Ontology and semantic web technologies
Ontology technology is a technology for clearly defining the relationships between concepts and terms in a particular domain or field, and provides a semantic framework that can improve information interoperability, allowing different applications to have a common understanding, or sharing and processing information between machines This is characterized by the fact that it facilitates the sharing and processing of information between machines.
On the other hand, Semantic Web technology is a technology for expressing information on the Web in a more meaningful format and improving interoperability. It uses technologies such as RDF and OWL to express information in a meaningful format, and by using SPARQL, data can be retrieved efficiently. This Semantic Web technology can be used with ontology technology to provide a common framework for better defining information and clarifying relevance.
Ontology technologists work in a variety of fields, including business, science, and medicine, where they understand discipline-specific terminology and concepts and develop ontologies for their fields so that information can be managed more effectively on the Semantic Web. Ontology technologists also use the Semantic Web to achieve a variety of goals, including search engines, information processing, data integration, knowledge management, and application development. This paper describes Semantic Web technologies from the perspective of ontology engineers, based on the book “Semantic Web for the Working Ontologist.
The Semantic Web for Ontology Engineers
This book mainly describes the modeling languages such as RDF, RDFS, OWL, etc., and the modeling of ontologies using them, including the description of the languages and the steps of the actual modeling methods. As for chapters 11 and 12, where ontology is discussed, I think it would be helpful to read the literature on formal semantics that I mentioned earlier.The table of contents is as follows.
Chapter 1 What is the Semantic Web?
What is a Web?
Smart, Web, Dumb Web
Smart Web application
Connected data is smart data
Semantic Data
A distributed web of data
Features of Semnatic Web
What about the round-worlders?
To reach their own
There's always one move
Summary
Fundamental concept
Chapter 2 Semantic Modeling
Modeling for Human Commenication
Explanation Prediction
Meditating Variability
Variation and classes
Variation ans layers
Expressivity in Modeling
Summary
Fundamental concept
Chapter 3 RDF - The basis of the Semantic Web
Distributing Data across the Web
Merging Data from Multiple Sources
Namespaces, URLs, and identity
Expressing URIs in print
Standard namespaces
identifiers in the RDF Namespace
Higher-order Relationships
Alternatives for Serialization
N-triples
Turtle
RDF/XML
Blank Nodes
Ordered information in RDF
Summary
Fundamental concept
Chapter 4 Semantic Web application architecture
RDF Parser/Serializer
Other data sources
RDF Store
RDF data standards and interoperability of RDF stores
RDF query engines
Comparison to relational queries
Application Code
RDF-backed web portals
Data Federation
Summary
Fundamental concept
Chapter 5 Querying the Semantic Web-SPARQL
Tell-and-Ask System
Common tell-and-ask infrastructure - spreadsheets
Advanced tell-and-ask infrastructure - relational database
RDF as a Tell-and-Ask System
SPARQL - Query Language for RDF
Naming question words in SPARQL
Query structure vs. data structure
Ordering of triples in SPARQL queries
Querying for properties and schema
Variables, bindings, and filters
Optional matches
Negation(SPARQL 1.1)
Yes/No queries
Construct Queries in SPARQL
Using Results of CONSTRUCT Queries
SPARQL rules - using SPARQL as a Rule Language
Transitive queries(SPARQL1.1)
Advanced Features of SPARQL
Limits and ordering
Aggregates and Grouping(SPARQL1.1)
Subqueries(SPARQL1.1)
Union
Assignments(SPARQL1.1)
Federating SPARQL Queries
Summary
Fundamental concept
Chapter 6 RDF and Inferencing
Inference in the Semantic Web
SPARQL and inference
Virtues of inference-based semantics
Where Are The Smarts?
Asserted triples versus inferred triples
When Does Inferencing Happen?
Inferencing as specification
Summary
Fundamental concept
Chapter7 RDF schema
Schema Languages and Their Function
Relationship between schema and data
The RDF Schema Language
Relationship propagation through rdf:subProprtyOf
Typing data by usage rdfs:domain and rdfs:range
Combination of domain and range with rdfs:subClassOf
RDFS Modeling Combinations Patterns
Set intersection
Property intersection
Set Union
Property union
Property transfer
Property reconcillation
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 Range
Multiple domains/ranges
Nonmodeling Properties in RDFS
Cross-referencing files:rdfs:seeAlso
Organizing vocablaries drfs:isDefineBy
Model documentation rdfs:comment
Summary
Fundamental concept
Chapter 8 RDFS-Plus
Inverse
Symmetric Properties
Using OWL to OWL
Transitivity
Managing networks of dependencies
Equivalence
Equivalent classes
Equivalent properties
Same individuals
Computing Sameness - Functional Properties
Functional properties
inverse functional properties
A Few More Constructs
Summary
Fundamental concept
Chapter 9 Using RDFS-Plus in the wild
Open Government Data
Describing relationship in data
Merging data with RDF and SPARQL
Data.gov Summary
FOAF
People and agents
Names in FOAF
Nicknames and online names
Online persona
Groups of people
Thing people make and do
Identity in FOAF
It's not what you know, it's who you are
Facebook's Open Graph Protocol
The OGP model
Embedding OGP in web page
Summary
Fundamental concept
Chapter 10 SKOS managing vocabularies with RDFS-Plus
Simple Knowledge Organization System(SKOS)
Semantic Relation in SKOS
Meaning of semantic relations
SKOS and linked vocabularies
Concept Schemes
Managing SKOS concept schemes
SKOS integrity
SKOS in Action
Summary
Fundamental concept
Chapter 11 Basic OWL
Restrictions
Adding"restrictions"
Kinds of restrictions(owl:someValuesFrom、owl:allValuesFrom、owl:hasValue)
Alternative Descriptions of Restriction
Summary
Fundamental concept
Chapter12 Counting and sets in OWL
Unions and Intersections
Closing the world (owl:oneOf)
Diffrentiating individuals with owl:differentFrom
Diffrentiating Multiple individuals
Cardinality
Qualified cardinality(OWL2.0)
Small cardinality limits
Set Complement
Disjoin Sets
Prerequisities Revised
No Prerequisities
Counting pterequisitie
Guarantees of existence
Contradiction
Unsatisfiable Class
Propagation of unsatisfiable class
Inferring Class Relationships
Reasoning with Individuals and with Classes
Summary
Fundamental concept
Chapter 13 Ontologies on the Web - putting it all together
The Good Relations Ontology
Inferencing in the Good Relations Ontology
Composing Files
owl:Ontology
owl:import
Summary
Quantities, Units, and Dimensions
Converting Units with QUDT
Using QUDT conversions
Dimension Checking in QUDT
Summary
Biological Ontologies
CHEBI as Unambiguous Reference
CHEBI for Complex Search
Summary
Fundamental concept
Chapter 14 Good and bad modelingpractices
Getting started
Know what you want
Say what you mean, mean what you say
Modeling for Reuse
Insightful names versus wishful names
Keeping track of classes and individuals
Modeling testing
Common Modeling Errors
Rampant classism (antipattern)
Exclusivity (antipattern)
Objectification (antipattern)
Creeping conceptualization (antpattern)
Summary
Fundamental concept
Chapter 15 Expert modeling in OWL
OWL Subsets and Modeling Philosophy
Provable models
Executable models
OWL2 Modeling Capabilities
Metamodeling
Multipart properties
Multiple inverse functional properties
OWL2 profiles
Rules
Summary
Fundamental concept
Chapter 16 Conclusions
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