A Developer’s Guide to the Semantic Web

Description

The Semantic Web represents a vision for how to make the huge amount of information on the Web automatically processable by machines on a large scale. For this purpose, a whole suite of standards, technologies and related tools have been specified and developed over the last couple of years and they have now become the foundation for numerous new applications. A Developer’s Guide to the Semantic Web helps the reader to learn the core standards, key components and underlying concepts. It provides in-depth coverage of both the what-is and how-to aspects of the Semantic Web. From Yu’s presentation, the reader will obtain not only a solid understanding about the Semantic Web, but also learn how to combine all the pieces to build new applications on the Semantic Web. The second edition of this book not only adds detailed coverage of the latest W3C standards such as SPARQL 1.1 and RDB2RDF, it also updates the readers by following recent developments. More specifically, it includes five new chapters on schema.org and semantic markup, on Semantic Web technologies used in social networks and on new applications and projects such as data.gov and Wikidata and it also provides a complete coding example of building a search engine that supports Rich Snippets. Software developers in industry and students specializing in Web development or Semantic Web technologies will find in this book the most complete guide to this exciting field available today. Based on the step-by-step presentation of real-world projects, where the technologies and standards are applied, they will acquire the knowledge needed to design and implement state-of-the-art applications. Preface to the First Edition Objectives of the Book Intended Readers Structure of the Book Where to Get the Code Acknowledgment Preface to the Second Edition Contents Part I: Core of the Semantic Web Chapter 1: A Web of Data: Toward the Idea of the Semantic Web 1.1 A Motivating Example: Data Integration on the Web 1.1.1 A Smart Data Integration Agent 1.1.2 Is Smart Data Integration Agent Possible? 1.1.3 The Idea of the Semantic Web 1.2 A More General Goal: A Web Understandable to Machines 1.2.1 How Do We Use the Web? 1.2.1.1 Searching 1.2.1.2 Information Integration 1.2.1.3 Web Data Mining 1.2.2 What Stops Us From Doing More? 1.2.3 Again, the Idea of the Semantic Web 1.3 The Semantic Web: A First Look 1.3.1 The Concept of the Semantic Web 1.3.2 The Semantic Web, Linked Data and the Web of Data 1.3.3 Some Basic Things About the Semantic Web Reference Chapter 2: The Building Block for the Semantic Web: RDF 2.1 RDF Overview 2.1.1 RDF In Official Language 2.1.2 RDF in Plain English 2.2 The Abstract Model of RDF 2.2.1 The Big Picture 2.2.2 Statement 2.2.3 Resource and Its URI Name 2.2.4 Predicate and Its URI Name 2.2.5 RDF Triples: Knowledge That Machines Can Use 2.2.6 RDF Literals and Blank Node 2.2.6.1 Basic Terminologies So Far 2.2.6.2 Literal Values 2.2.6.3 Blank Nodes 2.2.7 A Summary So Far 2.3 RDF Serialization: RDF/XML Syntax 2.3.1 The Big Picture: RDF Vocabulary 2.3.2 Basic Syntax and Examples 2.3.2.1 rdf:RDF, rdf:Description, rdf:about and rdf:resource 2.3.2.2 rdf:type and Typed Nodes 2.3.2.3 Using