Konclude: System description

Steigmiller, Andreas; Liebig, Thorsten; Glimm, Birte

doi:10.1016/j.websem.2014.06.003

Cited by 90 publications

(26 citation statements)

References 13 publications

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“…Through the reduction to classical description logics, it is possible to use available highly-optimized reasoners (e.g. [52,53]), but at the cost of incurring in an exponential blowup whenever the input (fuzzy) ontology uses both nominals and number restrictions. In addition, it includes an explicit representation of the exponentially large automata A r constructed to recognize relevant role chains.…”

Section: Discussionmentioning

confidence: 99%

Algorithms for reasoning in very expressive description logics under infinitely valued Gödel semantics

Borgwardt

Pealoza

2017

International Journal of Approximate Reasoning

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Fuzzy description logics (FDLs) are knowledge representation formalisms capable of dealing with imprecise knowledge by allowing intermediate membership degrees in the interpretation of concepts and roles. One option for dealing with these intermediate degrees is to use the so-called Gödel semantics, under which conjunction is interpreted by the minimum of the degrees of the conjuncts. Despite its apparent simplicity, developing reasoning techniques for expressive FDLs under this semantics is a hard task. In this paper, we introduce two new algorithms for reasoning in very expressive FDLs under Gödel semantics. They combine the ideas of a previous automata-based algorithm for Gödel FDLs with the known crispification and tableau approaches for FDL reasoning. The results are the two first practical algorithms capable of reasoning in infinitely valued FDLs supporting general concept inclusions.

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Section: Discussionmentioning

confidence: 99%

Algorithms for reasoning in very expressive description logics under infinitely valued Gödel semantics

Borgwardt

Pealoza

2017

International Journal of Approximate Reasoning

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“…It does not support functional roles, nor the interval constraints used to model storage duration, therefore it cannot be used to reason on the PL profile. Konclude [37] is a highly optimized reasoner with "pay-as-you-go" strategies (i.e. it becomes more efficient on less complex profiles of OWL2).…”

Section: Related Workmentioning

confidence: 99%

Big Data and Analytics in the Age of the GDPR

Bonatti

Kirrane

2019

2019 IEEE International Congress on Big Data (BigDataCongress)

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The new European General Data Protection Regulation places stringent restrictions on the processing of personally identifiable data. The GDPR does not only affect European companies, as the regulation applies to all the organizations that track or provide services to European citizens. Free exploratory data analysis is permitted only on anonymous data, at the cost of some legal risks. We argue that for the other kinds of personal data processing, the most flexible and safe legal basis is explicit consent. We illustrate the approach to consent management and compliance with the GDPR being developed by the European H2020 project SPECIAL, and highlight some related big data aspects.

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“…This complexity-theoretic result does not suggest that reasoning algorithms can be efficient in practice. Fortunately, practical reasoners for OWL 2 DL have been implemented: FaCT++ (Tsarkov and Horrocks, 2006), HermiT (Horrocks et al, 2012), Pellet (Sirin et al, 2007), Konclude (Steigmiller et al, 2014). Their efficiency in typical real-world applications (rather than worst-case scenarios) relies upon sophisticated optimisation techniques and the empirical fact that ontologies designed by humans for such applications are often simple enough for basic reasoning techniques.…”

Section: Complexity Of Reasoningmentioning

confidence: 99%

An Introduction to Description Logics and Query Rewriting

Kontchakov

Zakharyaschev

2014

Reasoning Web. Reasoning on the Web in the Big Data Era

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Abstract. This chapter gives an overview of the description logics underlying the OWL 2 Web Ontology Language and its three tractable profiles, OWL 2 RL, OWL 2 EL and OWL 2 QL. We consider the syntax and semantics of these description logics as well as main reasoning tasks and their computational complexity. We also discuss the semantical foundations for first-order and datalog rewritings of conjunctive queries over knowledge bases given in the OWL 2 profiles, and outline the architecture of the ontology-based data access system Ontop.

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Konclude: System description

Cited by 90 publications

References 13 publications

Algorithms for reasoning in very expressive description logics under infinitely valued Gödel semantics

Algorithms for reasoning in very expressive description logics under infinitely valued Gödel semantics

Big Data and Analytics in the Age of the GDPR

An Introduction to Description Logics and Query Rewriting

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