Search results for "Description Logic"

showing 10 items of 20 documents

Temporal Logic To Query Semantic Graphs Using The Model Checking Method

2012

International audience; Semantic interoperability problems have found their solutions due to the use of languages and techniques from the Semantic Web. The proliferations of ontologies and meta-information have improved the understanding of information and the relevance of search engine responses. However, the construction of semantic graphs is a source of numerous errors of interpretation or modeling, and scalability remains a major problem. The processing of large semantic graphs is a limit to the use of semantics in current information systems. The work presented in this paper is part of a new research at the border of two areas: the semantic web and the model checking. This line of rese…

[INFO.INFO-WB] Computer Science [cs]/WebComputer science[INFO.INFO-SE] Computer Science [cs]/Software Engineering [cs.SE][ INFO.INFO-WB ] Computer Science [cs]/WebSPARQL.02 engineering and technology[INFO.INFO-SE]Computer Science [cs]/Software Engineering [cs.SE][ INFO.INFO-SE ] Computer Science [cs]/Software Engineering [cs.SE]Ontology (information science)computer.software_genreQuery languagetemporal logic querySPARQLSocial Semantic WebSearch engineDescription logicSemantic similaritytemporal logicArtificial IntelligenceWeb query classificationSemantic computing0202 electrical engineering electronic engineering information engineeringInformation systemSemantic analyticsSPARQLSemantic Web StackRDFSemantic Webcomputer.programming_language[INFO.INFO-SC]Computer Science [cs]/Symbolic Computation [cs.SC]Web search querySemantic Web Rule LanguageProgramming languagebusiness.industry[INFO.INFO-SC] Computer Science [cs]/Symbolic Computation [cs.SC][INFO.INFO-WB]Computer Science [cs]/Web020207 software engineeringcomputer.file_formatSemantic interoperabilitymodel checking[ INFO.INFO-SC ] Computer Science [cs]/Symbolic Computation [cs.SC]Human-Computer InteractionSemantic graph020201 artificial intelligence & image processingbusinesscomputerSoftwareRDF query language
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The Crane Beach Conjecture

2002

A language L over an alphabet A is said to have a neutral letter if there is a letter e/spl isin/A such that inserting or deleting e's from any word in A* does not change its membership (or non-membership) in L. The presence of a neutral letter affects the definability of a language in first-order logic. It was conjectured that it renders all numerical predicates apart from the order predicate useless, i.e., that if a language L with a neutral letter is not definable in first-order logic with linear order then it is not definable in first-order. Logic with any set /spl Nscr/ of numerical predicates. We investigate this conjecture in detail, showing that it fails already for /spl Nscr/={+, *…

Predicate logicDiscrete mathematicsIterated logarithmConjectureComputational complexity theoryDescription logicComputer Science::Logic in Computer ScienceComputer Science::Software EngineeringBinary numberSigmaPredicate (grammar)MathematicsProceedings 16th Annual IEEE Symposium on Logic in Computer Science
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How to Enrich Description Logics with Fuzziness

2017

International audience; The paper describes the relation between fuzzy and non-fuzzy description logics. It gives an overview about current research in these areas and describes the difference between tasks for description logics and fuzzy logics. The paper also deals with the transformation properties of description logics to fuzzy logics and backwards. While the process of transformation from a description logic to a fuzzy logic is a trivial inclusion, the other way of reducing information from fuzzy logic to description logic is a difficult task, that will be topic of future work.

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]Theoretical computer science[ INFO ] Computer Science [cs]Relation (database)Process (engineering)Computer scienceMathematics::General Mathematics0102 computer and information sciences02 engineering and technology[INFO] Computer Science [cs]01 natural sciencesFuzzy logicTask (project management)[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]Knowledge-based systemsFuzzy Description LogicDescription logicComputer Science::Logic in Computer Science0202 electrical engineering electronic engineering information engineering[INFO]Computer Science [cs][ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI]Semantic WebSemantic WebUncertaintyTransformation (function)TheoryofComputation_MATHEMATICALLOGICANDFORMALLANGUAGES010201 computation theory & mathematics020201 artificial intelligence & image processingComputingMethodologies_GENERALHardware_LOGICDESIGN
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Modeling Changes for SHOIN(D) Ontologies: An Exhaustive Structural Model

