Search results for "Ontology-based data integration"

showing 10 items of 30 documents

Aligning Relational Schema and OWL Ontologies with Hidden Markov Model

2016

The problem of bridging the gap between relational schema and ontologies is actively investigated in the Semantic Web and business communities. The main motivations are the OBDA scenario, where a domain ontology allows to hidden the technical details of the db to end-users; and the persistent storage of ontologies in db for facilitating search and retrieval keeping the benefits of DBMSs such as security and integrity. In these cases, the ABox is usually stored into a db, and the TBox is maintained in an ontology; for this reason, schema alignment is a more significant problem than the instance matching one. The use of manual mappings is hard and expensive, especially for large representatio…

Information retrievalComputer scienceOntology-based data integrationProcess ontologySemi-structured modelWeb Ontology Language02 engineering and technologyOntology (information science)computer.software_genreAbox020204 information systemsStar schema0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingData miningcomputerData integrationcomputer.programming_languageInternational Journal of Knowledge Society Research
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A Web Centric Semantic Mediation Approach for Spatial Information Systems

2006

International audience; Semantic mediation is increasingly at the heart of the design of emerging webbased information systems, particularly spatial information systems thatrequire the integration or interoperation of a collection of heterogeneousdata sources. Ontologies are increasingly used to represent agreed semantic ofapplications by providing formal description models and related tools toexplicitly specify the conceptual entities of an application. Past research intraditional database integration has identified key issues in the resolution ofvarious structural and semantic conflicts. Recent research effort has extendedthis early research to address the interoperability of spatial info…

Information retrieval[ INFO ] Computer Science [cs]computer.internet_protocolComputer scienceOntology-based data integrationProcess ontology02 engineering and technology[INFO] Computer Science [cs]Semantic interoperabilityOntology (information science)Language and LinguisticsOWL-SComputer Science ApplicationsHuman-Computer Interaction020204 information systems0202 electrical engineering electronic engineering information engineeringUpper ontology020201 artificial intelligence & image processingSemantic integration[INFO]Computer Science [cs]Semantic Web Stackcomputer
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A Context-Based Enterprise Ontology

2007

The main purpose of an enterprise ontology is to promote the common understanding between people across enterprises, as well as to serve as a communication medium between people and applications, and between different applications. This paper outlines a top-level ontology, called the context-based enterprise ontology, which aims to advance the understanding of the nature, purposes and meanings of things in enterprises with providing basic concepts for conceiving, structuring and representing things within contexts and/or as contexts. The ontology is based on the contextual approach according to which a context involves seven domains: purpose, actor, action, object, facility, location, and t…

Knowledge managementbusiness.industryComputer scienceOntology-based data integrationProcess ontologySuggested Upper Merged OntologyOntology (information science)computer.software_genreOntology engineeringWorld Wide WebOntology chartUpper ontologybusinessOntology alignmentcomputer
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User experience-based information retrieval from semistar data ontologies

2019

The time necessary for the doubling of medical knowledge is rapidly decreasing. In such circumstances, it is of utmost importance for the information retrieval process to be rapid, convenient and straightforward. However, it often lacks at least one of these properties. Several obstacles prohibit domain experts extracting knowledge from their databases without involving the third party in the form of IT professionals. The main limitation is usually the complexity of querying languages and tools. This paper proposes the approach of using a keywords-containing natural language for querying the database and exploiting the system that could automatically translate such queries to already existi…

Medical knowledgeInformation retrievalComputer scienceProcess (engineering)business.industryOntology-based data integration02 engineering and technologyQuery languageData structureDomain (software engineering)User experience design020204 information systems0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingbusinessNatural language
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Semantics driven interaction using natural language in students tutoring

2007

The aim of this work is to introduce a semantic integration between an ontology and a chatbot in an Intelligent Tutoring Systems (ITS) to interact with students using natural language. The interaction process is driven by the use of a purposely defined ontology. In the ontology two types of conceptual relations are defined. Besides the usual relations, which are used to define the domain's structure, another type of relation is used to define the navigation schema inside the ontology according to the need of managing uncertainty. Uncertainty level is related to student knowledge level about the involved concepts. In this work we propose an ITS for the Java programming language called TutorJ…

