We study a family of operators (called ‘Tooth’ operators) that combine Description Logic concepts via weighted sums. These operators are intended to capture the notion of instances satisfying “enough” of the concept descriptions given. We examine two variants of these operators: the “knowledge-independent” one, that evaluates the concepts with respect to the current interpretation, and the “knowledge-dependent” one that instead evaluates them with respect to a specified knowledge base, comparing and contrasting their properties. We furthermore discuss the connections between these operators and linear classification models.

On Knowledge Dependence in Weighted Description Logic

Galliani P;
2019-01-01

Abstract

We study a family of operators (called ‘Tooth’ operators) that combine Description Logic concepts via weighted sums. These operators are intended to capture the notion of instances satisfying “enough” of the concept descriptions given. We examine two variants of these operators: the “knowledge-independent” one, that evaluates the concepts with respect to the current interpretation, and the “knowledge-dependent” one that instead evaluates them with respect to a specified knowledge base, comparing and contrasting their properties. We furthermore discuss the connections between these operators and linear classification models.
2019
GCAI 2019. Proceedings of the 5th Global Conference on Artificial Intelligence
5th Global Conference on Artificial Intelligence (GCAI 2019)
Bolzano
17.9.2019 - 19.9.2019
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11383/2145666
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