor algorithm, the measures have been experimentally tested on a task of inductive concept retrieval demonstrating the effectiveness of their application.
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Fanizzi, N., d’Amato, C., Esposito, F. (2008). Approximate Measures of Semantic Dissimilarity under Uncertainty. In: da Costa, P.C.G., et al. Uncertainty Reasoning for the Semantic Web I. URSW URSW URSW 2006 2007 2005. Lecture Notes in Computer Science(), vol 5327. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89765-1_20
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DOI: https://doi.org/10.1007/978-3-540-89765-1_20
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