2013

Ontology development starts with a rigorous ontological analysis that provides a conceptualization of the domain to model agreed by the community. An ontology, specified in a formal language, approximates the intended models of this conceptualization. It needs then to be revised and refined until an ontological commitment is found. Also ulterior updates, responding to changes in the domain and/or the conceptualization, are expected to occur throughout the ontology life cycle. To handle a consistent application of changes, a couple of ontology evolution methodologies have been proposed. Maintaining the structural consistency is one of the ontology evolution criteria. It implies modeling chan…

Ontology Inference Layer[INFO.INFO-LO] Computer Science [cs]/Logic in Computer Science [cs.LO][INFO.INFO-WB] Computer Science [cs]/WebComputer scienceProcess ontology030303 biophysicsData_MISCELLANEOUS[ INFO.INFO-WB ] Computer Science [cs]/Web02 engineering and technologyOntology (information science)computer.software_genre03 medical and health sciencesOntology chart[INFO.INFO-FL]Computer Science [cs]/Formal Languages and Automata Theory [cs.FL]SHOIN(D) Description LogicOntology components0202 electrical engineering electronic engineering information engineeringUpper ontologyOWL DL[INFO.INFO-FL] Computer Science [cs]/Formal Languages and Automata Theory [cs.FL]0303 health sciencesbusiness.industryOntology-based data integration[INFO.INFO-WB]Computer Science [cs]/WebSuggested Upper Merged Ontology[INFO.INFO-LO]Computer Science [cs]/Logic in Computer Science [cs.LO]Structural ConsistencyOntology EvolutionIEEE[ INFO.INFO-FL ] Computer Science [cs]/Formal Languages and Automata Theory [cs.FL]Ontology Model020201 artificial intelligence & image processing[ INFO.INFO-LO ] Computer Science [cs]/Logic in Computer Science [cs.LO]Artificial intelligenceComputingMethodologies_GENERALChange ModellingbusinesscomputerNatural language processing
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Reasoning with Vague Spatial Information from Upper Mesopotamia (2000BC)

2015

International audience; Concepts such as near, far, south of, etc., are by its own nature vague. However, they are quite common in human language. In the case of historical records, these concepts are often the only source of information regarding the position of ancient places whose exact location has been lost. In our research, we use digitized written records from Upper Mesopotamia (2000BC) from the HIGEOMES project. Our goal is to provide better understanding of the location of places, based on the analysis of spatial statements. In our approach, we analyse cardinal statements between places with known location. Using this information we construct a probabilistic function representing t…

[ INFO.INFO-IR ] Computer Science [cs]/Information Retrieval [cs.IR]media_common.quotation_subjectReasonning02 engineering and technology[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]Description logic0202 electrical engineering electronic engineering information engineeringMesopotamia ;[ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI]Function (engineering)Spatial analysisGeneral Environmental ScienceMathematicsmedia_commondescription logicsInformation retrievalPoint (typography)Ontologybusiness.industryProbabilistic logic[INFO.INFO-LO]Computer Science [cs]/Logic in Computer Science [cs.LO]020207 software engineeringVaguenessspatial uncertainty[INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR]Upper MesopotamiaOntologyGeneral Earth and Planetary Sciences[ INFO.INFO-LO ] Computer Science [cs]/Logic in Computer Science [cs.LO]020201 artificial intelligence & image processingArtificial intelligencebusinessConstruct (philosophy)Procedia Environmental Sciences
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OntoVersionGraph : a change management methodology dedicated to formal ontologies and their user views in a collaborative context

2014

The world changes over time, impacting the knowledge of every subdomain it contains. Therefore systems describing the knowledge of a certain domain should be able to consider changes occurred to keep its knowledge representation up-to-date. Formal ontologies are one of them: they explicitly and formally represent the knowledge of a domain in all its forms and modes of existence. Collaboratively developed, a formal ontology allows the domain users to understand each other by sharing the same terminology despite the different assumptions they have on the domain conceptualization. However, due to its completeness, the complexity of its conceptualization can sometimes make the domain knowledge …

