Towards Query Answering in Relational Multi-Context Systems

Barilaro, Rosamaria; Fink, Michael; Ricca, Francesco; Terracina, Giorgio

doi:10.1007/978-3-642-40564-8_17

Cited by 4 publications

(6 citation statements)

References 4 publications

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“…In practical applications however, there should either be a finite number of applicable (ground instances of) bridge-rules, or some suitable device for run-time dynamic bridge-rule instantiation and application should be provided. The issue of bridgerule grounding has been discussed in [4] for relational MCSs, where however the grounding is performed over a carefully defined finite domain, composed of constants only. Consider for instance a patient looking for a cardiologist from a medical directory, represented as a context, say, e.g., called med-dir.…”

Section: Motivating Scenarios and Discussionmentioning

confidence: 99%

“…In an implemented mMCS, as remarked in [4], "...computing equilibria and answering queries on top is not a viable solution." So, they assume a given MCS to admit an equilibrium, and define a query-answering procedure based upon some syntactic restriction on bridge-rule form, and involving the application and a concept of "unfolding" of positive atoms in bridge-rule bodies w.r.t.…”

Section: Logical Omniscience Assumption and Bridge Rules Application Mechanismsmentioning

confidence: 99%

“…To the best of our knowledge, the problem of loosening the constraint of bridgerules groundedness has not been so far extensively treated in the literature and no satisfactory solution exists. The issue has been discussed in [4] for relational MCSs, where however the grounding of bridge rules is performed over a carefully defined finite domain composed of constants only. Instead, we intend to consider any, even infinite, domain.…”

Section: Grounded Knowledge Assumptionmentioning

confidence: 99%

“…A context designator m(k) is a term where m is a fresh function symbol and k a fresh constant. 4 A bridge-rule pattern φ is an expression of the form:…”

Section: Static Set Of Bridge Rules: Bridge-rules Patternsmentioning

confidence: 99%

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Multi-context systems in dynamic environments

Cabalar

Costantini

Gasperis

et al. 2019

Ann Math Artif Intell

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Multi-Context Systems (MCS) model in Computational Logic distributed systems composed of heterogeneous sources, or "contexts", interacting via special rules called "bridge rules". In this paper, we consider how to enhance flexibility and generality in bridge-rules definition and application. In particular, we introduce and discuss some formal extensions of MCSs useful for a practical use in dynamic environments, and we try to provide guidelines for implementations.Multi-Context Systems (MCSs) have been proposed in Artificial Intelligence and Knowledge Representation to model information exchange among heterogeneous sources [8,9,11]. MCSs do not make any assumption about such sources nor require them to be homogeneous; rather, the MCS approach deals explicitly with their This work is partially supported by INdAM-GNCS-17 project, by Xunta de Galicia, Spain (projects GPC ED431B 2016/035 and 2016-2019 ED431G/01 for CITIC center) and by the European Regional Development Fund (ERDF).

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Section: Motivating Scenarios and Discussionmentioning

confidence: 99%

Section: Logical Omniscience Assumption and Bridge Rules Application Mechanismsmentioning

confidence: 99%

Section: Grounded Knowledge Assumptionmentioning

confidence: 99%

“…A context designator m(k) is a term where m is a fresh function symbol and k a fresh constant. 4 A bridge-rule pattern φ is an expression of the form:…”

Section: Static Set Of Bridge Rules: Bridge-rules Patternsmentioning

confidence: 99%

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Multi-context systems in dynamic environments

Cabalar

Costantini

Gasperis

et al. 2019

Ann Math Artif Intell

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show abstract

“…We emphasize that bridge rules only deal with elements of knowledge bases and elements of belief sets, both of which are considered to be atomic expressions from the perspective of MCS. Incorporating variables into bridge rules is possible but requires restrictions on context logics or additional machinery for variable substitution (for details, see Fink, Ghionna, & Weinzierl, 2011;Barilaro, Fink, Ricca, & Terracina, 2013;Schüller & Weinzierl, 2011).…”

Section: Preliminariesmentioning

confidence: 99%

Preference-Based Inconsistency Management in Multi-Context Systems

Eiter¹,

Weinzierl²

2017

jair

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Multi-Context Systems (MCS) are a powerful framework for interlinking possibly heterogeneous, autonomous knowledge bases, where information can be exchanged among knowledge bases by designated bridge rules with negation as failure. An acknowledged issue with MCS is inconsistency that arises due to the information exchange. To remedy this problem, inconsistency removal has been proposed in terms of repairs, which modify bridge rules based on suitable notions for diagnosis of inconsistency. In general, multiple diagnoses and repairs do exist; this leaves the user, who arguably may oversee the inconsistency removal, with the task of selecting some repair among all possible ones. To aid in this regard, we extend the MCS framework with preference information for diagnoses, such that undesired diagnoses are filtered out and diagnoses that are most preferred according to a preference ordering are selected. We consider preference information at a generic level and develop meta-reasoning techniques on diagnoses in MCS that can be exploited to reduce preference-based selection of diagnoses to computing ordinary subset-minimal diagnoses in an extended MCS. We describe two meta-reasoning encodings for preference orders: the first is conceptually simple but may incur an exponential blowup. The second is increasing only linearly in size and based on duplicating the original MCS. The latter requires nondeterministic guessing if a subset-minimal among all most preferred diagnoses should be computed. However, a complexity analysis of diagnoses shows that this is worst-case optimal, and that in general, preferred diagnoses have the same complexity as subset-minimal ordinary diagnoses. Furthermore, (subset-minimal) filtered diagnoses and (subset-minimal) ordinary diagnoses also have the same complexity.

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Bridge Rules for Reasoning in Component-Based Heterogeneous Environments

Costantini

Gasperis

2016

Rule Technologies. Research, Tools, and Applications

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Towards Query Answering in Relational Multi-Context Systems

Cited by 4 publications

References 4 publications

Multi-context systems in dynamic environments

Multi-context systems in dynamic environments

Preference-Based Inconsistency Management in Multi-Context Systems

Bridge Rules for Reasoning in Component-Based Heterogeneous Environments

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