Introduction to
the Semantic Web
Fulvio Corno, Laura Farinetti
Politecnico di Torino
Dipartimento di Automatica e Informatica
e-Lite Research Group – http://elite.polito.it

Semantic Web
http://www.w3.org/2001/sw/
Web second generation
Web 3.0
“Conceptual structuring of the Web in an explicit
machine-readable way”
(Tim Berners-Lee)
In other words…
…let the machine do most of the work!!!
F. Corno, L. Farinetti- Politecnico di Torino 2

“Official” introduction
The Semantic Web is a web of data. There is
lots of data we all use every day, and its not part
of the web. I can see my bank statements on the
web, and my photographs, and I can see my
appointments in a calendar. But can I see my
photos in a calendar to see what I was doing
when I took them? Can I see bank statement
lines in a calendar?
Why not? Because we don’t have a web of data.
Because data is controlled by applications, and
each application keeps it to itself
F. Corno, L. Farinetti- Politecnico di Torino 3

Example
F. Corno, L. Farinetti- Politecnico di Torino 4

“Official” introduction
The Semantic Web is about two things
It is about common formats for integration and
combination of data drawn from diverse sources,
where on the original Web mainly concentrated
on the interchange of documents.
It is also about language for recording how the
data relates to real world objects. That allows a
person, or a machine, to start off in one
database, and then move through an unending
set of databases which are connected not by
wires but by being about the same thing
F. Corno, L. Farinetti- Politecnico di Torino 5

An example …
How can a machine distinguish the
meanings … ?
“I am a professor of computer science.”
“I am a professor of computer science,
you may think. Well,…”
F. Corno, L. Farinetti- Politecnico di Torino 6

Key principles
The Semantic Web is the Web
Same base technologies, evolutionary
Decentralized (incomplete, inconsistent)
Provide explicit statements regarding web
resources
Authors, original information providers
Intermediaries (humans and/or machines)
Information consumers determine
consequences of the statements
Distributed ‘reasoning’
F. Corno, L. Farinetti- Politecnico di Torino 7

1989:
WWW
original
proposal
F. Corno, L. Farinetti- Politecnico di Torino 8

Technology stack (old: pre-2008)
F. Corno, L. Farinetti- Politecnico di Torino 9

Technology stack (current: 2008)
F. Corno, L. Farinetti- Politecnico di Torino 10

Goal of the semantic Web
The Semantic Web will enable
machines to COMPREHEND semantic
documents and data, NOT human
speech and writing
Then, how???
Semantic Web foundation: metadata
F. Corno, L. Farinetti- Politecnico di Torino 11

Metadata and
Metadata Standards

Resource and description
Resource
Content, format, …
Access method dependent on format (I can
read it if I “know” its language)
Resource description
Independent of the format (I can read
“people’s comments” about the resource…
provided that I know the language in which
the comment is written)
F. Corno, L. Farinetti- Politecnico di Torino 13

Resource and description
this resource is suitable
the title of this description
for PhD students
resource is
“Introduction to
resource
the Semantic
Web”
this resource
was created on
the author of
April 14th, 2009
this resource
is L. Farinetti
this resource is
related to
the quality of computer
this resource science,
is high, knowledge
according to representation
F. Corno and metadata
F. Corno, L. Farinetti- Politecnico di Torino 14

Resource and description
Resource
Content, format, …
Access method dependent on format (I can read it if I
“know” its language)
Standardization (i.e. common language for
applications) ???
Practically impossible …
Huge amount of existing information
Hundreds of human languages
Hundreds of computer languages (other word for
formats)
F. Corno, L. Farinetti- Politecnico di Torino 15

Resource and description
Resource description
Independent of the format (I can read “people’s
comments” about the resource… provided that I know
the language in which the comment is written)
Standardization (i.e. common language for
applications) ???
Feasible
Smaller amount of information, possibly new
Solution: define a standard language for writing
comments (“metadata” in semantic web terminology)
F. Corno, L. Farinetti- Politecnico di Torino 16

Resource and description
this resource is suitable
the title of this for PhD students
resource is
“Introduction to
the Semantic
Web”
this resource
was created on
the author of
April 14th, 2009
this resource
is L. Farinetti Metadata
this resource is
related to
the quality of computer
Field name = field value
this resource science,
is high, knowledge
according to representation
F. Corno and metadata
F. Corno, L. Farinetti- Politecnico di Torino 17

