Repairing inconsistent knowledge bases is a task that has been assessed, with great advances over several decades, from within the knowledge representation and reasoning and the database theory communities. As information becomes more complex and interconnected, new types of repositories, representation languages and semantics are developed in order to be able to query and reason about it. Graph databases provide an effective way to represent relationships among data, and allow processing and querying these connections efficiently. In this work, we focus on the problem of computing preferred (subset and superset) repairs for graph databases with data values, using a notion of consistency based on a set of Reg-GXPath expressions as integrity constraints. Specifically, we study the problem of computing preferred repairs based on two different preference criteria, one based on weights and the other based on multisets, showing that in most cases it is possible to retain the same computational complexity as in the case where no preference criterion is available for exploitation.

翻译：修复不一致的知识库是一项已经被知识表示与推理及数据库理论领域评估数十年的任务，并取得了重大进展。随着信息变得更加复杂和相互关联，新的存储库类型、表示语言和语义被开发出来，以便能够对其进行查询和推理。图数据库提供了一种有效表示数据间关系的方式，并允许高效处理和查询这些关联。在本工作中，我们聚焦于为具有数据值的图数据库计算优先（子集和超集）修复的问题，使用基于一组Reg-GXPath表达式作为完整性约束的一致性概念。具体而言，我们研究了基于两种不同偏好标准（一种基于权重，另一种基于多重集）计算优先修复的问题，并表明在大多数情况下，可以保持与无可利用偏好标准情况相同的计算复杂度。