Resource as Property Value 2.3.2.4 Using Untyped Literals as Property Values, rdf:value and rdf:parseType 2.3.2.5 Using Typed Literal Values and rdf:datatype 2.3.2.6 rdf:nodeID and More About Anonymous Resources 2.3.2.7 rdf:ID, xml:base and RDF/XML Abbreviation 2.3.3 Other RDF Capabilities and Examples 2.3.3.1 RDF Containers: rdf:Bag, rdf:Seq, rdf:Alt and rdf:li 2.3.3.2 RDF Collections: rdf:first, rdf:rest, rdf:nil and rdf:List 2.3.3.3 RDF Reification: rdf:statement, rdf:subject, rdf:predicate and rdf:object 2.4 Other RDF Sterilization Formats 2.4.1 Notation-3, Turtle and N-Triples 2.4.2 Turtle Language 2.4.2.1 Basic Language Features 2.4.2.2 Abbreviations and Shortcuts: Namespace Prefix, Default Prefix and @base 2.4.2.3 Abbreviations and Shortcuts: Token a, Comma and Semicolons 2.4.2.4 Turtle Blank Nodes 2.5 Fundamental Rules of RDF 2.5.1 Information that Is Understandable by Machines 2.5.2 Distributed Information Aggregation 2.5.3 A Hypothetical Real World Example 2.6 More About RDF 2.6.1 Dublin Core: Example of Predefined RDF Vocabulary 2.6.2 XML vs. RDF? 2.6.3 Use a RDF Validator 2.7 Summary Chapter 3: Other RDF-Related Technologies: Microformats, RDFa and GRDDL 3.1 Introduction: Why Do We Need These? 3.2 Microformats 3.2.1 Microformats: The Big Picture 3.2.2 Microformats: Syntax and Examples 3.2.2.1 From vCard to hCard Microformat 3.2.2.2 Using hCard Microformat to Markup Page Content 3.2.3 Microformats and RDF 3.2.3.1 What´s So Good About Microformats? 3.2.3.2 Microformats and RDF 3.3 RDFa 3.3.1 RDFa: The Big Picture 3.3.2 RDFa Attributes and RDFa Elements 3.3.3 RDFa: Rules and Examples 3.3.3.1 RDFa Rules 3.3.3.2 RDFa Examples 3.3.4 RDFa and RDF 3.3.4.1 What´s So Good About RDFa? 3.3.4.2 RDFa and RDF 3.4 GRDDL 3.4.1 GRDDL: The Big Picture 3.4.2 Using GRDDL with Microformats 3.4.3 Using GRDDL with RDFa 3.5 Summary Chapter 4: RDFS and Ontology 4.1 RDFS Overview 4.1.1 RDFS in Plain English 4.1.2 RDFS in Official Language 4.2 RDFS+RDF: One More Step Toward Machine-Readable 4.2.1 A Common Language to Share 4.2.2 Machine Inferencing Based on RDFS 4.3 RDFS Core Elements 4.3.1 The Big Picture: RDFS Vocabulary 4.3.2 Basic Syntax and Examples 4.3.2.1 Defining Classes 4.3.2.2 Defining Properties 4.3.2.3 More About Properties 4.3.2.4 RDFS Data Types 4.3.2.5 RDFS Utility Vocabulary 4.3.3 Summary So Far 4.3.3.1 Our Camera Vocabulary 4.3.3.2 Where Is the Knowledge? 4.4 The Concept of Ontology 4.4.1 What Is Ontology 4.4.2 The Benefits of Ontology 4.5 Building the Bridge to Ontology: SKOS 4.5.1 Knowledge Organization Systems (KOS) 4.5.2 Thesauri vs. Ontologies 4.5.3 Filling the Gap: SKOS 4.5.3.1 What Is SKOS? 4.5.3.2 SKOS Core Constructs 4.5.3.3 Interlinking Concepts by Using SKOS 4.6 Another Look at Inferencing Based on RDF Schema 4.6.1 RDFS Ontology Based Reasoning: Simple, Yet Powerful 4.6.2 Good, Better and Best: More Is Needed 4.7 Summary Chapter 5: OWL: Web Ontology Language 5.1 OWL Overview 5.1.1 OWL in Plain English 5.1.2 OWL in Official Language: OWL 1 and OWL 2 5.1.3 From OWL 1 to OWL 2 5.2 OWL 1 and OWL 2: The Big Picture 5.2.1 Basic Notions: Axiom, Entity, Expression and IRI Names 5.2.2 Basic Syntax Forms: Functional-Style, RDF/XML Syntax, Manchester Syntax and XML Syntax 5.3 OWL 1 Web Ontology