Preferences are a pivotal component in practical reasoning, especially in tasks that involve decision-making over different options or courses of action that could be pursued. In this work, we focus on repairing and querying inconsistent knowledge bases in the form of graph databases, which involves finding a way to solve conflicts in the knowledge base and considering answers that are entailed from every possible repair, respectively. Without a priori domain knowledge, all possible repairs are equally preferred. Though that may be adequate for some settings, it seems reasonable to establish and exploit some form of preference order among the potential repairs. We study the problem of computing prioritized repairs over graph databases with data values, using a notion of consistency based on GXPath expressions as integrity constraints. We present several preference criteria based on the standard subset repair semantics, incorporating weights, multisets, and set-based priority levels. We show that it is possible to maintain the same computational complexity as in the case where no preference criterion is available for exploitation. Finally, we explore the complexity of consistent query answering in this setting and obtain tight lower and upper bounds for all the preference criteria introduced.

翻译：偏好是实践推理中的关键组成部分，尤其在涉及对不同选项或可能采取的行动方案进行决策的任务中。本文研究以图数据库形式存在的知识库的修复与查询问题，这分别涉及寻找解决知识库中冲突的方法，以及考虑从每个可能修复中推导出的答案。在没有先验领域知识的情况下，所有可能的修复具有同等偏好。虽然这对某些场景可能足够，但在潜在修复间建立并利用某种形式的偏好顺序似乎是合理的。我们研究了在具有数据值的图数据库上计算优先修复的问题，采用基于GXPath表达式作为完整性约束的一致性概念。我们提出了基于标准子集修复语义的多种偏好准则，融合了权重、多重集和基于集合的优先级层次。我们证明了即使存在可利用的偏好准则，仍能保持与无偏好准则情形相同的计算复杂性。最后，我们探索了该设定下一致性查询应答的复杂性，并对提出的所有偏好准则给出了严格的下界与上界。