Resource and description
Level = PhD students
description
Title =
“Introduction to
the Semantic resource
Web”
Date =
2009-04-14
Author =
L. Farinetti
Topic =
{computer
science,
Quality = high
knowledge
representation,
Rated by F. Corno
Rated by F. Corno metadata}
F. Corno, L. Farinetti- Politecnico di Torino 18

Semantic Web main tasks
Metadata annotation
Description of resources using standard
languages
Search
Retrieve relevant information according to
user’s query / interest / intention
Use metadata (and possibly content) in a
“smart” way (i.e. “reasoning” about the
meaning of annotations)
F. Corno, L. Farinetti- Politecnico di Torino 19

Meaningful metadata annotations
Common language for describing resources
Resource description standards
Common language for description field names
Metadata standards
Common language for description field values
Metadata standards + controlled vocabularies
Semantically rich descriptions to support search
Knowledge representation techniques, ontologies
F. Corno, L. Farinetti- Politecnico di Torino 20

Common language for describing
resources
F. Corno, L. Farinetti- Politecnico di Torino 21

Common language for describing
resources
Resource Description Framework (RDF)
Resource = URI (retrievable, or not)
RDF is structured in statements
A statement is a triple
Subject – predicate – object
Subject: a resource
Predicate: a verb / property / relationship
Object: a resource, or a literal string
F. Corno, L. Farinetti- Politecnico di Torino 22

Common language for describing
resources
Author =
L. Farinetti
Diagram:
hasAuthor
URI L.Farinetti
Simple RDF assertion (triple):
triple (hasAuthor, URI, L.Farinetti)
F. Corno, L. Farinetti- Politecnico di Torino 23

Common language for describing
resources
Author =
L. Farinetti
RDF in XML syntax:
<RDF xmlns=“http://www.w3.org/TR/ … ” >
<RDF xmlns=“http://www.w3.org/TR/ … ” >
<Description about=“http://www.polito.it/semweb/intro”>
<Description about=“http://www.polito.it/semweb/intro”>
<Author>L.Farinetti</Author>
<Author>L.Farinetti</Author>
</Description>
</Description>
</RDF>
</RDF>
F. Corno, L. Farinetti- Politecnico di Torino 24

Common language for field names
Problem Topic = …
Topics, Subject, Subjects,
Level = …
Argument, Arguments
Title = ... Educational level,
destination, suitability, …
Singular / plural
Difficult to clearly
Date = …
define concept in a
Author = …
few words
Date of creation, date of
Creator, Maker,
last modification, date of
Contributor …
revision, …
Different concepts:
Synonymy
need for more details
F. Corno, L. Farinetti- Politecnico di Torino 25

Common language for field names
Solution: metadata standards
Many standardization bodies are involved
Standards may be general
e.g. Dublin Core (DC)
or may depend on goal, context, domain, …
e. g. educational resources (IEEE LOM), multimedia
resources (MPEG 7), images (VRA), people (FOAF,
-
IEEE PAPI), geospatial resources (GSDGM),
bibliographical resources (MARC, OAI), cultural
heritage resources (CIDOC CRM)
F. Corno, L. Farinetti- Politecnico di Torino 26

Metadata standards examples
F. Corno, L. Farinetti- Politecnico di Torino 27

Dublin Core
Dublin Core Metadata Element Set
(DCMES)
Building blocks to define metadata for the
Semantic Web
15 elements, or categories, general enough to
describe most of the published resources
Extra elements and element refinements
F. Corno, L. Farinetti- Politecnico di Torino 28

DC metadata element set
F. Corno, L. Farinetti- Politecnico di Torino 29

Example of description using
Dublin Core (in RDF)
A paper in the
“Ariadne” journal
F. Corno, L. Farinetti- Politecnico di Torino 30

Common language for field values
Problems
Value type Date =
2009-04-14
type = date
Title =
“Introduction to
the Semantic Author =
Web” L. Farinetti
type = string
type = string
“standard” format?
Laura Farinetti, Farinetti
Laura, Farinetti L., …
F. Corno, L. Farinetti- Politecnico di Torino 31

Common language for field values
Problems Level = PhD students
Value type any value?
list of possible values?
Value restrictions?
freedom vs shared understanding
Topic =
Quality = high {computer
science,
knowledge
High, medium, low?
representation,
1 to 5?
metadata}
any value?
any value?
any number of values?
F. Corno, L. Farinetti- Politecnico di Torino 32

Common language for field values
Solution: metadata standards + controlled
vocabularies
Metadata standards
Only some, and partially
Controlled vocabularies
Explicit list of possible values
F. Corno, L. Farinetti- Politecnico di Torino 33

