Wood Linking Enterprise Data

1. Auflage 2010
ISBN: 978-1-4419-7665-9
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 291 Seiten

Enterprise data is growing at a much faster rate than traditional technologies allow. New enterprise architectures combining existing technologies are desperately needed. This book suggests a way forward by applying new techniques of the World Wide Web to enterprise information systems.Linking Enterprise Data is an edited volume contributed by worldwide leaders in Semantic Web and Linked Data research, standards development and adoption. Linking enterprise data is the application of World Wide Web architecture principles to real-world information management issues faced by commercial, not-for-profit and government enterprises. This book is divided into four sections: Benefits of applying Linked Data principles in enterprise settings, enterprise approval and support of Linked Data projects, specific Linked Data techniques and a number of real-world success stories from early enterprise adopters.Linking Enterprise Data targets professionals working as CTOs, CIOs, enterprise architects, project managers and application developers in commercial, not-for-profit and government organizations concerned with scalability, flexibility and robustness of information management systems. Computer science graduate students and researchers focusing on enterprise information integration will also benefit.

Autoren/Hrsg.

Wood, David

Inhaltsverzeichnis

1;Preface;8
2;Contents;12
3;List of Contributors;20
4;Acronyms;24
5;Part I Why Link Enterprise Data?;28
5.1;Semantic Web and the Linked Data Enterprise;30
5.1.1;1 Social Data in the Enterprise;30
5.1.1.1;1.1 Causes;32
5.1.1.2;1.2 Technology Solutions;33
5.1.1.2.1;1.2.1 Data Warehousing;33
5.1.1.2.2;1.2.2 Master Data Management;34
5.1.1.2.3;1.2.3 Metadata Repositories;34
5.1.1.2.4;1.2.4 Controlled Vocabularies;35
5.1.1.2.5;1.2.5 Natural Language Processing;35
5.1.1.3;1.3 Localization and Globalization;36
5.1.2;2 The Linked Data Enterprise;37
5.1.2.1;2.1 Controlled Vocabularies;38
5.1.2.1.1;2.1.1 Tagging-style vocabularies;39
5.1.2.1.2;2.1.2 Schema-style vocabularies;41
5.1.2.1.3;2.1.3 Linked Enterprise Vocabularies;43
5.1.2.2;2.2 Prerequisites for Linked Data Vocabularies;45
5.1.3;3 Examples;47
5.1.3.1;3.1 Publishing;47
5.1.3.2;3.2 Government;48
5.1.4;4 Conclusions;49
5.1.5;References;49
5.2;The Role of Community-Driven Data Curation for Enterprises;52
5.2.1;1 Introduction;52
5.2.2;2 The Business Need for Curated Data;53
5.2.3;3 Data Curation;55
5.2.3.1;3.1 How to Curate Data;55
5.2.3.1.1;3.1.1 Setting up a Curation Process;56
5.2.4;4 Community-based Curated Enterprise Data;57
5.2.4.1;4.1 Internal Corporate Community;57
5.2.4.2;4.2 External Pre-competitive Communities;58
5.2.5;5 Case Study: Wikipedia - The World Largest Open Digital Curation Community;59
5.2.5.1;5.1 Social Organization;60
5.2.5.2;5.2 Artifacts, Tools and Processes;61
5.2.5.3;5.3 DBPedia - Community Curated Linked Open Data;62
5.2.6;6 Case Study: The New York Times - 100 Years of Expert Data Curation;63