Language 5.3.1 Defining Classes: The Basics 5.3.2 Defining Classes: Localizing Global Properties 5.3.2.1 Value Constraints: owl:allValuesFrom 5.3.2.2 Enhanced Reasoning Power 1 5.3.2.3 Value Constraints: owl:someValuesFrom 5.3.2.4 Enhanced Reasoning Power 2 5.3.2.5 Value Constraints: owl:hasValue 5.3.2.6 Enhanced Reasoning Power 3 5.3.2.7 Cardinality Constraints: owl:cardinality, owl:min(max)Cardinality 5.3.2.8 Enhanced Reasoning Power 4 5.3.3 Defining Classes: Using Set Operators 5.3.3.1 Set Operators 5.3.3.2 Enhanced Reasoning Power 5 5.3.4 Defining Classes: Using Enumeration, Equivalent and Disjoint 5.3.4.1 Enumeration, Equivalent and Disjoint 5.3.4.2 Enhanced Reasoning Power 6 5.3.5 Our Camera Ontology So Far 5.3.6 Define Properties: The Basics 5.3.7 Defining Properties: Property Characteristics 5.3.7.1 Symmetric Properties 5.3.7.2 Enhanced Reasoning Power 7 5.3.7.3 Transitive Properties 5.3.7.4 Enhanced Reasoning Power 8 5.3.7.5 Functional Properties 5.3.7.6 Enhanced Reasoning Power 9 5.3.7.7 Inverse Property 5.3.7.8 Enhanced Reasoning Power 10 5.3.7.9 Inverse Functional Property 5.3.7.10 Enhanced Reasoning Power 11 5.3.8 Camera Ontology Written Using OWL 1 5.4 OWL 2 Web Ontology Language 5.4.1 What Is New in OWL 2 5.4.2 New Constructs for Common Patterns 5.4.2.1 Common Pattern: Disjointness 5.4.2.2 Common Pattern: Negative Assertions 5.4.3 Improved Expressiveness for Properties 5.4.3.1 Property Self Restriction 5.4.3.2 Property Self Restriction: Enhanced Reasoning Power 12 5.4.3.3 Property Cardinality Restrictions 5.4.3.4 Property Cardinality Restrictions: Enhanced Reasoning Power 13 5.4.3.5 More About Property Characteristics: Reflexive, Irreflexive and Asymmetric Properties 5.4.3.6 More About Property Characteristics: Enhanced Reasoning Power 14 5.4.3.7 Disjoint Properties 5.4.3.8 Disjoint Properties: Enhanced Reasoning Power 15 5.4.3.9 Property Chains 5.4.3.10 Property Chains: Enhanced Reasoning Power 16 5.4.3.11 Keys 5.4.3.12 Keys: Enhanced Reasoning Power 17 5.4.4 Extended Support for Datatypes 5.4.4.1 Wider Range of Supported Datatypes and Extra Built-in Datatypes 5.4.4.2 Restrictions on Datatypes and User-Defined Datatypes 5.4.4.3 Data Range Combinations 5.4.5 Punning and Annotations 5.4.5.1 Understanding Punning 5.4.5.2 OWL Annotations, Axioms About Annotation Properties 5.4.6 Other OWL 2 Features 5.4.6.1 Entity Declarations 5.4.6.2 Top and Bottom Properties 5.4.6.3 Imports and Versioning 5.4.7 OWL Constructs in Instance Documents 5.4.8 OWL 2 Profiles 5.4.8.1 Why Do We Need All These? 5.4.8.2 Assigning Semantics to OWL Ontology: Description Logic vs. RDF-Based Semantics 5.4.8.3 Three Faces of OWL 1 5.4.8.4 Understanding OWL 2 Profiles 5.4.8.5 OWL 2 EL, QL and RL 5.4.9 Our Camera Ontology in OWL 2 5.5 Summary Chapter 6: SPARQL: Querying the Semantic Web 6.1 SPARQL Overview 6.1.1 SPARQL in Official Language 6.1.2 SPARQL in Plain Language 6.1.3 RDF Datasets and SPARQL Endpoints 6.2 SPARQL 1.0 Query Language 6.2.1 The Big Picture 6.2.1.1 Triple Pattern 6.2.1.2 Graph Pattern 6.2.2 SELECT Query 6.2.2.1 Structure of a SELECT Query 6.2.2.2 Writing Basic SELECT Query 6.2.2.3 Using OPTIONAL Keyword for Matches 6.2.2.4 Using Solution Modifier 6.2.2.5 Using FILTER Keyword to Add Value Constraints 6.2.2.6 Using Union Keyword for Alternative Match 6.2.2.7 Working with Multiple Graphs 6.2.3 CONSTRUCT Query 6.2.4 DESCRIBE Query 6.2.5 ASK Query 6.2.6 What Is Missing from SPARQL 1.0? 