3,123 research outputs found
Prioritized Repairing and Consistent Query Answering in Relational Databases
A consistent query answer in an inconsistent database is an answer obtained
in every (minimal) repair. The repairs are obtained by resolving all conflicts
in all possible ways. Often, however, the user is able to provide a preference
on how conflicts should be resolved. We investigate here the framework of
preferred consistent query answers, in which user preferences are used to
narrow down the set of repairs to a set of preferred repairs. We axiomatize
desirable properties of preferred repairs. We present three different families
of preferred repairs and study their mutual relationships. Finally, we
investigate the complexity of preferred repairing and computing preferred
consistent query answers.Comment: Accepted to the special SUM'08 issue of AMA
Optimizing the computation of overriding
We introduce optimization techniques for reasoning in DLN---a recently
introduced family of nonmonotonic description logics whose characterizing
features appear well-suited to model the applicative examples naturally arising
in biomedical domains and semantic web access control policies. Such
optimizations are validated experimentally on large KBs with more than 30K
axioms. Speedups exceed 1 order of magnitude. For the first time, response
times compatible with real-time reasoning are obtained with nonmonotonic KBs of
this size
Priority-Based Conflict Resolution in Inconsistent Relational Databases
We study here the impact of priorities on conflict resolution in inconsistent
relational databases. We extend the framework of repairs and consistent query
answers. We propose a set of postulates that an extended framework should
satisfy and consider two instantiations of the framework: (locally preferred)
l-repairs and (globally preferred) g-repairs. We study the relationships
between them and the impact each notion of repair has on the computational
complexity of repair checking and consistent query answers
Reasoning in inconsistent prioritized knowledge bases: an argumentative approach
A study of query answering in prioritized ontological knowledge bases (KBs) has received attention in recent years. While several semantics of query answering have been proposed and their complexity is rather well-understood, the problem of explaining inconsistency-tolerant query answers has paid less attention. Explaining query answers permits users to understand not only what is entailed or not entailed by an inconsistent DL-LiteR KBs in the presence of priority, but also why. We, therefore, concern with the use of argumentation frameworks to allow users to better understand explanation techniques of querying answers over inconsistent DL-LiteR KBs in the presence of priority. More specifically, we propose a new variant of Dungâs argumentation frameworks, which corresponds to a given inconsistent DL-LiteR KB. We clarify a close relation between preferred subtheories adopted in such prioritized DL-LiteR setting and acceptable semantics of the corresponding argumentation framework. The significant result paves the way for applying algorithms and proof theories to establish preferred subtheories inferences in prioritized DL-LiteR KBs
On various forms of bipolarity in flexible querying
International audienceThe paper discusses the modeling of âif possible" in requirements of the form âA and if possible B". We distinguish between two types of understanding: either i) A and B are requirements of the same nature and are viewed as constraints with different levels of priority, or ii) they are of different nature (only A induces constraint(s) and B is only used for breaking ties among items that are equally satisfying A). We indicate that the two views are related to different types of bipolarity, and discuss them in relation with possibilistic logic. The disjunctive dual of the first view (âA or at least B") is then presented in this logical setting. We also briefly mention the idea of an extension of the second view where B may refer both to bonus conditions or malus conditions that may increase or decrease respectively the interest in an item satisfying A
Belief Revision in Structured Probabilistic Argumentation
In real-world applications, knowledge bases consisting of all the information
at hand for a specific domain, along with the current state of affairs, are
bound to contain contradictory data coming from different sources, as well as
data with varying degrees of uncertainty attached. Likewise, an important
aspect of the effort associated with maintaining knowledge bases is deciding
what information is no longer useful; pieces of information (such as
intelligence reports) may be outdated, may come from sources that have recently
been discovered to be of low quality, or abundant evidence may be available
that contradicts them. In this paper, we propose a probabilistic structured
argumentation framework that arises from the extension of Presumptive
Defeasible Logic Programming (PreDeLP) with probabilistic models, and argue
that this formalism is capable of addressing the basic issues of handling
contradictory and uncertain data. Then, to address the last issue, we focus on
the study of non-prioritized belief revision operations over probabilistic
PreDeLP programs. We propose a set of rationality postulates -- based on
well-known ones developed for classical knowledge bases -- that characterize
how such operations should behave, and study a class of operators along with
theoretical relationships with the proposed postulates, including a
representation theorem stating the equivalence between this class and the class
of operators characterized by the postulates
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