Key ingredients of the Semantic Web explained in 30 minutes.: 1. WHAT IS THE GOAL? 2. WHAT ARE THE BUILDING BLOCKS? 3. HOW DO WE CREATE THE GRAPH? WHY LINKED DATA? 4. SHORT INTRODUCTION TO ONTOLOGIE�S

1. The Semantic Web
a short guide
Maciej
Dabrowski
macdab@gmail.com

2. THE SEMANTIC WEB
WHAT ISTHE GOAL?
WHAT ARETHE BUILDING BLOCKS?
HOW DO WE CREATETHE GRAPH?
WHY LINKED DATA?
SHORT INTROTO ONTOLOGIES

3. What’s in a page ? And in a link ?
?
?
?

4. VISION FOR THE WEB
TIM BERNERS-LEE,THE 1ST WORLD WIDE WEB
CONFERENCE, GENEVA, MAY 1994:
DESCRIBE DOCUMENTS IN MACHINE READIBLE FORM
CREATE MEANINGFUL LINKS (“RELATIONSHIPVALUES”)
“ONLY WHEN WE HAVETHIS EXTRA LEVEL OF SEMANTICS
WILL WE BE ABLETO USE COMPUTER POWERTO HELP
US EXPLOITTHE INFORMATIONTO A GREATER EXTENT
THAN OUR OWN READING.”

5. Aims of the Semantic Web
BRIDGINGTHE GAP BETWEEN A WEB OF
DOCUMENTSTO A WEB OF DATA,WITH
TYPED OBJECTS ANDTYPED RELATIONSHIPS
ADDING MACHINE-READABLE METADATA
TO EXISTING CONTENT, SOTHAT
INFORMATION CAN BE PARSED, QUERIED,
REUSED

6. Aims of the Semantic Web
DEFINING SHARED SEMANTICS FORTHIS
METADATATO ALLOW INTEROPERABILITY
BETWEEN APPLICATIONS AND FOR
ADVANCED PURPOSES, SUCH AS REASONING
ENABLING MACHINE-READABLE KNOWLEDGE
AT WEB SCALE, MAKING INFORMATION MORE
EASYTO FIND AND PROCESS

7. The Semantic Web, circa 2010
MOST STANDARDISATION WORK IS DONE IN
THE W3C:
HTTP://WWW.W3.ORG/
INCUBATOR GROUPS,WORKING GROUP,
INTEREST GROUPS:
WGS FOR SPARQL, RDB2RDF, RIF, ETC.
HCLS IG, SOCIAL WEB XG, ETC.

8. Name everything

9. Identifying resources with URIs
URIS ARE USEDTO IDENTIFY EVERYTHING IN A
UNIQUE AND NON-AMBIGUOUS WAY
NOT ONLY PAGES (AS ONTHE CURRENT WEB),
BUT ANY RESOURCE (PEOPLE, DOCUMENTS,
BOOKS, INTERESTS, ETC.)
A URI FOR A PERSON IS DIFFERENT FROM A URI
FOR A DOCUMENT ABOUTTHE PERSON,
BECAUSE A PERSON IS NOT A DOCUMENT!
e.g. http://deri.ie/user/maciej-dabrowski
e.g. http://deri.ie/content/modelling-preference-relaxation-e-commerce

10. Defining assertions with RDF
• URIS IDENTIFY RESOURCES:
• WE USE RDF (RESOURCE DESCRIPTION
FRAMEWORK)TO DEFINE ASSERTIONS
ABOUTTHESE RESOURCES
• RDF IS A DATA MODEL;A DIRECTED, LABELED
GRAPH USING URIS
• RDF IS BASED ONTRIPLES:
– <SUBJECT> <PREDICATE> <OBJECT>.!

11. Simple triples
Maciej
Dabrowski
MDabrowski-lecture3
author
Semantic_Web
Introduction to the
Semantic Web
title
subject

12. Use Uris
http://example.org/maciej-dabrowski
http://example.org/MDabrowski-lecture3
http://example.org/author
http://example.org/Semantic_Web
Introduction to the
Semantic Web
http://example.org/title
http://example.org/subject

13. Abbreviating uris
PREFIX ex: http://example.org/#
ex:maciej = <http://example.org/#maciej>
ex:maciej-dabrowski
ex:MDabrowski-lecture3
ex:author
ex:Semantic_Web
Introduction to the
Semantic Web
ex:title
ex:subject

14. Reuse existing vocabularies
PREFIX dct: http://purl.org/dc/terms/
http://deri.ie/user/maciej-dabrowski
http://example.org/MDabrowski-lecture3
dct:creator
http://dbpedia.org/resource/Semantic_Web
Introduction to the
Semantic Web
dct:title
dct:subject

15. RDF by example
!
!
@prefix dct: <http://purl.org/dc/terms/> . !
<http://example.org/dm110-semweb>!
!dct:title “Introduction to the Semantic Web” ; !
!dct:author <http://www.deri.ie/users/maciej-dabrowski> ; !!
!dct:subject <http://dbpedia.org/resource/Semantic_Web> .!

16. RDFA
A WAY OF EMBEDDING RDF IN (X)HTML
DOCUMENTS:
ONE PAGE FOR BOTH HUMANS AND
MACHINES
DON’T NEEDTO REPEATYOURSELF
INTRODUCING NEW XHTML ATTRIBUTES
CURRENT WORK IS ONGOING ON RDFa 1.1:
FOR PROFILES, ETC.

17. RDFa example

18. Triples are everywhere!
10/06/2013
SUBJECT
PREDICATE
OBJECT
PREDICATE
OBJECT
OBJECT
…

20. Defining semantics with ontologies
• RDF PROVIDES A WAYTO WRITE ASSERTIONS
ABOUT URIS
• WHAT ABOUTTHE SEMANTICS OFTHESE
ASSERTIONS, E.G.TO STATETHAT HTTP://
XMLNS.COM/FOAF/0.1/KNOWS IDENTIFIES AN
ACQUAINTANCE RELATIONSHIP?
• ONTOLOGIES PROVIDE COMMON
SEMANTICS FOR RESOURCES ONTHE
SEMANTIC WEB

21. Ontologies consist mainly of classes and
properties
– :Person a rdfs:Class .!
– :father a rdfs:Property .!
– :father rdfs:domain :Person .!
– :father rdfs:range :Person .!
:Maciej
:Mark
:father
:Person
a
:Person
a

22. Notable ontologies
SOCIAL NETWORKS AND SOCIAL DATA:
FOAF, SIOC
TAXONOMIES AND CONTROLLED
VOCABULARIES:
SKOS, MOAT

23. Linked Data