5.2.6.1;6.1 Data Curation;63
5.2.6.2;6.2 Publishing Curated Linked Data;64
5.2.7;7 Case Study: Thomson Reuters - Data Curation, a Core Business Competency;65
5.2.7.1;7.1 Data Curation;66
5.2.8;8 Case Study: ChemSpider - Open Data Curation in the Global Chemistry Community;67
5.2.8.1;8.1 Community Objectives;68
5.2.8.2;8.2 Curation Approach & Types;68
5.2.9;9 Case Study: Protein Data Bank, Pre-competitive Bioinformatics;69
5.2.9.1;9.1 Serving the Community;69
5.2.9.2;9.2 Curation Approaches & Types;69
5.2.9.3;9.3 Observations;70
5.2.10;10 Case Study Learnings;71
5.2.10.1;10.1 Social Best Practices;71
5.2.10.2;10.2 Technical Best Practices;72
5.2.11;11 Conclusion;73
5.2.12;References;73
6;Part II Approval and Support of Linked Data Projects;76
6.1;Preparing for a Linked Data Enterprise;78
6.1.1;1 Introduction;79
6.1.2;2 The Cost of Linked Data;79
6.1.2.1;2.1 The Cost of Services and Support;80
6.1.2.2;2.2 Education and Training;80
6.1.2.3;2.3 Infrastructure;81
6.1.3;3 Is your Organization Ready for Linked Data?;81
6.1.4;4 The Linked Data Initiative;84
6.1.5;5 A Decentralized Approach to Data Management;85
6.1.6;6 Being On theWeb vs. In the Web;86
6.1.7;7 Leverage Vocabularies;87
6.1.8;8 A Simple Approach to Linked Data;88
6.1.9;9 Conclusions;89
6.1.9.1;9.1 Prepare for a Linked Data Enterprise;89
6.1.10;References;90
6.2;Selling and Building Linked Data: Drive Value and Gain Momentum;92
6.2.1;1 The Data Burden;93
6.2.2;2 Driving Value Principles;94
6.2.3;3 Building a Team;97
6.2.4;4 Committing to Something Bigger;99
6.2.5;5 Putting it together;100
6.2.6;6 Conclusions;101
6.2.7;References;103
7;Part III Techniques for Linking Enterprise Data;104
7.1;Enhancing Enterprise 2.0 Ecosystems Using Semantic Web and Linked Data Technologies:The Sem SLATES Approach;106
7.1.1;1 Introduction;107
7.1.2;2 Issues with Current Enterprise 2.0 Ecosystems;108
7.1.2.1;2.1 Information Fragmentation and Heterogeneity of Data Formats;109
7.1.2.2;2.2 Knowledge Capture and Re-use;110
7.1.2.3;2.3 Tagging and Information Retrieval;110
7.1.3;3 SemSLATES: A Social and Semantic Middleware Approach for Enterprise 2.0;111
7.1.3.1;3.1 The SemSLATES Architecture;112
7.1.3.2;3.2 Ontologies for Enterprise 2.0;115
7.1.3.3;3.3 Generating Semantic Annotations Through Software Add-ons;116
7.1.3.4;3.4 Deploying Additional Services;117
7.1.4;4 Case-study: Enabling SemSLATES at EDF R&D;118
7.1.4.1;4.1 Background;118
7.1.4.2;4.2 Extending Popular Ontologies;119
7.1.4.3;4.3 Automated SIOC-based Annotations;120
7.1.4.4;4.4 Knowledge Capture Using UfoWiki;120
7.1.4.5;4.5 Semantic Tagging Add-ons;122
7.1.4.6;4.6 Additional Features of the Platform;123
7.1.4.6.1;4.6.1 Enhancing the Wiki Features;123
7.1.4.6.2;4.6.2 Semantic Search;124
7.1.4.6.3;4.6.3 Semantic Mashups;125
7.1.5;5 Conclusion;126
7.1.6;References;127
7.2;Linking XBRL Financial Data;130
7.2.1;1 Introduction;130
7.2.1.1;1.1 XBRL;133
7.2.1.1.1;1.1.1 Instances;133
7.2.1.1.2;1.1.2 Taxonomies;134
7.2.1.2;1.2 Related Work;135
7.2.2;2 Approach;136
7.2.2.1;2.1 XSD2OWL Mapping;137
7.2.2.2;2.2 XML2RDF Mapping;139
