A framework for a modular multi-concept lexicographic closure semantics
This work addresses a specific issue in knowledge representation for AI, offering an incremental improvement in modular semantics for defeasible reasoning.
The authors tackled the problem of extending lexicographic closure semantics for defeasible description logics with typicality by proposing a modular multi-concept framework that distributes defeasible properties into modules and combines their preferences, resulting in a spectrum of alternative semantics.
We define a modular multi-concept extension of the lexicographic closure semantics for defeasible description logics with typicality. The idea is that of distributing the defeasible properties of concepts into different modules, according to their subject, and of defining a notion of preference for each module based on the lexicographic closure semantics. The preferential semantics of the knowledge base can then be defined as a combination of the preferences of the single modules. The range of possibilities, from fine grained to coarse grained modules, provides a spectrum of alternative semantics.