A Developer's Guide to the Semantic Web

Preface

Objectives of the Book

Intended Readers

Structure of the Book

Where to Get the Code

Acknowledgment

Contents

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

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 Machine 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 Un-typed 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 Feature

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 Understandable by Machine

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 Pre-defined RDF Vocabulary

2.6.2 XML vs. RDF?

2.6.3 Use an RDF Validator

2.7 Summary

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 Mark Up Page Content

3.2.3 Microformats and RDF

3.2.3.1 What Is 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 Is 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

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 Datatypes

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

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 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

6 SPARQL: Querying the Semantic Web

6.1 SPARQL Overview

6.1.1 SPARQL in Official Language

6.1.2 SPARQL in Plain English

6.1.3 Other Related Concepts: RDF Data Store, RDF Database, and Triple Store

6.2 Set up Joseki SPARQL Endpoint

6.3 SPARQL Query Language

6.3.1 The Big Picture

6.3.1.1 Triple Pattern

6.3.1.2 Graph Pattern

6.3.2 SELECT Query

6.3.2.1 Structure of a SELECT Query

6.3.2.2 Writing Basic SELECT Query

6.3.2.3 Using OPTIONAL Keyword for Matches

6.3.2.4 Using Solution Modifier

6.3.2.5 Using FILTER Keyword to Add Value Constraints

6.3.2.6 Using Union Keyword for Alternative Match

6.3.2.7 Working with Multiple Graphs

6.3.3 CONSTRUCT Query

6.3.4 DESCRIBE Query

6.3.5 ASK Query

6.4 What Is Missing from SPARQL?

6.5 SPARQL 1.1

6.5.1 Introduction: What Is New?

6.5.2 SPARQL 1.1 Query

6.5.2.1 Aggregate Functions

6.5.2.2 Subqueries

6.5.2.3 Negation

6.5.2.4 Expressions with SELECT

6.5.2.5 Property Paths

6.5.3 SPARQL 1.1 Update

6.5.3.1 Graph Update: Adding RDF Statements

6.5.3.2 Graph Update: Deleting RDF Statements

6.5.3.3 Graph Update: LOAD and CLEAR

6.5.3.4 Graph Management: Graph Creation

6.5.3.5 Graph Management: Graph Removal

6.6 Summary

7 FOAF: Friend of a Friend

7.1 What Is FOAF 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

8 Semantic Markup at Work: Rich Snippets and SearchMonkey

8.1 Introduction

8.1.1 Prerequisite: How Does a Search Engine Work?

8.1.1.1 Basic Search Engine Tasks

8.1.1.2 Basic Search Engine Workflow

8.1.2 Rich Snippets and SearchMonkey

8.2 Rich Snippets by Google

8.2.1 What Is Rich Snippets: An Example

8.2.2 How Does It Work: Semantic Markup Using Microformats/RDFa

8.2.2.1 Rich Snippets Powered by Semantic Markup

8.2.2.2 Microformats Supported by Rich Snippets

8.2.2.3 Ontologies Supported by Rich Snippets

8.2.3 Test It Out Yourself

8.3 SearchMonkey from Yahoo

8.3.1 What Is SearchMonkey: An Example

8.3.2 How Does It Work: Semantic Markup Using Microformats/RDFa

8.3.2.1 SearchMonkey Architecture

8.3.2.2 Microformats Supported by SearchMonkey

8.3.2.3 Ontologies Supported by SearchMonkey

8.3.3 Test It Out Yourself

8.4 Summary

Reference

9 Semantic Wiki

9.1 Introduction: From Wiki to Semantic Wiki

9.1.1 What Is a Wiki?