7.2.2.3;2.3 Algorithm;140
7.2.3;3 Results;140
7.2.3.1;3.1 Links to External Data;142
7.2.3.2;3.2 Semantic Integration;145
7.2.4;4 Evaluation;146
7.2.4.1;4.1 Use Case;149
7.2.5;5 Conclusions and Future Work;149
7.2.6;References;151
7.3;Scalable Reasoning Techniques for Semantic Enterprise Data;154
7.3.1;1 Introduction;154
7.3.2;2 Survey of Reasoning Techniques;155
7.3.2.1;2.1 Traditional Rule Engines;157
7.3.2.2;2.2 Forward Chaining and the RETE algorithm;158
7.3.2.3;2.3 Backward Chaining;159
7.3.3;3 Bayesian Networks;160
7.3.3.1;3.1 Representing Probabilities within the Ontological Model;162
7.3.4;4 Unsupervised Reasoning;163
7.3.5;5 Semantic Reasoning;164
7.3.5.1;5.1 Performance and Reasoning;166
7.3.5.2;5.2 Applying Best-First Search (A* Search) to Semantic Reasoning;167
7.3.5.3;5.3 High-level View of Distributed Reasoning;167
7.3.5.4;5.4 Map-Reduce and Similar Techniques;168
7.3.5.5;5.5 Performance and Ontology Engineering;170
7.3.6;6 Semantic Reasoning vs. Business Intelligence;170
7.3.7;7 Best Practices for Application Developers and System Integrators;171
7.3.8;8 Summary;173
7.3.9;References;173
7.4;Reliable and Persistent Identification of Linked Data Elements;176
7.4.1;1 Introduction;177
7.4.2;2 Metadata Before the World Wide Web;177
7.4.3;3 Metadata on the World Wide Web;181
7.4.4;4 Persistent URLs;186
7.4.5;5 Extending Persistent URLs for Web Resource Curation;187
7.4.6;6 Redirection of URL Fragments;191
7.4.7;7 Using Persistent URLs and Retrieved Metadata;191
7.4.8;8 Federations of PURL Servers;193
7.4.9;9 Conclusions and Further Work;197
7.4.10;References;198
8;Part IV Success Stories;202
8.1;Linked Data for Fighting Global Hunger: Experiences in setting standards for Agricultural Information Management;204
8.1.1;1 Agricultural information and Semantic Web;204
8.1.1.1;Promoting trusted URIs for use in Linked Data;206
8.1.2;2 Integrating access using Dublin Core metadata;207
8.1.2.1;Feedback from application profile users;209
8.1.2.2;Metadata enrichment and conversion to Linked Data;211
8.1.2.3;Accepting “whatever you can get”;212
8.1.3;3 AGROVOC and specialized domain ontologies;213
8.1.3.1;User experience of AGROVOC and AIMS ontologies;217
8.1.3.2;AGROVOC as a “quarry” of terms;218
8.1.3.3;Correcting the model for less precision;220
8.1.4;4 Networking, capacity development, and outreach;224
8.1.4.1;Fishing in a Sea of Agrovoc?;225
8.1.4.2;The global “coherence” of information about food;227
8.1.5;References;228
8.2;Enterprise Linked Data as Core Business Infrastructure;230
8.2.1;1 Introduction;230
8.2.2;2 Motivations;231
8.2.3;3 Garlik’s System Architectures;233
8.2.3.1;3.1 DataPatrol;234
8.2.3.2;3.2 QDOS;238
8.2.3.2.1;3.2.1 foaf.qdos.com;240
8.2.4;4 Schema Driven Software Deployment;242
8.2.5;5 Technology and the Need to Scale;243
8.2.5.1;5.1 4store;243
8.2.5.2;5.2 5store;244
8.2.5.2.1;5.2.1 Platform;244
8.2.5.2.2;5.2.2 Performance;245
8.2.6;6 Conclusions;245
8.2.7;7 Future Work;246
8.2.8;References;246
8.3;Standardizing Legal Content with OWL and RDF;248
8.3.1;1 Introduction;248
8.3.1.1;1.1 The problem domain;248