Ontology Inference LayerComputer sciencecomputer.internet_protocolOntology (information science)Semanticscomputer.software_genreOWL-SIntelligent tutoring systemsLatent semantic analysisNatural language dialogueSemantic driven interactionSemantic navigationSemantic similaritySemantic computingSchema (psychology)Upper ontologySemantic integrationSemantic compressionSettore ING-INF/05 - Sistemi Di Elaborazione Delle Informazionisemantic navigationLatent semantic analysisbusiness.industryOntology-based data integrationKnowledge levelIntelligent Tutoring SystemsOntologylatent semantic analysisArtificial intelligencesemantic driven interactionbusinesscomputernatural language dialogueNatural language processing
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A literature review of sensor ontologies for manufacturing applications

2013

The purpose of this paper is to review existing sensor and sensor network ontologies to understand whether they can be reused as a basis for a manufacturing perception sensor ontology, or if the existing ontologies hold lessons for the development of a new ontology. We develop an initial set of requirements that should apply to a manufacturing perception sensor ontology. These initial requirements are used in reviewing selected existing sensor ontologies. Additionally, we present our developed sensor ontology thus far that incorporates a refined list of requirements. This paper describes 1) extending and refining the requirements; 2) proposing hierarchical structures for verifying the purpo…

Ontology Inference LayerDatabaseComputer sciencebusiness.industrycomputer.internet_protocolOntology-based data integrationProcess ontologySuggested Upper Merged OntologyOntology (information science)computer.software_genreOWL-SUpper ontologySoftware engineeringbusinesscomputerOntology alignment2013 IEEE International Symposium on Robotic and Sensors Environments (ROSE)
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A Survey on Ontology Evaluation Methods

2015

International audience; Ontologies nowadays have become widely used for knowledge representation, and are considered as foundation for Semantic Web. However with their wide spread usage, a question of their evaluation increased even more. This paper addresses the issue of finding an efficient ontology evaluation method by presenting the existing ontology evaluation techniques, while discussing their advantages and drawbacks. The presented ontology evaluation techniques can be grouped into four categories: gold standard-based, corpus-based, task-based and criteria based approaches.

Ontology Inference Layer[ INFO.INFO-IR ] Computer Science [cs]/Information Retrieval [cs.IR][ INFO.INFO-TT ] Computer Science [cs]/Document and Text ProcessingComputer sciencecomputer.internet_protocolProcess ontology[ INFO.INFO-WB ] Computer Science [cs]/Web02 engineering and technologyOntology (information science)OWL-S[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]020204 information systems0202 electrical engineering electronic engineering information engineeringUpper ontology[ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI]EvaluationInformation retrievalOntologyOntology-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][INFO.INFO-TT]Computer Science [cs]/Document and Text Processing[INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR]020201 artificial intelligence & image processing[ INFO.INFO-LO ] Computer Science [cs]/Logic in Computer Science [cs.LO]computerOntology alignmentSemantic web
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Ontology Views for Ontology Change Management

2014

International audience; In the literature, ontology change management systems (OCMS) are direct implementation of the concept of “change management” stated by reference (Klein, 2004). Ontology change management combines ontol- ogy evolution and versioning features to manage ontol- ogy changes and their impacts. Since 2007, many works have combined ontology evolution and versioning into ontology change management systems (OCMS). The evolution subject has been massively studied in these works. They especially addressed the consistence issue for the application of changes on the ontology. These proposals constituted a consequent background for ontology change management but they did not take i…

Ontology Inference Layer[ INFO.INFO-MO ] Computer Science [cs]/Modeling and SimulationComputer scienceProcess ontologyURI[ INFO.INFO-WB ] Computer Science [cs]/Web02 engineering and technologyOntology (information science)computer.software_genreRDFOpen Biomedical Ontologies[INFO.INFO-FL]Computer Science [cs]/Formal Languages and Automata Theory [cs.FL]ontology evolution0202 electrical engineering electronic engineering information engineeringUpper ontologyontologyOWL DLOWLInformation retrievalOntology-based data integration[INFO.INFO-WB]Computer Science [cs]/WebSuggested Upper Merged Ontologymaterialized view020207 software engineering[INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation[ INFO.INFO-FL ] Computer Science [cs]/Formal Languages and Automata Theory [cs.FL]database viewontology mapping020201 artificial intelligence & image processingData miningComputingMethodologies_GENERALontology change managementOntology alignmentcomputer
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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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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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