Data management in the semantic web / / Hal Jin, editor

• Record Nr.: UNINA9910969169603321
• Pubbl/distr/stampa: Hauppauge, N.Y., : Nova Science Publishers, c2012
• ISBN: 1-61324-760-5
• Edizione: [1st ed.]
• Descrizione fisica: 1 online resource (450 p.)
• Collana: Distributed, cluster and grid computing
• Altri autori (Persone): JinHal
• Disciplina: 025.042/7
• Soggetti: Web databases; Internet searching; Semantic Web
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Intro -- DATA MANAGEMENT IN THE SEMANTIC WEB -- DATA MANAGEMENT IN THE SEMANTIC WEB -- CONTENTS -- PREFACE -- Chapter1INTERPRETATIONSOFTHEWEBOFDATA -- Abstract -- 1Introduction -- 2ADistributedKnowledgeBase -- 2.1RDFSchema -- 2.2WebOntologyLanguage -- 2.3Non-AxiomaticLogic -- 2.3.1TheNon-AxiomaticLanguage -- 2.3.2TheNon-AxiomaticReasoner -- 3ADistributedMulti-RelationalNetwork -- 3.1Single-RelationalNetworks -- 3.2Multi-RelationalNetworks -- 3.3Single-RelationalNetworkAlgorithms -- 3.3.1ShortestPath -- 3.3.2Eccentricity,Radius,andDiameter -- 3.3.3ClosenessandBetweennessCentrality -- 3.3.4StationaryProbabilityDistribution -- 3.3.5PageRank -- 3.3.6SpreadingActivation -- 3.3.7AssortativeMixing -- 3.4PortingSingle-RelationalAlgorithmstotheMulti-RelationalDomain -- 3.4.1AMulti-RelationalPathAlgebra -- 3.4.2Multi-RelationalGrammarWalkers -- 4ADistributedObjectRepository -- 4.1PartialObjectRepository -- 4.2FullObjectRepository -- 4.3VirtualMachineRepository -- 5Conclusion -- Acknowledgements -- References -- Chapter 2 TOWARD SEMANTICS-AWARE WEB CRAWLING -- ABSTRACT -- 1 Introduction -- 2 Related Work -- 3 Semantics-Aware Crawler -- 3.1 Identifying Topic-Specific URLs -- 3.2 Building Training Examples -- 3.3 Ordering URLs in the Crawler's Frontier -- 4 Experimental Evaluation -- 4.1 Semantics-Aware Crawling Performance -- 5 Discussion -- 6 ; Conclusion -- References -- Related Terms -- Chapter3ASEMANTICTREEREPRESENTATIONFORDOCUMENTCATEGORIZATIONWITHACOMPOSITEKERNEL -- Abstract -- 1Introduction -- 2Relatedwork -- 3TheUMLSFramework -- 4DocumentModeling -- 5TheSemanticKernel -- 5.1TheMercerkernelframework -- 5.2TheUMLS-basedKernel -- 5.3TheConceptKernel -- 6ExperimentalEvaluation -- 6.1TheSVMClassifier -- 6.2TheMultinomialNaiveBayesClassifier -- 6.3Thecorpus -- 6.4Experimentalsetup -- 6.5Experimentalresults.; 6.6Discussionaboutthe2007CMCMedicalNLPChallenge -- 7Conclusion -- Acknowledgments -- References -- Chapter4ONTOLOGYREUSE-ISITFEASIBLE? -- 1Introduction -- 2OntologyReuse -- 2.1ProcessOverview -- 2.2StateofPractice -- 2.3Methodologies,MethodsandTools -- 3AnEconomicModelforOntologyReuse -- 3.1AnEconomicAnalysisofOntologyReuse -- 3.2TowardsanEconomicModelforOntologyReuse -- 3.2.1TheONTOCOMmodel -- 3.2.2ExtensionsofONTOCOMforReuse -- 3.2.3CalculatingtheRelativeCostsofOntologyReuse -- 3.3ApplicationoftheModel -- 4ConclusionsandOutlook -- References -- Chapter 5 COMPUTATIONAL LOGIC AND KNOWLEDGE REPRESENTATION ISSUES IN DATA ANALYSIS FOR THE SEMANTIC WEB -- ABSTRACT -- 1 Introduction -- 2 Automated Reasoning for Ontology Engineering -- 2.1 Logical databases versus knowledge dynamism -- 3 Poor Representation and Deficient Ontologies -- 3.1 Skolem noise and poor representation -- 3.2 A special case: Semantic Mobile Web 2.0 -- 4 When is an Ontology Robust? -- 4.1 Representational perspective -- 4.2 Computational logic perspective -- 5 Lattice Categorical Theories as Robust Ontologies -- 5.1 Computational viewpoint of ontological