Examples from IEEE LOM
1484.12.1 - 2002 Learning Object
Metadata (LOM) Standard
Developed by the IEEE Learning Technology
Standards Committee (LTSC)
Standard to describe the “Learning
Objects” in order to guarantee their
interoperability
F. Corno, L. Farinetti- Politecnico di Torino 34

Examples from IEEE LOM
F. Corno, L. Farinetti- Politecnico di Torino 35

Examples from IEEE LOM
F. Corno, L. Farinetti- Politecnico di Torino 36

Examples from IEEE LOM
F. Corno, L. Farinetti- Politecnico di Torino 37

… + controlled vocabularies
A closed list of named subjects, which can
be used for classification
Topic =
Metadata field values are {computer
science,
restricted to a list of terms informatics,
(selected by experts) knowledge
representation,
metadata}
F. Corno, L. Farinetti- Politecnico di Torino 38

Semantically rich descriptions to
support search
F. Corno, L. Farinetti- Politecnico di Torino 39

Semantically rich descriptions to
support search
http://dictybase.org/db/html/help/GO.html
http://dictybase.org/db/html/help/GO.html
Topic =
{metabolism, …}
F. Corno, L. Farinetti- Politecnico di Torino 40

Knowledge
Representation

Need for knowledge representation
Semantically rich descriptions need
“understanding” the meaning of a resource
and the domain related to the resource
Disambiguation of terms
Shared agreement on meanings
Description of the domain, with concepts and
relations among concepts
F. Corno, L. Farinetti- Politecnico di Torino 42

Example: Dublin Core metadata
Metadata of a single paper
F. Corno, L. Farinetti- Politecnico di Torino 43

Problems
Title usually offers good clues, but
it does not necessarily mention all names of all
subjects the user is interested in
it may presuppose knowledge the user does not
actually possess
Subject is meant to convey precisely what the
document is about, but
much depends on how extensive the set of keywords
is, whether all related subjects are mentioned, and
whether too many subjects are listed
Metadata does not say much about “how
related” a resource is to a given subject
F. Corno, L. Farinetti- Politecnico di Torino 44

Search results for “topic maps”
F. Corno, L. Farinetti- Politecnico di Torino 45

Problems
Authors were free to define their own
subject keywords
Results are not “about” topic maps, but
“related to” topic maps
If an author forgets to list “topic maps”, his
paper will never be found
F. Corno, L. Farinetti- Politecnico di Torino 46

Subject-based classification
Any form of content classification that groups
objects by their subjects
e.g the use of keywords to classify papers
Metadata fields describe what the objects are
about by listing discrete subjects inside a
subject-based classification
Important: difference between describing the
objects being classified and describing the
subjects used to classify them
Metadata describe objects
Subject bsed classification is the approach to
-a
describe subject
F. Corno, L. Farinetti- Politecnico di Torino 47

Subject-based classification ...
“On those remote pages it is written that animals are divided into:
a. those that belong to the Emperor
b. embalmed ones
c. those that are trained
From The Celestial Emporium of
d. suckling pigs
Benevolent Knowledge, Borges
e. mermaids
f. fabulous ones
g. stray dogs
h. those that are included in this classification
i. those that tremble as if they were mad
j. innumerable ones
k. those drawn with a very fine camel's hair brush
l. others
m. those that have just broken a flower vase
n. those that resemble flies from a distance\"
F. Corno, L. Farinetti- Politecnico di Torino 48

Subject-based classification
techniques
Controlled vocabularies
Taxonomies
Thesauri
Faceted classification
Ontologies
Folksonomies
Others
… Most come from library science
F. Corno, L. Farinetti- Politecnico di Torino 49

Controlled vocabulary
A closed list of named subjects, which can be
used for classification
Composed of terms: particular name for a
particular concept
similar to keywords
Terms are not concepts
A single term may be the name of one or more
concepts
A single concept may have multiple names
Ambiguity avoided by forbidding duplicate terms
F. Corno, L. Farinetti- Politecnico di Torino 50

Topic =
Controlled vocabulary {computer
science,
knowledge
representation,
mtadata, RDF,
topic navigation
maps}
topic maps
Goal
Prevent authors from defining terms that are
meaningless, too broad or too narrow
Prevent authors from misspelling
Prevent different authors from choosing
slightly different forms of the same term
The simplest form of controlled vocabulary
is a list of terms (or “pick list”)
F. Corno, L. Farinetti- Politecnico di Torino 51