[INFO.INFO-WB] Computer Science [cs]/WebOntology VersioningFormal OntologyGestion du changement dans les ontologiesDescription LogicOntologie formelleOntology EvolutionLogique de descriptionKnowledge ManagementOntoVersionGraphEvolution d'ontologieOntology Change ManagementGestion du changementVersioning d'ontologieWeb sémantique[INFO.INFO-MO] Computer Science [cs]/Modeling and SimulationSemantic WebOWL[INFO.INFO-FL] Computer Science [cs]/Formal Languages and Automata Theory [cs.FL]
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Pellet: A Practical OWL-DL Reasoner

2007

In this paper, we present a brief overview of Pellet: a complete OWL-DL reasoner with acceptable to very good performance, extensive middleware, and a number of unique features. Pellet is the first sound and complete OWL-DL reasoner with extensive support for reasoning with individuals (including nominal support and conjunctive query), user-defined datatypes, and debugging support for ontologies. It implements several extensions to OWL-DL including a combination formalism for OWL-DL ontologies, a non-monotonic operator, and preliminary support for OWL/Rule hybrid reasoning. Pellet is written in Java and is open source.

JavaComputer scienceProgramming languagemedia_common.quotation_subjectWeb Ontology LanguageSemantic reasonercomputer.software_genreOperator (computer programming)Description logicDebuggingMiddleware (distributed applications)Conjunctive querycomputermedia_commoncomputer.programming_languageSSRN Electronic Journal
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Querying the Guarded Fragment with Transitivity

2016

We study the problem of answering a union of Boolean conjunctive queries q against a database Δ, and a logical theory φ which falls in the guarded fragment with transitive guards (GF + TG). We trace the frontier between decidability and undecidability of the problem under consideration. Surprisingly, we show that query answering under GF2 + TG, i.e., the two-variable fragment of GF + TG, is already undecidable (even without equality), whereas its monadic fragment is decidable; in fact, it is 2exptime-complete in combined complexity and coNP-complete in data complexity. We also show that for a restricted class of queries, query answering under GF+TG is decidable. © 2013 Springer-Verlag.

Discrete mathematicsClass (set theory)Transitive relationTrace (linear algebra)0102 computer and information sciences02 engineering and technology16. Peace & justice01 natural sciencesDecidabilityUndecidable problemTheoryofComputation_MATHEMATICALLOGICANDFORMALLANGUAGESDescription logicFragment (logic)010201 computation theory & mathematics0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingConjunctive queryMathematicsAutomata, Languages, and Programming
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The complexity of finite model reasoning in description logics

2005

AbstractWe analyse the complexity of finite model reasoning in the description logic ALCQI, i.e., ALC augmented with qualifying number restrictions, inverse roles, and general TBoxes. It turns out that all relevant reasoning tasks such as concept satisfiability and ABox consistency are ExpTime-complete, regardless of whether the numbers in number restrictions are coded unarily or binarily. Thus, finite model reasoning with ALCQI is not harder than standard reasoning with ALCQI.

Deductive reasoningTheoretical computer scienceFinite satisfiabilityInverseLogic modelFinite satisfiabilitySatisfiabilityAboxDescription logicTheoretical Computer ScienceComputer Science ApplicationsConsistency (database systems)Number restrictionsTBox ALCQI-Konzept Beschreibungslogik EXPTIME-komplettDescription logicComputational Theory and Mathematicsddc:004TBox ALCQI-concept description logic EXPTIME-completeAlgorithmMathematicsInformation SystemsInformation and Computation
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Model Driven Specification of Ontology Translations

2008

The alignment of different ontologies requires the specification, representation and execution of translation rules. The rules need to integrate translations at the lexical, the syntactic and the semantic layer requiring semantic reasoning as well as low-level specification of ad-hoc conversions of data. Existing formalisms for representing translation rules cannot cover the representation needs of these three layers in one model. We propose a metamodel-based representation of ontology alignments that integrate semantic translations using description logics and lower level translation specifications into one model of representation for ontology alignments.

Ontology Inference Layerbusiness.industryProgramming languageComputer scienceOntology-based data integrationProcess ontologySuggested Upper Merged Ontology02 engineering and technologyOntology (information science)computer.software_genreDescription logic020204 information systems0202 electrical engineering electronic engineering information engineeringUpper ontology020201 artificial intelligence & image processingArtificial intelligencebusinesscomputerOntology alignmentNatural language processingLecture Notes in Computer Science Conceptual Modeling - ER 2008
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