LOAIDBAug 11, 2025

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

arXiv:2508.07742v11 citationsh-index: 28DL
Originality Incremental advance
AI Analysis

This work addresses a gap in how to specify preferences for selecting optimal repairs in inconsistent knowledge bases, which is incremental as it builds on existing repair-based semantics.

The paper tackles the problem of specifying preferences over conflicting facts in inconsistent knowledge bases, introducing a declarative rule-based framework and demonstrating its implementation with answer set programming for querying.

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.

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