6.3 SPARQL 1.1 Query Language 6.3.1 Introduction: What Is New? 6.3.2 SPARQL 1.1 Query 6.3.2.1 Aggregates 6.3.2.2 Subqueries 6.3.2.3 Negation 6.3.2.4 Property Paths 6.3.2.5 Assignment 6.3.3 SPARQL 1.1 Federated Query 6.3.3.1 Simple Query to a Remote SPARQL Endpoint 6.3.3.2 Federated Queries with Multiple SPARQL Endpoints 6.3.4 SPARQL 1.1 Update 6.3.4.1 Graph Update: INSERT DATA Operation 6.3.4.2 Graph Update: DELETE DATA Operation 6.3.4.3 Graph Update: DELETE/INSERT Operation Based on Binding Patterns 6.3.4.4 Graph Update: LOAD Operation 6.3.4.5 Graph Update: CLEAR Operation 6.3.4.6 Graph Management: CREATE Operation 6.3.4.7 Graph Management: DROP Operation 6.3.4.8 Graph Management: COPY Operation 6.3.4.9 Graph Management: MOVE Operation 6.3.4.10 Graph Management: ADD Operation 6.3.5 Other SPARQL 1.1 Features 6.3.5.1 Examples of String Functions 6.3.5.2 Examples of Numeric Functions 6.3.5.3 Examples of Date/Time and Related Functions 6.3.5.4 Examples of Hash Functions 6.3.5.5 Other New Functions and Operators 6.4 Summary Part II: Applied Semantic Web Chapter 7: FOAF: Friend of a Friend 7.1 What FOAF Is and What It Does 7.1.1 FOAF in Plain English 7.1.2 FOAF in Official Language 7.2 Core FOAF Vocabulary and Examples 7.2.1 The Big Picture: FOAF Vocabulary 7.2.2 Core Terms and Examples 7.3 Create Your FOAF Document and Get into the Friend Circle 7.3.1 How Does the Circle Work? 7.3.2 Create Your FOAF Document 7.3.3 Get into the Circle: Publish Your FOAF Document 7.3.4 From Web Pages for Human Eyes to Web Pages for Machines 7.4 Semantic Markup: A Connection Between the Two Worlds 7.4.1 What Is Semantic Markup? 7.4.2 Semantic Markup: Procedure and Example 7.4.3 Semantic Markup: Feasibility and Different Approaches 7.5 Summary Chapter 8: DBpedia 8.1 Introduction to DBpedia 8.1.1 From Manual Markup to Automatic Generation of Annotation 8.1.2 From Wikipedia to DBpedia 8.1.3 The Look-and-Feel of DBpedia: Page Redirect 8.2 Semantics in DBpedia 8.2.1 Infobox Template 8.2.2 Creating DBpedia Ontology 8.2.2.1 The Need for Ontology 8.2.2.2 Mapping Infobox Templates to Classes 8.2.2.3 Mapping Infobox Template Attributes to Properties 8.2.3 Infobox Extraction Methods 8.2.3.1 Generic Infobox Extraction Method 8.2.3.2 Mapping-Based Infobox Extraction Method 8.3 Accessing DBpedia Dataset 8.3.1 Using SPARQL to Query DBpedia 8.3.1.1 SPARQL Endpoints for DBpedia 8.3.1.2 Examples of Using SPARQL to Access DBpedia 8.3.2 Direct Download of DBpedia Datasets 8.3.2.1 The Wikipedia Datasets 8.3.2.2 DBpedia Core Datasets 8.3.2.3 Extended Datasets 8.3.3 Access DBpedia as Linked Data 8.4 Summary Reference Chapter 9: Linked Open Data 9.1 The Concept of Linked Data and Its Basic Rules 9.1.1 The Concept of Linked Data 9.1.2 How Big Are the Web of Linked Data and the LOD Project? 