9.1.2 From Wiki to Semantic Wiki

9.2 Adding Semantics to Wiki Site

9.2.1 Namespace and Category System

9.2.2 Semantic Annotation in Semantic MediaWiki

9.2.2.1 Semantic Annotation: Links

9.2.2.2 Semantic Annotation: Text

9.3 Using the Added Semantics

9.3.1 Browsing

9.3.1.1 FactBox

9.3.1.2 Semantic Browsing Interface

9.3.2 Wiki Site Semantic Search

9.3.2.1 Direct Wiki Query: Basics

9.3.2.2 Direct Wiki Query: Advanced Search

9.3.2.3 Displaying Information

9.3.3 Inferencing

9.4 Where Is the Semantics?

9.4.1 SWiVT: an Upper Ontology for Semantic Wiki

9.4.2 Understanding OWL/RDF Exports

9.4.3 Importing Ontology: a Bridge to Outside World

9.5 The Power of the Semantic Web

9.6 Use Semantic MediaWiki to Build Your Own Semantic Wiki

9.7 Summary

10 DBpedia

10.1 Introduction to DBpedia

10.1.1 From Manual Markup to Automatic Generation of Annotation

10.1.2 From Wikipedia to DBpedia

10.1.3 The Look and Feel of DBpedia: Page Redirect

10.2 Semantics in DBpedia DBpedia look and feel

10.2. Infobox Template

10.2.2 Creating DBpedia Ontology

10.2.2.1 The Need for Ontology

10.2.2.2 Mapping Infobox Templates to Classes

10.2.2.3 Mapping Infobox Template Attributes to Properties

10.2.3 Infobox Extraction Methods

10.2.3.1 Generic Infobox Extraction Method

10.2.3.2 Mapping-Based Infobox Extraction Method

10.3 Accessing DBpedia Dataset

10.3.1 Using SPARQL to Query DBpedia

10.3.1.1 SPARQL Endpoints for DBpedia

10.3.1.2 Examples of Using SPARQL to Access DBpedia

10.3.2 Direct Download of DBpedia Datasets

10.3.2.1 The Wikipedia Datasets

10.3.2.2 DBpedia Core Datasets

10.3.2.3 Extended Datasets

10.3.3 Access DBpedia as Linked Data

10.4 Summary

Reference

11 Linked Open Data

11.1 The Concept of Linked Data and Its Basic Rules

11.1.1 The Concept of Linked Data

11.1.2 How Big Is the Web of Linked Data and the LOD Project

11.1.3 The Basic Rules of Linked Data

11.2 Publishing RDF Data on the Web

11.2.1 Identifying Things with URIs

11.2.1.1 Web Document, Information Resource, and URI

11.2.1.2 Non-information Resources and Their URIs

11.2.1.3 URIs for Non-information Resources: 303 URIs and Content Negotiation

11.2.1.4 URIs for Non-information Resources: Hash URIs

11.2.1.5 URIs for Non-information Resources: 303 URIs vs. Hash URIs

11.2.1.6 URI Aliases

11.2.2 Choosing Vocabularies for RDF Data

11.2.3 Creating Links to Other RDF Data

11.2.3.1 Basic Language Constructs to Create Links

11.2.3.2 Creating Links Manually

11.2.3.3 Creating Links Automatically

11.2.4 Serving Information as Linked Data

11.2.4.1 Minimum Requirements for Being Linked Open Data

11.2.4.2 Example: Publishing Linked Data on the Web

11.2.4.3 Make Sure You Have Done It Right

11.3 The Consumption of Linked Data

11.3.1 Discover Specific Target on the Linked Data Web

11.3.1.1 Semantic Web Search Engine for Human Eyes

11.3.1.2 Semantic Web Search Engine for Applications

11.3.2 Accessing the Web of Linked Data

11.3.2.1 Using a Linked Data Browser

11.3.2.2 Using SPARQL Endpoints

11.3.2.3 Accessing the Linked Data Web Programmatically

11.4 Linked Data Application

11.4.1 Linked Data Application Example: Revyu

11.4.1.1 Revyu: An Overview

11.4.1.2 Revyu: Why It Is Different

11.4.2 Web 2.0 Mashups vs. Linked Data Mashups

11.5 Summary

12 Building the Foundation for Development on the Semantic Web

