Weighted Conditional EL{^}bot Knowledge Bases with Integer Weights: an ASP Approach
This work addresses a technical challenge in knowledge representation for AI, but it is incremental as it builds on existing multipreference semantics and applies known ASP tools.
The paper tackles the problem of reasoning with weighted conditional EL^bot knowledge bases under concept-wise multipreference semantics by encoding the entailment using Answer Set Programming (ASP) and asprin, specifically for integer weights.
Weighted knowledge bases for description logics with typicality have been recently considered under a "concept-wise" multipreference semantics (in both the two-valued and fuzzy case), as the basis of a logical semantics of Multilayer Perceptrons. In this paper we consider weighted conditional EL^bot knowledge bases in the two-valued case, and exploit ASP and asprin for encoding concept-wise multipreference entailment for weighted KBs with integer weights.