Computational Complexity of Preferred Subset Repairs on Data-Graphs
CoRR(2024)
摘要
The problem of repairing inconsistent knowledge bases has a long history
within the communities of database theory and knowledge representation and
reasoning, especially from the perspective of structured data. However, as the
data available in real-world domains becomes more complex and interconnected,
the need naturally arises for developing new types of repositories,
representation languages, and semantics, to allow for more suitable ways to
query and reason about it. Graph databases provide an effective way to
represent relationships among semi-structured data, and allow processing and
querying these connections efficiently. In this work, we focus on the problem
of computing prioritized repairs over graph databases with data values, using a
notion of consistency based on Reg-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
study the most common repairing tasks, showing that it is possible to maintain
the same computational complexity as in the case where no preference criterion
is available for exploitation. To complete the picture, we explore the
complexity of consistent query answering in this setting and obtain tight lower
and upper bounds for all the preference criteria introduced.
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