数据图上首选子集修复的计算复杂度
Computational Complexity of Preferred Subset Repairs on Data-Graphs
AI总结：
本文研究图数据库上基于子集修复的偏好标准，提出权重、多重集和优先级方法，证明计算复杂度不变，并给出一致查询回答的紧致上下界。
摘要：
偏好是实际推理中的关键组成部分，尤其是在涉及对不同选项或可采取行动方案进行决策的任务中。在本工作中，我们专注于以图数据库形式修复和查询不一致的知识库，这分别涉及找到解决知识库中冲突的方法，并考虑从每种可能的修复中蕴含的答案。在没有先验领域知识的情况下，所有可能的修复都被同等偏好。虽然这在某些设置中可能足够，但在潜在修复之间建立并利用某种形式的偏好顺序似乎是合理的。我们研究在具有数据值的图数据库上计算优先修复的问题，使用基于GXPath表达式作为完整性约束的一致性概念。我们提出了几种基于标准子集修复语义的偏好标准，结合权重、多重集和基于集合的优先级级别。我们表明，在没有任何偏好标准可利用的情况下，保持相同的计算复杂度是可能的。最后，我们探讨了此设置中一致查询回答的复杂度，并为所有引入的偏好标准获得了紧密的上下界。
英文摘要：
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.
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。
