Using ASP(Q) to Handle Inconsistent Prioritized Data

2026-04-23

AI Summary

This work addresses inconsistency-tolerant query answering over prioritized conflicting data by proposing a unified framework based on Answer Set Programming (ASP) and its quantified extension ASP(Q), supporting three notions of optimal repairs: Pareto, global, and complete. It presents the first formalization and implementation of globally optimal repair semantics along with a tractable grounded variant. Through logical encodings and complexity analysis, the study delivers efficient computational strategies. Empirical evaluation demonstrates the practical computability of global optimal repairs and systematically assesses the impact of different repair semantics, approximation techniques, and encoding approaches on query performance.

Technology Category

• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Knowledge Representation and Reasoning: Qualitative Reasoning
• Search and Optimization: Adversarial Search

Application Category

• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Provenance, trust, security and privacy, and ethical issues in managing semantic data

Abstract

We explore the use of answer set programming (ASP) and its extension with quantifiers, ASP(Q), for inconsistency-tolerant querying of prioritized data, where a priority relation between conflicting facts is exploited to define three notions of optimal repairs (Pareto-, globally- and completion-optimal). We consider the variants of three well-known semantics (AR, brave and IAR) that use these optimal repairs, and for which query answering is in the first or second level of the polynomial hierarchy for a large class of logical theories. Notably, this paper presents the first implementation of globally-optimal repair-based semantics, as well as the first implementation of the grounded semantics, which is a tractable under-approximation of all these optimal repair-based semantics. Our experimental evaluation sheds light on the feasibility of computing answers under globally-optimal repair semantics and the impact of adopting different semantics, approximations, and encodings.

Problem

• inconsistent data
• prioritized data
• optimal repairs
• query answering
• inconsistency-tolerant reasoning

Innovation

• ASP(Q)
• inconsistency-tolerant querying
• globally-optimal repair
• grounded semantics
• prioritized data