4 research outputs found
Complexity of Inconsistency-Tolerant Query Answering in Datalog+/- under Cardinality-Based Repairs
This is the author accepted manuscript. The final version is available from Association for the Advancement of Artificial Intelligence (AAAI) via the link in this recordQuerying inconsistent ontological knowledge bases is an important
problem in practice, for which several inconsistencytolerant
query answering semantics have been proposed, including
query answering relative to all repairs, relative to
the intersection of repairs, and relative to the intersection of
closed repairs. In these semantics, one assumes that the input
database is erroneous, and the notion of repair describes a
maximally consistent subset of the input database, where different
notions of maximality (such as subset and cardinality
maximality) are considered. In this paper, we give a precise
picture of the computational complexity of inconsistencytolerant
(Boolean conjunctive) query answering in a wide
range of Datalog± languages under the cardinality-based versions
of the above three repair semantics.This work was supported by the Alan
Turing Institute under the UK EPSRC grant EP/N510129/1,
and by the EPSRC grants EP/R013667/1, EP/L012138/1,
and EP/M025268/1
Complexity of Inconsistency-Tolerant Query Answering in Datalog+/- under Cardinality-Based Repairs
Querying inconsistent ontological knowledge bases is an important problem in practice, for which
several inconsistency-tolerant semantics have been proposed. In these semantics, the input database is
erroneous, and a repair is a maximally consistent database subset. Different notions of maximality (such
as subset and cardinality maximality) have been considered. In this paper, we give a precise picture of
the computational complexity of inconsistency-tolerant query answering in a wide range of Datalog+/–
languages under the cardinality-based versions of three prominent repair semantic
Querying and Repairing Inconsistent Prioritized Knowledge Bases: Complexity Analysis and Links with Abstract Argumentation
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.Comment: 27 pages. To appear in the 17th International Conference on
Principles of Knowledge Representation and Reasoning (KR 2020) without the
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