Extensions -- 5.2 Representational perspective: Knowledge reconciliation and ontological extensions -- 5.3 Merging robust ontologies -- 5.4 Conservative retractions -- 5.5 Conservative retractions -- 6 Anomalies in Ontologies -- 6.1 Inconsistency: debugging, updating and beyond -- 6.2 Arguments, logic and trust -- 7 FOL as the Universal Provider for Formal Semantics -- 7.1 Model Theory for Semantic Web -- 8 Untrustworthy Information Versus Ontology and Knowledge -- 8.1 Mental attitudes and ontology reasoning -- 8.2 Emergent Ontologies -- 9 Understanding Ontologies: Mereotopology and Entailment-based Visualization -- 10 Meta-logical Trust -- 10.1 Extend OWL to ROWL.; 10.2 Verification of Description Logics -- 11 Final Remarks -- Acknowledgments -- References -- Chapter6APPLYINGSEMANTICWEBTECHNOLOGIESTOBIOLOGICALDATAINTEGRATIONANDVISUALIZATION -- Abstract -- 1Introduction -- 2SemanticWebtechnologies -- 3SemanticWebforthelifesciences -- 3.1Biologicaldataarehugeinvolume -- 3.2Biologicaldatasourcesareheterogeneous -- 3.3Bio-ontologiesdonotfollowstandardsforontologydesign -- 3.4Biologicalknowledgeiscontextdependant -- 3.5Dataprovenanceisofcrucialimportance -- 4BiologicaldataintegrationwithSemanticWebTechnologies -- 4.1Datagathering -- 4.2Dataconversion -- 4.3OntologyofgeneratedRDFdescriptions -- 4.4PrincipeofURIsencoding -- 4.5Unificationofresources -- 4.6Ontologiesmerging -- 4.7Datarepository -- 4.8InformationretrievalwithSPARQL -- 5Datavisualization -- 6Discussion -- 7Conclusion -- References -- Chapter 7 AN ONTOLOGY AND PEER-TO-PEER BASED DATA AND SERVICE UNIFIED DISCOVERY SYSTEM -- ABSTRACT -- 1 Introduction -- 2 Preliminaries -- 2.1 Terms and definitions -- 2.2 Ontological ; data -- 2.2.1 Resource domain ontology -- 2.2.2 Thesaurus ontology -- 2.2.3 Service description ontology -- 2.2.4 QoS ontology -- 2.3 JXTA -- 3. Design of Unified Discovery System -- 4 Combine with JXTA -- 5. Resource registry and discovery -- 5.1. Resource registry process -- 5.2 Resource discovery process -- 5.3 Algorithms -- 5.3.1 Getting group location algorithm -- 5.3.2 Locating resource algorithm -- 5.3.3 Service matching algorithm -- 6 Implementation and Experimental Results -- 7 Related Work -- 8 Conclusion -- References -- Chapter8THEDESIGNANDDEVELOPMENTOFASEMANTICENVIRONMENTFORHOLISTICEGOVERNMENTSERVICES -- Abstract -- 1Introduction -- 2Analysisoftheproblem&amp -- motivation -- 3RelatedWork -- 4BusinessModel -- 5LifeEvents.Theknowledgemodel -- 6Semanticsupport -- 6.1Semantics.; 6.2Applyingsemantics -- 7SupportingArchitecture -- 8LEsinmotion -- 9Conclusion -- 10Acknowledgment -- References -- Chapter 9 SEMANTIC TOPIC MODELING AND ITS APPLICATION IN BIOINFORMATICS -- ABSTRACT -- 1 Introduction -- 2 Latent Dirichlet allocation (LDA) model -- 2.1 Model specification -- 2.2 Statistical learning -- 3 Applications of LDA in biomedical research -- 3.1 Identifying biological related topics -- 3.2 Enhancing text categorization with semantic-enriched representation and training data augmentation -- 3.3 Evaluating the functional coherence of protein groups -- 4 Discussion -- Acknowledgments -- References -- Chapter 10 SUPPORTING A USER IN HIS ANNOTATION AND BROWSING ACTIVITIES IN FOLKSONOMIES -- ABSTRACT -- 1 Introduction -- 2 Preliminaries -- 2.1 Basic Definitions -- 3 Phase 1: Neighborhood Computation -- 3.1 Step 2: Construction of the sets of candidate tags starting fromTSetInput -- 3.2 Step 3: Construction