查询与修复不一致的优先知识库：复杂性分析与抽象论证的联系
Querying and Repairing Inconsistent Prioritized Knowledge Bases: Complexity Analysis and Links with Abstract Argumentation
AI总结：
本文研究优先知识库中不一致性处理问题，定义了全局、帕累托和完成最优修复，分析了基于这些修复的查询蕴含、唯一最优修复存在性及枚举的数据复杂度，并揭示了最优修复与抽象论证框架扩展之间的关系。
摘要：
本文探讨了优先知识库（由本体、事实集和冲突事实间的优先关系组成）的不一致性处理问题。在数据库设置中，已研究了密切相关的场景，并定义了优先不一致数据库的三种不同最优修复概念（全局、帕累托和完成）。将这些全局、帕累托和完成最优修复概念迁移到我们的设置后，我们研究了核心推理任务的数据复杂度：基于最优修复的不一致性容忍语义下的查询蕴含、唯一最优修复的存在性以及所有最优修复的枚举。我们的结果为用常见DL-Lite方言表述的本体上这些任务的数据复杂度提供了近乎完整的图景。我们工作的第二个贡献是阐明了最优修复与（基于集合的）论证框架不同扩展概念之间的关系。在我们的结果中，我们展示了帕累托最优修复精确对应于稳定扩展（并且通常也对应于优先扩展），并提出了一种受基础扩展启发且具有良好计算特性的优先知识库新语义。我们的研究还产生了一些关于基于偏好的论证框架的独立兴趣结果。
英文摘要：
In this paper, we explore the issue of inconsistency handling over prioritized knowledge bases (KBs), which consist of an ontology, a set of facts, and a priority relation between conflicting facts. In the database setting, a closely related scenario has been studied and led to the definition of three different notions of optimal repairs (global, Pareto, and completion) of a prioritized inconsistent database. After transferring the notions of globally-, Pareto- and completion-optimal repairs to our setting, we study the data complexity of the core reasoning tasks: query entailment under inconsistency-tolerant semantics based upon optimal repairs, existence of a unique optimal repair, and enumeration of all optimal repairs. Our results provide a nearly complete picture of the data complexity of these tasks for ontologies formulated in common DL-Lite dialects. The second contribution of our work is to clarify the relationship between optimal repairs and different notions of extensions for (set-based) argumentation frameworks. Among our results, we show that Pareto-optimal repairs correspond precisely to stable extensions (and often also to preferred extensions), and we propose a novel semantics for prioritized KBs which is inspired by grounded extensions and enjoys favourable computational properties. Our study also yields some results of independent interest concerning preference-based argumentation frameworks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