8.3.1.2;1.2 Application of Semantic Web technologies;249
8.3.2;2 Toward a Common Legal Content Format;250
8.3.3;3 OWL Ontology;251
8.3.3.1;3.1 Creating the ontology;251
8.3.3.1.1;3.1.1 Metadata properties;251
8.3.3.1.2;3.1.2 Document model;252
8.3.3.1.3;3.1.3 Knowledge structures;253
8.3.3.2;3.2 Domain Ontology Mapping;253
8.3.4;4 Content Architecture;254
8.3.4.1;4.1 Modularized XHTML + RDFa for Textual Content;254
8.3.4.2;4.2 RDF for Metadata, Relations and Classifications;255
8.3.5;5 Working with RDF in a Content Supply Chain;256
8.3.5.1;5.1 The Open World Enigma;257
8.3.5.2;5.2 Ensuring RDF Data Integrity;257
8.3.5.3;5.3 Managing Fragmented Ontologies;259
8.3.5.4;5.4 Managing Performance;259
8.3.5.5;5.5 Using RDF with XSLT;260
8.3.6;6 Enabling Large-Scale Triple Production;261
8.3.6.1;6.1 Experimental XSD generation;262
8.3.6.2;6.2 RDFBeans;263
8.3.7;7 Conclusions;265
8.3.8;References;266
8.4;A Role for Semantic Web Technologies in Patient Record Data Collection;268
8.4.1;1 Introduction;269
8.4.2;2 Architectural Styles;270
8.4.2.1;2.1 REST Architectural Style;270
8.4.2.2;2.2 Service Oriented Architecture;271
8.4.2.2.1;2.2.1 Workflow Systems;272
8.4.2.2.2;2.2.2 Process Representation;272
8.4.3;3 Semantic Web Technologies;273
8.4.4;4 SemanticDB Concurrent Data CollectionWorkflow;274
8.4.4.1;4.1 Requirements;275
8.4.4.2;4.2 XML and RDF Content Management;276
8.4.4.3;4.3 RESTful XSLT Services;277
8.4.4.4;4.4 Declarative AJAX Framework;277
8.4.4.5;4.5 Implementation;278
8.4.5;5 General Architectural Observations;284
8.4.6;6 Review of Service-oriented Metrics;284
8.4.7;7 Conclusions;287
8.4.8;References;287
8.5;Use of Semantic Web technologies on the BBC Web Sites;290
8.5.1;1 Introduction;290
8.5.1.1;1.1 Linking microsites for cross-domain navigation;291
8.5.1.2;1.2 Making data available to developers;291
8.5.1.3;1.3 Making use of the wider Web;292
8.5.2;2 Programme support on theWeb;292
8.5.2.1;2.1 BBC Programmes;293
8.5.2.2;2.2 The Programmes Ontology;293
8.5.2.2.1;2.2.1 Main terms;293
8.5.2.2.2;2.2.2 Tagging programmes;294
8.5.2.2.3;2.2.3 A flexible segmentation model;295
8.5.2.3;2.3 Web identifiers for broadcast radio and television sites;296
8.5.3;3 BBC Music;297
8.5.3.1;3.1 BBC Music as Linked Data;298
8.5.3.2;3.2 Web identifiers for BBC Music;298
8.5.3.3;3.3 The Web as a content management system;299
8.5.3.4;3.4 Using the BBC Programmes and the BBC Music Linked Data;299
8.5.3.4.1;3.4.1 Programmes and locations;299
8.5.3.4.2;3.4.2 Artist recommendations;299
8.5.4;4 BBC Wildlife Finder;301
8.5.4.1;4.1 The Wildlife Ontology;301
8.5.4.1.1;4.1.1 Main terms;302
8.5.4.1.2;4.1.2 Species as Classes vs Species as Instances;302
8.5.4.1.3;4.1.3 Web identifiers - using DBpedia as a controlled vocabulary;303
8.5.4.2;4.2 Web identifiers;303
8.5.4.3;4.3 The Web as a Content Management System;305
8.5.4.4;4.4 The importance of curation;305
8.5.5;5 Journalism;306
8.5.5.1;5.1 Populating and using the ontology;307
8.5.5.2;5.2 Future developments;308
8.5.6;6 Conclusion;309
8.5.7;References;310
9;Glossary;312
10;Index;316