Lecture slides for the course "Semantic Web: Technologies, Tools, Applications" by Fulvio Corno and Laura Farinetti at Politecnico di Torino, year 2012.
Further information and material: http://elite.polito.it/teaching-mainmenu-69/master-a-phd-mainmenu-94/56-01lhviu-semweb
2. 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
3. “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
5. “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
6. 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
7. 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
16. 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
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17. 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 17
18. Resource and description
this resource is suitable
the title of this for PhD students description
resource is
“Introduction to
the Semantic resource
Web”
this resource
the author of was created on
this resource April 14th, 2009
is L. Farinetti
this resource is
related to
the quality of computer
this resource science,
is high, knowledge
according to F. representation
Corno and metadata
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19. 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)
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20. 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 20
21. Resource and description
this resource is suitable
the title of this for PhD students
resource is
“Introduction to
the Semantic
Web”
this resource
the author of was created on
this resource April 14th, 2009
is L. Farinetti Metadata
this resource is
related to
the quality of computer
this resource Field name = field value
science,
is high, knowledge
according to F. representation
Corno and metadata
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22. Resource and description
Level = PhD students
Title = description
“Introduction to
the Semantic resource
Web”
Date =
Author = 2009-04-14
L. Farinetti
Topic =
{computer
Quality = high science,
knowledge
representation,
Rated by F. Corno
metadata}
F. Corno, L. Farinetti - Politecnico di Torino 22
23. 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)
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24. 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
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25. Common language for describing
resources
F. Corno, L. Farinetti - Politecnico di Torino 25
26. 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
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27. Common language for describing
resources
Author =
L. Farinetti
Diagram:
hasAuthor
URI L.Farinetti
Simple RDF assertion (triple):
triple (hasAuthor, URI, L.Farinetti)
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28. Common language for describing
resources
Author =
L. Farinetti
RDF in XML syntax:
<RDF xmlns=“http://www.w3.org/TR/ … ” >
<Description about=“http://www.polito.it/semweb/intro”>
<Author>L.Farinetti</Author>
</Description>
</RDF>
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29. Common language for field names
Problem Topic = …
Topics, Subject, Subjects,
Level = …
Argument, Arguments
Title = ... Educational level,
destination, suitability, …
Singular / plural
Difficult to clearly
define concept in a Date = …
Author = …
few words
Date of creation, date of
Creator, Maker,
last modification, date of
Contributor …
revision, …
Synonymy Different concepts:
need for more details
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30. 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)
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32. 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
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34. Example of description using
Dublin Core (in RDF)
A paper in the
“Ariadne” journal
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35. 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 35
36. Common language for field values
Problems Level = PhD students
Value type any value?
Value restrictions? list of possible values?
freedom vs shared understanding
Topic =
Quality = high {computer
science,
High, medium, low? knowledge
1 to 5? representation,
any value? metadata}
any value?
any number of values?
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37. Common language for field values
Solution: metadata standards + controlled
vocabularies
Metadata standards
Only some, and partially
Controlled vocabularies
Explicit list of possible values
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38. 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 38
39. Examples from IEEE LOM
F. Corno, L. Farinetti - Politecnico di Torino 39
40. Examples from IEEE LOM
F. Corno, L. Farinetti - Politecnico di Torino 40
41. Examples from IEEE LOM
F. Corno, L. Farinetti - Politecnico di Torino 41
42. … + 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}
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44. Semantically rich descriptions to
support search
http://dictybase.org/db/html/help/GO.html
Topic =
{metabolism, …}
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46. 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
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47. Example: Dublin Core metadata
Metadata of a single paper
F. Corno, L. Farinetti - Politecnico di Torino 47
48. 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 48
49. Search results for “topic maps”
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50. 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
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51. 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-based classification is the approach to
describe subject
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52. 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
d. suckling pigs From The Celestial Emporium of
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"
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53. Subject-based classification
techniques
Controlled
vocabularies
Taxonomies
Thesauri
Faceted classification
Ontologies
Folksonomies
Others
… Most come from library science
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54. 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 54
55. Topic =
Controlled vocabulary {computer
science,
knowledge
representation,
mtadata, RDF,
topic navigation
maps}
Goal topic maps
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 55
56. 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
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57. Problems solved
Synonym
different words with identical or very similar meanings
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58. 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
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59. Problems solved
take (I'll get the drinks)
to get become (she got scared)
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 59
60. Controlled vocabulary examples
Circuit theory Blood
Electronic circuits Cord blood
Microwave technology Erythrocyte
Electron tubes Leukocyte
Semiconductor materials and devices Basophil
Dielectric materials and devices Eosynophil
Magnetic materials and devices Lymphoblast
Superconducting materials and devices Lymphocyte
… Monocyte
Neutrophil
…
Practically no “real” examples
With very little extra effort: taxonomies and
thesauri!
