Querying Inconsistent Prioritized Data with ORBITS: Algorithms, Implementation, and Experiments

📰 ArXiv cs.AI

Learn to query inconsistent prioritized data using ORBITS algorithms for inconsistency-tolerant query answering

advanced Published 23 Apr 2026

Action Steps

Implement ORBITS algorithms for inconsistency-tolerant query answering over prioritized knowledge bases
Apply Pareto and completion optimal repairs to handle conflicting facts
Evaluate query answers using AR, IAR, and brave semantics
Analyze the complexity of query answering under these semantics
Run experiments to test the performance of ORBITS algorithms

Who Needs to Know This

Data scientists and AI researchers working with prioritized knowledge bases can benefit from this micro-lesson to improve their query answering capabilities

Key Insight

💡 ORBITS algorithms enable inconsistency-tolerant query answering over prioritized knowledge bases

Key Takeaways

Learn to query inconsistent prioritized data using ORBITS algorithms for inconsistency-tolerant query answering

Full Article

Title: Querying Inconsistent Prioritized Data with ORBITS: Algorithms, Implementation, and Experiments

Abstract:
arXiv:2202.07980v4 Announce Type: replace-cross Abstract: We investigate practical algorithms for inconsistency-tolerant query answering over prioritized knowledge bases, which consist of a logical theory, a set of facts, and a priority relation between conflicting facts. We consider three well-known semantics (AR, IAR and brave) based upon two notions of optimal repairs (Pareto and completion). Deciding whether a query answer holds under these semantics is (co)NP-complete in data complexity for

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