# A Rule-Based Approach to Specifying Preferences over Conflicting Facts and Querying Inconsistent Knowledge Bases

arXiv: https://arxiv.org/abs/2508.07742 · HTML: https://mlsanity.com/paper/2508.07742/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 37 references that are in the map.
- First posted on arXiv 11 Aug 2025.

## Builds on (most cited first, 3 of 3)

- [Complexity of Consistent Query Answering in Databases under Cardinality-Based and Incremental Repair Semantics (extended version)](https://mlsanity.com/paper/1605.07159/) (2016)
- [Querying and Repairing Inconsistent Prioritized Knowledge Bases: Complexity Analysis and Links with Abstract Argumentation](https://mlsanity.com/paper/2003.05746/) (2020)
- [Inconsistency Handling in Prioritized Databases with Universal Constraints: Complexity Analysis and Links with Active Integrity Constraints](https://mlsanity.com/paper/2306.03523/) (2023)

## Built on by (most cited first, 1 of 1)

- [Using ASP(Q) to Handle Inconsistent Prioritized Data](https://mlsanity.com/paper/2604.21603/) (2026)

## Abstract (arXiv)

Repair-based semantics have been extensively studied as a means of obtaining meaningful answers to queries posed over inconsistent knowledge bases (KBs). While several works have considered how to exploit a priority relation between facts to select optimal repairs, the question of how to specify such preferences remains largely unaddressed. This motivates us to introduce a declarative rule-based framework for specifying and computing a priority relation between conflicting facts. As the expressed preferences may contain undesirable cycles, we consider the problem of determining when a set of preference rules always yields an acyclic relation, and we also explore a pragmatic approach that extracts an acyclic relation by applying various cycle removal techniques. Towards an end-to-end system for querying inconsistent KBs, we present a preliminary implementation and experimental evaluation of the framework, which employs answer set programming to evaluate the preference rules, apply the desired cycle resolution techniques to obtain a priority relation, and answer queries under prioritized-repair semantics.