The representation of defeasible information in Description Logics is a well-known issue and many formal approaches have been proposed. However, in these proposals, little attention has been devoted to studying their capabilities in capturing the interpretation of typicality and exceptions from an ontological and cognitive point of view. In this regard, we are developing a model of defeasible knowledge for description logics based on combining ideas from prototype theory, weighted description logic (aka ‘tooth logic’), and earlier work on justifiable exceptions. This machinery is then used to determine exceptions in case of conflicting axioms. In this paper, we analyse this formalisation with respect to some interesting cases where the defeasible properties to which we may have exceptions are also present as features in prototype descriptions. The analysis will suggest that a new preference order, which considers what happens inside the models, may be best suited and we outline how this new preference order can be defined.

Defeasible Reasoning with Prototype Descriptions: A New Preference Order

Loris Bozzato;
2024-01-01

Abstract

The representation of defeasible information in Description Logics is a well-known issue and many formal approaches have been proposed. However, in these proposals, little attention has been devoted to studying their capabilities in capturing the interpretation of typicality and exceptions from an ontological and cognitive point of view. In this regard, we are developing a model of defeasible knowledge for description logics based on combining ideas from prototype theory, weighted description logic (aka ‘tooth logic’), and earlier work on justifiable exceptions. This machinery is then used to determine exceptions in case of conflicting axioms. In this paper, we analyse this formalisation with respect to some interesting cases where the defeasible properties to which we may have exceptions are also present as features in prototype descriptions. The analysis will suggest that a new preference order, which considers what happens inside the models, may be best suited and we outline how this new preference order can be defined.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11383/2180834
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