9.1.3 The Basic Rules of Linked Data 9.2 Publishing RDF Data on the Web 9.2.1 Identifying Things with URIs 9.2.1.1 Web Document, Information Resource and URI 9.2.1.2 Non-information Resources and Their URIs 9.2.1.3 URIs for Non-information Resources: 303 URIs and Content Negotiation 9.2.1.4 URIs for Non-information Resources: Hash URIs 9.2.1.5 URIs for Non-information Resources: 303 URIs vs. Hash URIs 9.2.1.6 URI Aliases 9.2.2 Choosing Vocabularies for RDF Data 9.2.3 Creating Links to Other RDF Data 9.2.3.1 Basic Language Constructs to Create Links 9.2.3.2 Creating Links Manually 9.2.3.3 Creating Links Automatically 9.2.4 Serving Information as Linked Data 9.2.4.1 Minimum Requirements for Being Linked Open Data 9.2.4.2 Example: Publishing Linked Data on the Web 9.2.4.3 Make Sure You Have Done It Right 9.3 The Consumption of Linked Data 9.3.1 Discover Specific Targets on the Linked Data Web 9.3.1.1 Semantic Web Search Engine for Human Eyes 9.3.1.2 Semantic Web Search Engine for Applications 9.3.2 Accessing the Web of Linked Data 9.3.2.1 Using a Linked Data Browser 9.3.2.2 Using SPARQL Endpoints 9.3.2.3 Accessing the Linked Data Web Programmatically 9.4 Linked Data Application 9.4.1 Linked Data Application Example: Revyu 9.4.1.1 Revyu: An Overview 9.4.1.2 Revyu: Why It Is Different 9.4.2 Web 2.0 Mashups vs. Linked Data Mashups 9.5 Summary Chapter 10: schema.org and Semantic Markup 10.1 Introduction to schema.org 10.1.1 What Is schema.org? 10.1.2 Understanding the schema.org Vocabulary 10.2 Content Markup Using schema.org 10.2.1 RDFa 1.1 Lite: A Simple Subset of RDFa 10.2.2 What Markup Format to Use? 10.2.3 Type Checking and Other Issues 10.2.4 Validating Your Markup 10.3 Content Markup Example 1: Google Rich Snippets 10.3.1 What Is Rich Snippets: An Example 10.3.2 Google Rich Snippets: Semantic Markup Using schema.org 10.3.2.1 The Basic Flow of Rich Snippets 10.3.2.2 Markup for Rich Snippets: Basic Steps 10.3.2.3 Markup for Rich Snippets: Examples by RDFa 10.3.3 Using Google Rich Snippets Testing Tool 10.4 Content Markup Example 2: LRMI Project 10.4.1 The Idea of LRMI 10.4.2 LRMI Specification 10.4.3 LRMI Implementation Examples 10.4.3.1 LRMI Markup Example 10.4.3.2 Customized Searching and Filtering Based on LRMI Markup 10.5 Summary References Chapter 11: Social Networks and the Semantic Web 11.1 Overview of Social Networking Websites 11.2 Facebook´s Open Graph Protocol 11.2.1 Open Graph Protocol 11.2.2 How Does It Work: Creating Typed Links Using OGP 11.2.2.1 The Basic Idea and Process 11.2.2.2 Open Graph Markup Examples 11.2.2.3 Open Graph Issues 11.2.3 Implications for the Semantic Web 11.3 Twitter Cards for Structured Information 11.3.1 Twitter Cards Overview 11.3.2 How Does It Work: Structured Information for Rich Tweets 11.3.2.1 The Basic Idea and Process 11.3.2.2 Twitter Card Markup Examples 11.3.2.3 Twitter Card Issues 11.3.3 Structured Information, But Not Semantic Web Yet 11.4 Rich Pins for Structured Information 11.4.1 Rich Pin Overview 11.4.2 How Does It Work: Generating Rich Pins Using schema.org 11.4.2.1 The Basic Idea and Process 11.4.2.2 Rich Pin Markup Examples 11.4.2.3 Rich Pin Issues 11.4.3 Semantic Markup at Work 11.5 Summary Chapter 12: Other Recent Applications: data.gov and Wikidata 12.1 Data.gov and the Semantic Web 12.1.1 Understanding Data.gov 12.1.2 How Is Data.gov Related to the Semantic Web? 12.1.3 Potential eGov Standards: Breaking the Boundaries of Datasets 12.1.4 Example Data.gov Applications 12.2 Wikidata and the Semantic Web 12.2.1 From Wikipedia to Wikidata 12.2.1.1 Are All the Infoboxes the Same? 12.2.1.2 Are All the Language Links the Same? 12.2.1.3 What About Fact Lists? 