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

Format:

Book

Contributor:

Jin, Hal.

Series:

Distributed, cluster and grid computing.

Distributed, cluster and grid computing

Language:

English

Subjects (All):

Web databases.

Internet searching.

Semantic Web.

Physical Description:

1 online resource (450 p.)

Edition:

1st ed.

Place of Publication:

Hauppauge, N.Y. : Nova Science Publishers, c2012.

Language Note:

English

Summary:

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.

Contents:

Intro

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

Related Terms

Chapter3ASEMANTICTREEREPRESENTATIONFORDOCUMENTCATEGORIZATIONWITHACOMPOSITEKERNEL

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

Chapter4ONTOLOGYREUSE-ISITFEASIBLE?

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

Chapter 5 COMPUTATIONAL LOGIC AND KNOWLEDGE REPRESENTATION ISSUES IN DATA ANALYSIS FOR THE SEMANTIC WEB

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

Chapter6APPLYINGSEMANTICWEBTECHNOLOGIESTOBIOLOGICALDATAINTEGRATIONANDVISUALIZATION

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

Chapter 7 AN ONTOLOGY AND PEER-TO-PEER BASED DATA AND SERVICE UNIFIED DISCOVERY SYSTEM

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

Chapter8THEDESIGNANDDEVELOPMENTOFASEMANTICENVIRONMENTFORHOLISTICEGOVERNMENTSERVICES

2Analysisoftheproblem&amp

motivation

3RelatedWork

4BusinessModel

5LifeEvents.Theknowledgemodel

6Semanticsupport

6.1Semantics.

6.2Applyingsemantics

7SupportingArchitecture

8LEsinmotion

9Conclusion

10Acknowledgment

Chapter 9 SEMANTIC TOPIC MODELING AND ITS APPLICATION IN BIOINFORMATICS

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

Chapter 10 SUPPORTING A USER IN HIS ANNOTATION AND BROWSING ACTIVITIES IN FOLKSONOMIES

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

8 Conclusions

Chapter 11 DATA MANAGEMENT IN SENSOR NETWORKS USING SEMANTIC WEB TECHNOLOGIES

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

Chapter 12 CHINESE SEMANTIC DEPENDENCY ANALYSIS

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

Chapter 13 CREATING PERSONAL CONTENT MANAGEMENT SYSTEMS USING SEMANTIC WEB TECHNOLOGIES

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

8 Acknowledgments

Chapter 14 A HYBRID DATA LAYER TO UTILIZE OPEN CONTENT FOR HIGHER-LAYERED APPLICATIONS

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.

Notes:

Description based upon print version of record.

Includes bibliographical references and index.

ISBN:

1-61324-760-5

OCLC:

831663026