Semantic Reasoning with SPARQL in Heterogeneous Multi-context Systems

Schüller, Peter; Weinzierl, Antonius

doi:10.1007/978-3-642-22056-2_59

Cited by 2 publications

(2 citation statements)

References 9 publications

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“…Beyond bridge rules: In [23] we introduce MCS where knowledge exchange is realised using SPARQL construct-queries. This is surprisingly simple and again shows the versatility of MCS.…”

Section: Contributions: Methods Of Inconsistency Managementmentioning

confidence: 99%

Advancing Multi-Context Systems by Inconsistency Management

Weinzierl¹

2011

Preprint

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Multi-Context Systems are an expressive formalism to model (possibly) non-monotonic information exchange between heterogeneous knowledge bases. Such information exchange, however, often comes with unforseen side-effects leading to violation of constraints, making the system inconsistent, and thus unusable. Although there are many approaches to assess and repair a single inconsistent knowledge base, the heterogeneous nature of Multi-Context Systems poses problems which have not yet been addressed in a satisfying way: How to identify and explain a inconsistency that spreads over multiple knowledge bases with different logical formalisms (e.g., logic programs and ontologies)? What are the causes of inconsistency if inference/information exchange is nonmonotonic (e.g., absent information as cause)? How to deal with inconsistency if access to knowledge bases is restricted (e.g., companies exchange information, but do not allow arbitrary modifications to their knowledge bases)? Many traditional approaches solely aim for a consistent system, but automatic removal of inconsistency is not always desireable. Therefore a human operator has to be supported in finding the erroneous parts contributing to the inconsistency. In my thesis those issues will be adressed mainly from a foundational perspective, while our research project also provides algorithms and prototype implementations.

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“…Beyond bridge rules: In [23] we introduce MCS where knowledge exchange is realised using SPARQL construct-queries. This is surprisingly simple and again shows the versatility of MCS.…”

Section: Contributions: Methods Of Inconsistency Managementmentioning

confidence: 99%

Advancing Multi-Context Systems by Inconsistency Management

Weinzierl¹

2011

Preprint

Self Cite

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“…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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Semantic Reasoning with SPARQL in Heterogeneous Multi-context Systems

Cited by 2 publications

References 9 publications

Advancing Multi-Context Systems by Inconsistency Management

Advancing Multi-Context Systems by Inconsistency Management

Preference-Based Inconsistency Management in Multi-Context Systems

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