12.2.2 Three Phases of the Wikidata Project 12.2.3 Wikidata as a Data Repository 12.2.3.1 Wikidata URI Schema 12.2.3.2 DBpedia Vs. Wikidata 12.2.4 Wikidata and the Semantic Web 12.2.4.1 Wikidata Data Model and Its Ontology 12.2.4.2 Example Wikidata Datasets 12.3 Summary Part III: Building Your Own Applications on the Semantic Web Chapter 13: Getting Started: Change Your Data into Structured Data 13.1 RDF Data in General 13.1.1 What Does RDF Data Refer to? 13.1.2 Decide in Which Format to Publish Your RDF Data 13.1.2.1 RDF/XML 13.1.2.2 Turtle 13.1.2.3 N-triples 13.1.2.4 TriG and NQuads 13.1.3 Decide Which Ontology to Use to Publish Your Data 13.1.3.1 Discovering Ontologies 13.1.3.2 Understanding a New Ontology 13.2 Creating RDF Data Manually 13.2.1 Popular Editors and Validators 13.2.2 Examples: Using TopBraid to Create RDF Data 13.3 RDB2RDF: W3C´s Standard for Converting DB Content to RDF Triples 13.3.1 RDB2RDF: General Background 13.3.2 Direct Mapping from RDB to RDF 13.3.3 R2RML: RDB to RDF Mapping You Can Control 13.3.3.1 R2RML Mapping Language 13.3.3.2 R2RML Mapping Customization 13.4 RDB2RDF Example Implementation 13.4.1 RDB2RDF Direct Mapping 13.4.2 Step-by-Step R2RML Example: Virtuoso 13.4.2.1 Installing and Configuring Virtuoso Open Source Edition 13.4.2.2 Creating Database Tables and Loading Table Contents 13.4.2.3 Loading Ontology 13.4.2.4 Creating R2RML Mapping Document 13.4.2.5 Exposing the Database Tables as RDF Dataset 13.4.2.6 Creating the Physical Dump of the Generated RDF View 13.5 Summary Chapter 14: Building the Foundation for Development on the Semantic Web 14.1 Development Tools for the Semantic Web 14.1.1 Frameworks for the Semantic Web Applications 14.1.1.1 What Is a Framework and Why Do We Need It? 14.1.1.2 Jena 14.1.1.3 Sesame 14.1.1.4 Virtuoso 14.1.1.5 Redland 14.1.2 Reasoners for the Semantic Web Applications 14.1.2.1 What Is a Reasoner and Why Do We Need It? 14.1.2.2 Pellet 14.1.2.3 RacerPro 14.1.2.4 Jena 14.1.2.5 Virtuoso 14.1.3 Ontology Engineering Environments 14.1.3.1 What Is an Ontology Engineering Environment and Why Do We Need It? 14.1.3.2 Protégé 14.1.3.3 NeOn 14.1.3.4 TopBraid Composer 14.1.4 Other Tools: Search Engines for the Semantic Web 14.1.5 Where to Find More? 14.2 Semantic Web Application Development Methodology 14.2.1 From Domain Models to Ontology-Driven Architecture 14.2.1.1 Domain Models and MVC Architecture 14.2.1.2 The Uniqueness of Semantic Web Application Development 14.2.1.3 Ontology-Driven Software Development 14.2.1.4 Further Discussions 14.2.2 An Ontology Development Methodology Proposed by Noy and McGuinness 14.2.2.1 Basic Tasks and Fundamental Rules 14.2.2.2 Basic Steps of Ontology Development Step 1. Determine the Domain and Scope of the Ontology Step 2. Consider Reusing Existing Ontologies Step 3. Enumerate Important Terms in the Ontology Step 4. Define Classes and the Class Hierarchy Step 5. Define the Properties of Classes Step 6. Add Constraints to the Properties Step 7. Create Instances 14.2.2.3 Other Considerations 14.3 Summary Reference Chapter 15: Example: Using