Using ASP(Q) to Handle Inconsistent Prioritized Data

📰 ArXiv cs.AI

Learn to handle inconsistent prioritized data using ASP(Q) for optimal query answering and repairs

advanced Published 25 Apr 2026

Action Steps

Apply ASP(Q) to model prioritized data and define optimal repairs
Use Pareto-, globally-, and completion-optimal repairs to handle inconsistencies
Implement query answering using AR, brave, and IAR semantics with optimal repairs
Configure ASP(Q) to exploit priority relations between conflicting facts
Test the approach on real-world datasets with inconsistent prioritized data

Who Needs to Know This

Data scientists and AI researchers working with prioritized data can benefit from this approach to handle inconsistencies and optimize query answering. This can be particularly useful in applications where data conflicts need to be resolved based on priority relations.

Key Insight

💡 ASP(Q) can be used to define optimal repairs for inconsistent prioritized data, enabling effective query answering and decision-making.

Key Takeaways

Learn to handle inconsistent prioritized data using ASP(Q) for optimal query answering and repairs

Full Article

Title: Using ASP(Q) to Handle Inconsistent Prioritized Data

Abstract:
arXiv:2604.21603v1 Announce Type: cross 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 th

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