of NeighTSetInput starting from the sets of candidate tags -- 4 Phase 2: Hierarchy Construction -- 4.1 The MST-based algorithm -- 4.2 The Concentric algorithm -- 5 Prototype Description -- 5.1 Class Diagram -- 5.2 Use Case and Sequence Diagrams -- 6 Experiments -- 7 Related Work -- 8 Conclusions -- References -- Chapter 11 DATA MANAGEMENT IN SENSOR NETWORKS USING SEMANTIC WEB TECHNOLOGIES -- ABSTRACT -- 1 Introduction -- 2 Sensor Networks -- 2.1 Sensor Nodes: Functionality and Characteristics -- 2.2 Sensor Networks Topologies -- 2.3 Application Areas -- 2.4 Sensor Web: Data and Services in a Sensor Network -- 3 Knowledge Management in Sensor Networks -- 3.1 Current Approaches -- 3.2 A Unifying Generic Architecture for Sensor Data Management -- 3.2.1 Data Layer -- 3.2.2 Processing Layer -- 3.2.3 Semantic Layer -- 3.2.4 Use Case Scenario.; 4 Conclusions - Open Issues -- References -- Chapter 12 CHINESE SEMANTIC DEPENDENCY ANALYSIS -- ABSTRACT -- 1 Introduction -- 2 Semantic Annotation -- Semantic Role Labeling -- Semantic Dependency Analysis -- 3 Chinese Semantic Dependency Corpus and Tag Set -- 4 Semantic Dependency Relation Classification -- Mutli-Classifier Classification -- Classification Features -- Rule-Based Correction -- 5 Experimental Results -- 6 The SEEN System -- Syntatic Analysis Module -- Headword Assignment Module -- Semantic Dependency Assignment Module -- 7 Conclusion -- References -- Chapter 13 CREATING PERSONAL CONTENT MANAGEMENT SYSTEMS USING SEMANTIC WEB TECHNOLOGIES -- ABSTRACT -- 1 Introduction -- 2 Related Work -- 3 PCMS Metadata Model -- 3.1 Content-independent metadata -- 3.1.1 System Metadata -- 3.1.2 Security Metadata -- 3.1.3 User Metadata -- 3.2 Image-related Metadata -- 4 Semantic PCMS -- 4.1 Interoperability Issues -- 4.2 Semantic ; Metadata Model -- 4.3 Lower layer -- 4.4 Mapping and rules -- 5 Metadata Service -- 5.1 Metadata Management -- 5.2 XML to RDF Conversion -- 6 Use Case Scenario -- 6.1 Solving the Use Case Scenario with the Proposed System -- 6.2 Solving the Use Case Scenario with the Related Work -- 7 Conclusion -- 8 Acknowledgments -- References -- Chapter 14 A HYBRID DATA LAYER TO UTILIZE OPEN CONTENT FOR HIGHER-LAYERED APPLICATIONS -- ABSTRACT -- 1 Introduction -- Resources and Open Content -- Web 3.0 alias Social Semantic Web behind the scenes -- 2 How to Utilize Open Content for Higher-layered Applications? -- 3. State of the Art and Related Work -- 4 qKAI Application Framework -- qKAI system layer -- Data storage and change management -- Discovering Linked Data by setting Points of Interest -- SQL replaces SPARQL -- Hybrid knowledge index -- Change management.; Reusability and extensibility.
• Sommario/riassunto: Effective and efficient data management is vital to today's applications. Traditional data management mainly focuses on information procession involving data within a single organisation. Data are unified according to the same schema and there exists an agreement between the interacting units as to the correct mapping between these concepts. Nowadays, data management systems have to handle a variety of data sources, from proprietary ones to data publicly available. Investigating the relevance between data for information sharing has become an essential challenge for data management. This book explores the technology and application of semantic data management by bringing together various research studies in different subfields.