Jena for Development on the Semantic Web 15.1 Jena: A Semantic Web Framework for Java 15.1.1 What Is Jena and What Can It Do for Us? 15.1.2 Getting the Jena Package 15.1.3 Using Jena in Your Projects 15.1.3.1 Using Jena in Eclipse 15.1.3.2 Hello World! from a Semantic Web Application 15.2 Basic RDF Model Operations 15.2.1 Creating an RDF Model 15.2.2 Reading an RDF Model 15.2.3 Understanding an RDF Model 15.3 Handling Persistent RDF Models 15.3.1 From In-Memory Model to Persistent Model 15.3.2 Setting up MySQL 15.3.3 Database-Backed RDF Models 15.3.3.1 Single Persistent RDF Model 15.3.3.2 Multiple Persistent RDF Models 15.4 Inferencing Using Jena 15.4.1 Jena Inferencing Model 15.4.2 Jena Inferencing Examples 15.5 Summary Chapter 16: Follow Your Nose: A Basic Semantic Web Agent 16.1 The Principle of Follow-Your-Nose Method 16.1.1 What Is the Follow-Your-Nose Method? 16.1.2 URI Declarations, Open Linked Data and Follow-Your-Nose Method 16.2 A Follow-Your-Nose Agent in Java 16.2.1 Building the Agent 16.2.2 Running the Agent 16.2.3 More Clues for Follow-Your-Nose 16.2.4 Can You Follow Your Nose on Traditional Web? 16.3 A Better Implementation of Follow-Your-Nose Agent: Using SPARQL Queries 16.3.1 In-Memory SPARQL Operation 16.3.2 Using SPARQL Endpoints Remotely 16.4 Summary Chapter 17: A Search Engine That Supports Rich Snippets 17.1 Why This Is an Interesting Project 17.2 Introduction to Lucene 17.2.1 Lucene and Our Own Customized Search Engine 17.2.2 Core Components of Lucene 17.2.2.1 Lucene Document 17.2.2.2 Lucene Indexer 17.2.2.3 Lucene Searcher 17.2.3 Use Lucene in Your Development Environment 17.3 Preparing the Semantic Markups 17.3.1 From Semantic Markup to Rich Snippets 17.3.2 Different Deployment Models of the Markup 17.3.3 Examples of Markup 17.4 Building the Search Engine 17.4.1 Creating the Indexer 17.4.1.1 The Updated Flow of the Indexing Process 17.4.1.2 Converting Semantic Markup to Rich Snippets 17.4.1.3 Examining the Generated Index 17.4.2 Creating the Searcher 17.4.2.1 The Updated Flow of the Searching Process 17.4.2.2 Retrieving the Rich Snippets 17.4.2.3 Passing the Rich Snippets to the Front 17.4.3 Using Web Container to Start the Search 17.4.3.1 Using Apache Web Server for the Search Interface 17.4.3.2 Rendering the Rich Snippets in Search Result 17.5 Test It Out and Possible Expansions 17.5.1 Test Runs of the Search Engine 17.5.2 Possible Expansions 17.6 Summary Chapter 18: More Application Examples on the Semantic Web 18.1 Building Your Circle of Trust: A FOAF Agent You Can Use 18.1.1 Who Is on Your E-Mail List? 18.1.2 The Basic Idea 18.1.3 Building the EmailAddressCollector Agent 18.1.3.1 EmailAddressCollector 18.1.3.2 Running the EmailAddressCollector Agent 18.1.4 Can You Do the Same for the Traditional Web? 18.2 A ShopBot on the Semantic Web 18.2.1 A ShopBot We Can Have 18.2.2 A ShopBot We Really Want 18.2.2.1 How Does It Understand Our Needs? 18.2.2.2 How Does It Find the Next Candidate? 18.2.2.3 How Does It Decide Whether There Is a Match or Not? 18.2.3 Building Our ShopBot 18.2.3.1 Utility Methods and Class 18.2.3.2 Processing the Catalog Document 18.2.3.3 The Main Work Flow 18.2.3.4 Running Our ShopBot 18.2.4 Discussion: From Prototype to Reality 18.3 Summary Index