Controlled vocabulary
Reduce ambiguity inherent in normal
human languages
Solve the problems of homographs,
homonyms, synonyms and polysemes by
ensuring
That each concept is described using only
one authorized term
That each authorized term in the controlled
vocabulary describes only one concept
F. Corno, L. Farinetti- Politecnico di Torino 52

Problems solved
Synonym
different words with identical or very similar meanings
F. Corno, L. Farinetti- Politecnico di Torino 53

Problems solved
“Will you please close that door!”
close
“The tiger was now so close that I could smell it...”
student
pupil
opening in the iris of the eye
('æk.səz) plural of axe
axes
('æk.siz) plural of axis
Synonym
different words with identical or very similar meanings
F. Corno, L. Farinetti- Politecnico di Torino 54

Problems solved
take (I'll get the drinks)
become (she got scared)
to get
understand (I get it)
a piece of a tree
wood
a geographical area with many trees
Synonym
different words with identical or very similar meanings
student and pupil (noun)
buy and purchase (verb)
sick and ill (adjective)
F. Corno, L. Farinetti- Politecnico di Torino 55

Controlled vocabulary examples
Blood
Blood
Circuit theory
Circuit theory
Cord blood
Cord blood
Electronic circuits
Electronic circuits
Erythrocyte
Erythrocyte
Microwave technology
Microwave technology
Leukocyte
Leukocyte
Electron tubes
Electron tubes
Basophil
Basophil
Semiconductor materials and devices
Semiconductor materials and devices
Eosynophil
Eosynophil
Dielectric materials and devices
Dielectric materials and devices
Lymphoblast
Lymphoblast
Magnetic materials and devices
Magnetic materials and devices
Lymphocyte
Lymphocyte
Superconducting materials and devices
Superconducting materials and devices
Monocyte
Monocyte
…
…
Neutrophil
Neutrophil
…
…
Practically no “real” examples
With very little extra effort: taxonomies and
thesauri!
F. Corno, L. Farinetti- Politecnico di Torino 56

Taxonomy
Subject-based
classification that
arranges the terms in the
controlled vocabulary
into a hierarchy
Dates back to Carl
Linnæus’s work on
zoological and botanical
classification (18th
century)
F. Corno, L. Farinetti- Politecnico di Torino 57

Taxonomy
Allow related terms to be grouped together
It is clear that “topic
maps” and “XTM” are
related
Easier to classify
documents
Easier to choose
search keywords
F. Corno, L. Farinetti- Politecnico di Torino 58

Taxonomies and metadata
Metadata are
stored as usual
with the resource
The “subject” will
contain only
controlled terms
Controlled terms
belong to a
hierarchy, shared
by all papers
F. Corno, L. Farinetti- Politecnico di Torino 59

Taxonomy example: INSPEC
http://www.theiet.org/publishing/inspec/index.cfm
http://www.theiet.org/publishing/inspec/index.cfm
F. Corno, L. Farinetti- Politecnico di Torino 60

Taxonomy example: INSPEC
F. Corno, L. Farinetti- Politecnico di Torino 61

INSPEC
journal
article
database
F. Corno, L. Farinetti- Politecnico di Torino 62

Taxonomy example: anatomy terms
http://www.cbil.upenn.edu/anatomy.php3
http://www.cbil.upenn.edu/anatomy.php3
F. Corno, L. Farinetti- Politecnico di Torino 63

Taxonomy example
F. Corno, L. Farinetti- Politecnico di Torino 64

Taxonomy example
http://www.acm.org/class/1998/ccs98.html
http://www.acm.org/class/1998/ccs98.html
F. Corno, L. Farinetti- Politecnico di Torino 65

Taxonomy limits
Only two kinds of relationships between terms
Parent = broader term
Child = narrower term
no more in use
synonym
topic navigation maps
difference?
synonym
XML topic map
difference?
F. Corno, L. Farinetti- Politecnico di Torino 66

Thesaurus
Extends taxonomies
subjects are arranged in a hierarchy
Other statements can be made about the
subjects
Two ISO standards
ISO2788 for monolingual thesauri
ISO5964 for multilingual thesauri
F. Corno, L. Farinetti- Politecnico di Torino 67

Thesaurus relationships
BT – broader term
Refers to a term with wider or less specific meaning
Some systems allow multiple BTs for one term, while
others do not
Inverse property: NT- narrower term
A taxonomy only uses BT and NT
SN – scope note
String explaining its meaning within the thesaurus
Useful when the precise meaning of the term is not
obvious from context
F. Corno, L. Farinetti- Politecnico di Torino 68

