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This research introduces a declarative rule-based framework to resolve inconsistencies in knowledge bases by defining priority relations between conflicting facts using Answer Set Programming. The study addresses critical challenges such as detecting and removing undesirable cycles within preference rules, offering both theoretical guarantees for acyclic outcomes and practical cycle removal techniques. By enabling meaningful query answers over inconsistent data through prioritized-repair semantics, this work advances the foundational logic required for robust AI systems managing real-world noisy information.
A Rule-Based Approach to Specifying Preferences over Conflicting Facts and Querying Inconsistent Knowledge Bases
arXiv:2508.07742v3 Announce Type: replace-cross Abstract: 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.
Abstract: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.
This is an extended version of a paper appearing at the 22nd International Conference on Principles of Knowledge Representation and Reasoning (KR 2025). 24 pages. This version corrects Definition 4 and some minor glitches (in particular a few misreported numbers in the experimental section)
Logic in Computer Science (cs.LO); Artificial Intelligence (cs.AI); Databases (cs.DB)
arXiv:2508.07742 [cs.LO]
(or arXiv:2508.07742v3 [cs.LO] for this version)
Full text under the publisher's open licence · 219 words
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