12.1 Development Tools for the Semantic Web

12.1.1 Frameworks for the Semantic Web Applications

12.1.1.1 What Is a Framework and Why We Need It?

12.1.1.2 Jena

12.1.1.3 Sesame

12.1.1.4 Virtuoso

12.1.1.5 Redland

12.1.2 Reasoners for the Semantic Web Applications

12.1.2.1 What Is a Reasoner and Why We Need It?

12.1.2.2 Pellet

12.1.2.3 RacerPro

12.1.2.4 Jena

12.1.2.5 Virtuoso

12.1.3 Ontology Engineering Environments

12.1.3.1 What Is an Ontology Engineering Environment and Why We Need It?

12.1.3.2 Protégé

12.1.3.3 NeOn

12.1.3.4 TopBraid Composer

12.1.4 Other Tools: Search Engines for the Semantic Web

12.1.5 Where to Find More?

12.2 Semantic Web Application Development Methodology

12.2.1 From Domain Models to Ontology-Driven Architecture

12.2.1.1 Domain Models and MVC Architecture

12.2.1.2 The Uniqueness of Semantic Web Application Development

12.2.1.3 Ontology-Driven Software Development

12.2.1.4 Further Discussions

12.2.2 An Ontology Development Methodology Proposed by Noy and McGuinness

12.2.2.1 Basic Tasks and Fundamental Rules

12.2.2.2 Basic Steps of Ontology Development

12.2.2.3 Other Considerations

12.3 Summary

Reference

13 Jena: A Framework for Development on the Semantic Web

13.1 Jena: A Semantic Web Framework for Java

13.1.1 What Is Jena and What It Can Do for Us?

13.1.2 Getting Jena Package

13.1.3 Using Jena in Your Projects

13.1.3.1 Using Jena in Eclipse

13.1.3.2 Hello World! from Semantic Web Application

13.2 Basic RDF Model Operations

13.2.1 Creating an RDF Model

13.2.2 Reading an RDF Model

13.2.3 Understanding an RDF Model

13.3 Handling Persistent RDF Models

13.3.1 From In-memory Model to Persistent Model

13.3.2 Setting Up MySQL

13.3.3 Database-Backed RDF Models

13.3.3.1 Single Persistent RDF Model

13.3.3.2 Multiple Persistent RDF Models

13.4 Inferencing Using Jena

13.4.1 Jena Inferencing Model

13.4.2 Jena Inferencing Examples

13.5 Summary

14 Follow Your Nose: A Basic Semantic Web Agent

14.1 The Principle of Follow-Your-Nose Method

14.1.1 What Is Follow-Your-Nose Method?

14.1.2 URI Declarations, Open Linked Data, and Follow-Your-Nose Method

14.2 A Follow-Your-Nose Agent in Java

14.2.1 Building the Agent

14.2.2 Running the Agent

14.2.3 More Clues for Follow Your Nose

14.2.4 Can You Follow Your Nose on Traditional Web?

14.3 A Better Implementation of Follow-Your-Nose Agent: Using SPARQL Queries

14.3.1 In-memory SPARQL Operation

14.3.2 Using SPARQL Endpoints Remotely

14.4 Summary

15 More Application Examples on the Semantic Web

15.1 Building Your Circle of Trust: A FOAF Agent You Can Use

15.1.1 Who Is on Your E-mail List?

15.1.2 The Basic Idea

15.1.3 Building the EmailAddressCollector Agent

15.1.3.1 EmailAddressCollector

15.1.3.2 Running the EmailAddressCollector Agent

15.1.4 Can You Do the Same for Traditional Web?

15.2 A ShopBot on the Semantic Web

15.2.1 A ShopBot We Can Have

15.2.2 A ShopBot We Really Want

15.2.2.1 How Does It Understand Our Needs?

15.2.2.2 How Does It Find the Next Candidate?

15.2.2.3 How Does It Decide Whether There Is a Match or Not?

15.2.3 Building Our ShopBot

15.2.3.1 Utility Methods and Class

15.2.3.2 Processing the Catalog Document

15.2.3.3 The Main Work Flow

15.2.3.4 Running Our ShopBot

15.2.4 Discussion: From Prototype to Reality

15.3 Summary

Index