Thesaurus relationships
USE
Another term that is to be preferred instead of this
term
Implies that the terms are synonymous
Inverse property: UF
TT – top term
The topmost ancestor of this term
The BT of the BT of the BT...
RT – related term
A term that is related to this term, without being a
synonym of it or a broader/narrower term
F. Corno, L. Farinetti- Politecnico di Torino 69

Thesaurus example
http://www.ukat.org.uk/thesaurus/
http://www.ukat.org.uk/thesaurus/
F. Corno, L. Farinetti- Politecnico di Torino 70

Thesaurus example
http://www.swinburne.edu.au/corporate/registrar/rms/keywords.htm
http://www.swinburne.edu.au/corporate/registrar/rms/keywords.htm
F. Corno, L. Farinetti- Politecnico di Torino 71

Thesaurus example
Library of Congress
Subject Heading
http://www.loc.gov/cds/lcsh.html
http://www.loc.gov/cds/lcsh.html
F. Corno, L. Farinetti- Politecnico di Torino 72

Faceted classification
Proposed by
S.R. Ranganathan in the ‘30s
Facets are the different axes along which
documents can be classified
Each facet contains a number of terms
Usually with a thesaurus organization
Usually a term belongs to one facet only
A document is classified by selecting one term
from each facet
F. Corno, L. Farinetti- Politecnico di Torino 73

Faceted classification example
http://flamenco.berkeley.edu/
http://flamenco.berkeley.edu/
F. Corno, L. Farinetti- Politecnico di Torino 74

Advantages
Multi-
dimensionality
Persistence
Scalability
Flexibility
http://freeable.polito.it/
http://freeable.polito.it/
F. Corno, L. Farinetti- Politecnico di Torino 75

Ontology
Model for describing the world that
consists of a set of types, properties, and
relationships
Extends the other subject-based
classification approaches
Has open vocabularies
Has open relationship types (not just BT/NT,
RT and USE/UF)
F. Corno, L. Farinetti- Politecnico di Torino 76

Ontology structure
Concepts
Relationships
Is-a
Other
Instances
F. Corno, L. Farinetti- Politecnico di Torino 77

Ontology example
http://onto.stanford.edu:8080/wino/index.jsp
http://onto.stanford.edu:8080/wino/index.jsp
F. Corno, L. Farinetti- Politecnico di Torino 78

Folksonomy
Internet-mediated
social environments
Tags compiled
through social tagging
Social tagging
Decentralized practice where individuals and
groups create, manage and share tags to
annotate digital resources in an online social
environment
Generally characterized by non-standard
tagging
F. Corno, L. Farinetti- Politecnico di Torino 79

Example (flickr - del.icio.us)
Knowledge Management - Intro 80

Other subject-based techniques
Synonym rings
Connect together a set of terms as being
equivalent for search purpose
Similar to UF/USE relationship of thesauri,
but no preferred term
F. Corno, L. Farinetti- Politecnico di Torino 81

Other subject-based techniques
Authority file
Similar to a synonym ring, but consists of UF/USE
relationships instead of synonym relationships
One term in each synonym ring is indicated as the
preferred term for that subject
e.g. Library of
Congress Name
Authority File
F. Corno, L. Farinetti- Politecnico di Torino 82

Subject-based classification
summary
Terminology is rarely
Terminology is rarely
used in a consistent way
used in a consistent way
Controlled vocabularies
Controlled vocabularies
are thesauri, thesauri are
are thesauri, thesauri are
ontologies, …
ontologies, …
http://www.iesr.ac.uk/profile/vocabs/index.html/#CtrldVocabsList
http://www.iesr.ac.uk/profile/vocabs/index.html/#CtrldVocabsList
F. Corno, L. Farinetti- Politecnico di Torino 83

Subject-based classification
summary
F. Corno, L. Farinetti- Politecnico di Torino 84

Coming next …
More on RDF
More on Ontologies
F. Corno, L. Farinetti- Politecnico di Torino 85

License
This work is licensed under the Creative
Commons Attribution-Noncommercial-
Share Alike 3.0 Unported License.
To view a copy of this license, visit
http://creativecommons.org/licenses/by-
nc-sa/3.0/ or send a letter to Creative
Commons, 171 Second Street, Suite 300,
San Francisco, California, 94105, USA.
F. Corno, L. Farinetti- Politecnico di Torino 86