AIMar 1, 2024

Know your exceptions: Towards an Ontology of Exceptions in Knowledge Representation

arXiv:2403.00685v22 citationsh-index: 11FOIS
Originality Synthesis-oriented
AI Analysis

This work addresses a challenge for knowledge representation modellers in choosing suitable formalisms, but it is incremental as it builds on existing systems without introducing a new paradigm.

The paper tackles the problem of selecting appropriate defeasible reasoning formalisms for modeling common-sense contexts by proposing a framework based on exceptionality and defeasibility to compare systems and reveal their ontological commitments, applying it to four systems to show differences from an ontological perspective.

Defeasible reasoning is a kind of reasoning where some generalisations may not be valid in all circumstances, that is general conclusions may fail in some cases. Various formalisms have been developed to model this kind of reasoning, which is characteristic of common-sense contexts. However, it is not easy for a modeller to choose among these systems the one that better fits its domain from an ontological point of view. In this paper we first propose a framework based on the notions of exceptionality and defeasibility in order to be able to compare formalisms and reveal their ontological commitments. Then, we apply this framework to compare four systems, showing the differences that may occur from an ontological perspective.

Foundations

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