Allemang D. Hendler J.Semantic Web for the Working Ontologist: Effective Modeling in RDFS and OWL

2008, Morgan Kaufmann
ISBN: 0123735564
352 р.SEMANTIC WEB for the WORKING ONTOLOGIST cuts through the hype around the Semantic Web, providing a simple, feasible, coherent story of a technology that provides real business value today. Authors Allemang and Hendler describe how the Semantic Web builds on the same principles that powered the success of Wikipedia, and of the World Wide Web itself.Лучшая книга по теме: "Семантический ВЕБ", который так же известен как "Web 3.0"About the AuthorsWhat Is the Semantic Web?
What Is a Web?
Smart Web, Dumb Web
Smart Web Applications
A Connected Web Is a Smarter Web
Semantic Data
A Distributed Web of Data
Features of a Semantic Web
What about the Round-Worlders?
To Each Their Own
There’s Always One MoreFundamental ConceptsSemantic Modeling
Modeling for Human Communication
Explanation and Prediction
Mediating Variability
Variation and Classes
Variation and Layers
Expressivity in ModelingFundamental ConceptsRDF—The Basis of the Semantic Web
Distributing Data Across the Web
Merging Data from Multiple Sources
Namespaces, URIs, and Identity
Expressing URIs in Print
Standard Namespaces
Identifiers in the RDF Namespace
Challenge: RDF and Tabular Data
Higher-Order Relationships
Alternatives for Serialization
N-Triples
viiNotation 3 RDF (N3)
RDF/XML
Blank Nodes
Ordered Information in RDFFundamental ConceptsSemantic Web Application Architecture
RDF Parser/Serializer
Other Data Sources—Converters and Scrapers
RDF Store
RDF Data Standards and Interoperability of RDF Stores
RDF Query Engines and SPARQL
Comparison to Relational Queries
Application Code
RDF-Backed Web Portals
Data FederationFundamental ConceptsRDF and Inferencing
Inference in the Semantic Web
Virtues of Inference-Based Semantics
Where are the Smarts?
Asserted Triples versus Inferred Triples
When Does Inferencing Happen?
Inferencing as GlueFundamental ConceptsRDF Schema
Schema Languages and Their Functions
What Does It Mean? Semantics as Inference
The RDF Schema Language
Relationship Propagation through rdfs:subPropertyOf
Typing Data by Usage—rdfs:domain and rdfs:range
Combination of Domain and Range with rdfs:subClassOf
RDFS Modeling Combinations and Patterns
Set Intersection
Property Intersection
Set Union
Property Union
Property Transfer
Challenges
Term Reconciliation
Instance-Level Data Integration
Readable Labels with rdfs:label
Data Typing Based on Use
Filtering Undefined Data
RDFS and Knowledge Discovery
Modeling with Domains and Ranges
Multiple Domains/Ranges
Nonmodeling Properties in RDFS
Cross-Referencing Files: rdfs:seeAlso
Organizing Vocabularies: rdfs:isDefinedBy
Model Documentation: rdfs:commentFundamental ConceptsRDFS-Plus
Inverse
Challenge: Integrating Data that Do Not Want to Be Integrated
Challenge: Using the Modeling Language tо Extend the Modeling Language
Challenge: The Marriage of Shakespeare
Symmetric Properties
Using OWL to Extend OWL
Transitivity
Challenge: Relating Parents to Ancestors
Challenge: Layers of Relationships
Managing Networks of Dependencies
Equivalence
Equivalent Classes
Equivalent Properties
Same Individuals
Challenge: Merging Data from Different Databases
Computing Sameness—Functional Properties
Functional Properties
Inverse Functional Properties
Combining Functional and Inverse Functional Properties
Contents ixA Few More ConstructsFundamental ConceptsUsing RDFS-Plus in the Wild
SKOS
Semantic Relations in SKOS
Meaning of Semantic Relations
Special Purpose Inference
Published Subject Indicators
SKOS in Action
FOAF
People and Agents
Names in FOAF
Nicknames and Online Names
Online Persona
Groups of People
Things People Make and Do
Identity in FOAF
It’s Not What You Know, It’s Who You KnowFundamental ConceptsBasic OWL
Restrictions
Example: Questions and Answers
Adding Restrictions
Kinds of Restrictions
Challenge Problems
Challenge: Local Restriction of Ranges
Challenge: Filtering Data Based on Explicit Type
Challenge: Relationship Transfer in SKOS
Relationship Transfer in FOAF
Alternative Descriptions of RestrictionsFundamental ConceptsCounting and Sets in OWL
Unions and Intersections
Closing the World
Enumerating Sets with owl:oneOf
Differentiating Individuals with owl:differentFrom
Differentiating Multiple Individuals
Cardinality
Small Cardinality Limits
Set Complement
Disjoint Sets
Prerequisites Revisited
No Prerequisites
Counting Prerequisites
Guarantees of Existence
Contradictions
Unsatisfiable Classes
Propagation of Unsatisfiable Classes
Inferring Class Relationships
Reasoning with Individuals and with ClassesFundamental ConceptsUsing OWL in the Wild
The Federal Enterprise Architecture Reference Model Ontology
Reference Models and Composability
Resolving Ambiguity in the Model: Sets versus Individuals
Constraints between Models
OWL and Composition
owl:Ontology
owl:imports
Advantages of the Modeling Approach
The National Cancer Institute Ontology
Requirements of the NCI Ontology
Upper-Level Classes
Describing Classes in the NCI Ontology
Instance-Level Inferencing in the NCI OntologyFundamental ConceptsGood and Bad Modeling Practices
Getting Started
Know What You Want
Inference Is Key
Modeling for Reuse
Insightful Names versus Wishful Names
Contents xiKeeping Track of Classes and Individuals
Model Testing
Common Modeling Errors
Rampant Classism (Antipattern)
Exclusivity (Antipattern)
Objectification (Antipattern)
Managing Identifiers for Classes (Antipattern)
Creeping Conceptualization (Antipattern)Fundamental ConceptsOWL Levels and Logic
OWL Dialects and Modeling Philosophy
Provable Models
Executable Models
OWL Full versus OWL DL
Class/Individual Separation
InverseFunctional Datatypes
OWL Lite
Other Subsets of OWL
Beyond OWL 1.0
Metamodeling
Multipart Properties
Qualified Cardinality
Multiple Inverse Functional Properties
RulesFundamental ConceptsConclusions
APPENDIX Frequently Asked Questions
Further Reading