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Thursday, April 28, 2011

Programming the Semantic Web






Table of Contents
Foreword . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi
Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii
Part I. Semantic Data
1. Why Semantics? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
Data Integration Across the Web 4
Traditional Data-Modeling Methods 5
Tabular Data 6
7
Relational Data
Evolving and Refactoring Schemas 9
Very Complicated Schemas 11
Getting It Right the First Time 12
Semantic Relationships 14
Metadata Is Data 16
Building for the Unexpected 16
“Perpetual Beta” 17
2. Expressing Meaning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
An Example: Movie Data 21
Building a Simple Triplestore 23
23
Indexes
The add and remove Methods 24
Querying 25
Merging Graphs 26
Adding and Querying Movie Data 28
Other Examples 29
Places 29
Celebrities 31
Business 33
3. Using Semantic Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
A Simple Query Language 37
Variable Binding 38
Implementing a Query Language 40
Feed-Forward Inference 43
Inferring New Triples 43
Geocoding 45
Chains of Rules 47
A Word About “Artificial Intelligence” 50
Searching for Connections 50
Six Degrees of Kevin Bacon 51
Shared Keys and Overlapping Graphs 53
Example: Joining the Business and Places Graphs 53
Querying the Joined Graph 54
Basic Graph Visualization 55
Graphviz 55
Displaying Sets of Triples 56
Displaying Query Results 57
Semantic Data Is Flexible 59
Part II. Standards and Sources
4. Just Enough RDF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
What Is RDF? 63
The RDF Data Model 64
URIs As Strong Keys 64
65
Resources
66
Blank Nodes
Literal Values 68
RDF Serialization Formats 68
A Graph of Friends 69
70
N-Triples
N3 72
RDF/XML 73
RDFa 76
Introducing RDFLib 80
Persistence with RDFLib 83
SPARQL 84
SELECT Query Form 86
OPTIONAL and FILTER Constraints 87
Multiple Graph Patterns 89
CONSTRUCT Query Form 91

ASK and DESCRIBE Query Forms 91
SPARQL Queries in RDFLib 92
Useful Query Modifiers 94
5. Sources of Semantic Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
Friend of a Friend (FOAF) 97
Graph Analysis of a Social Network 101
Linked Data 105
106
The Cloud of Data
Are You Your FOAF file? 107
Consuming Linked Data 110
Freebase 116
An Identity Database 117
RDF Interface 118
Freebase Schema 119
MQL Interface 121
Using the metaweb.py Library 123
Interacting with Humans 125
6. What Do You Mean, “Ontology”? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127
What Is It Good For? 127
A Contract for Meaning 128
Models Are Data 128
129
An Introduction to Data Modeling
Classes and Properties 129
Modeling Films 132
Reifying Relationships 134
Just Enough OWL 135
140
Using Protégé
Creating a New Ontology 140
Editing an Ontology 141
Just a Bit More OWL 145
Functional and Inverse Functional Properties 146
Inverse Properties 146
Disjoint Classes 146
Keepin’ It Real 148
Some Other Ontologies 148
Describing FOAF 148
A Beer Ontology 149
This Is Not My Beautiful Relational Schema! 152
7. Publishing Semantic Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155
Embedding Semantics 155
Microformats 156
RDFa 158
Yahoo! SearchMonkey 160
Google’s Rich Snippets 161
Dealing with Legacy Data 162
Internet Video Archive 162
Tables and Spreadsheets 167
Legacy Relational Data 169
RDFLib to Linked Data 172
Part III. Putting It into Practice
8. Overview of Toolkits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183
Sesame 183
Using the Sesame Java API 184
RDFS Inferencing in Sesame 193
A Servlet Container for the Sesame Server 196
Installing the Sesame Web Application 196
The Workbench 197
Adding Data 199
200
SPARQL Queries
REST API 202
Other RDF Stores 203
Jena (Open Source) 204
Redland (Open Source) 204
Mulgara (Open Source) 204
OpenLink Virtuoso (Commercial and Open Source) 204
Franz AllegroGraph (Commercial) 205
Oracle (Commercial) 205
SIMILE/Exhibit 205
A Simple Exhibit Page 206
Searching, Filtering, and Prettier Views 209
Linking Up to Sesame 211
Timelines 212
9. Introspecting Objects from Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215
RDFObject Examples 215
RDFObject Framework 217
How RDFObject Works 225
10. Tying It All Together . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 227
A Job Listing Application 227
Application Requirements 228
Job Listing Data 228
Converting to RDF 228
Loading the Data into Sesame 231
Serving the Website 232
CherryPy 232
Mako Page Templates 233
A Generic Viewer 234
Getting Data from Sesame 236
The Generic Template 236
Getting Company Data 237
Crunchbase 238
Yahoo! Finance 241
Reconciling Freebase Connections 243
Specialized Views 244
Publishing for Others 248
RDFa 248
RDF/XML 250
Expanding the Data 251
Locations 251
Geography, Economy, Demography 252
Sophisticated Queries 253
Visualizing the Job Data 255
Further Expansion 258
Part IV. Epilogue
11. The Giant Global Graph . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 261
Vision, Hype, and Reality 262
Participating in the Global Graph Community 264
Releasing Data into the Commons 265
License Considerations 266
267
The Data Cycle
Bracing for Continuous Change 268
Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 271


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