F. Corno, L. Farinetti - Politecnico di Torino 60
61. 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)
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62. 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
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63. 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 63
64. Taxonomy example: INSPEC
http://www.theiet.org/publishing/inspec/index.cfm
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69. Taxonomy example
http://www.acm.org/class/1998/ccs98.html
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70. 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?
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71. 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
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72. 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
Stringexplaining its meaning within the thesaurus
Useful when the precise meaning of the term is not
obvious from context
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73. 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
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74. Thesaurus example
http://www.ukat.org.uk/thesaurus/
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75. Thesaurus example
http://www.swinburne.edu.au/corporate/registrar/rms/keywords.htm
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76. Thesaurus example
Library of Congress
Subject Heading
http://www.loc.gov/cds/lcsh.html
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77. W3C
standard:
SKOS
UK Archival Thesaurus
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78. 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
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80. Advantages
Multi-
dimensionality
Persistence
Scalability
Flexibility
http://freeable.polito.it/
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81. 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 81
82. Ontology structure
Concepts
Relationships
Is-a
Other
Instances
F. Corno, L. Farinetti - Politecnico di Torino 82
83. 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 83
84. 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 84
85. 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 85
86. Subject-based classification
summary
Terminology is rarely used
in a consistent way
Controlled vocabularies
are thesauri, thesauri are
ontologies, …
http://www.iesr.ac.uk/profile/vocabs/index.html/#CtrldVocabsList
F. Corno, L. Farinetti - Politecnico di Torino 86
89. Semantically rich descriptions to
support search
http://dictybase.org/db/html/help/GO.html
Topic =
{metabolism, …}
F. Corno, L. Farinetti - Politecnico di Torino 89
90. Ontologies
An ontology is an explicit description of
a domain
concepts
properties and attributes of concepts
constraints on properties and attributes
individuals (often, but not always)
An ontology defines
a common vocabulary
a shared understanding
F. Corno, L. Farinetti - Politecnico di Torino 90
91. “Ontology engineering”
Defining terms in the domain and relations
among them
defining concepts in the domain (classes)
arranging the concepts in a hierarchy
(subclass-superclass hierarchy)
defining which attributes and properties (slots)
classes can have and constraints on their
values
defining individuals and filling in slot values
F. Corno, L. Farinetti - Politecnico di Torino 91
92. Why develop an ontology?
To share common understanding of the
structure of information
among people
among software agents
To enable reuse of domain knowledge
to avoid “re-inventing the wheel”
to introduce standards to allow interoperability
F. Corno, L. Farinetti - Politecnico di Torino 92
93. An ontology
takes Certificate
1 year
Is_equivalent_to Is_a
takes
Is_a
HNC Award
Is_a
takes Is_a
HND
2 years Diploma
takes
F. Corno, L. Farinetti - Politecnico di Torino 93
94. A more complex ontology
[base.Entity]
Person
Worker
Faculty
Professor
AssistantProfessor
AssociateProfessor
FullProfessor
VisitingProfessor
Lecturer
PostDoc
Assistant
ResearchAssistant
TeachingAssistant
AdministrativeStaff
Director
Chair {Professor}
Dean {Professor}
ClericalStaff
SystemsStaff
Student
UndergraduateStudent
GraduateStudent
F. Corno, L. Farinetti - Politecnico di Torino 94
95. A more complex ontology
Organization
Department
School
University
Program
ResearchGroup
Institute
Publication
Article
TechnicalReport
JournalArticle
ConferencePaper
UnofficialPublication
Book
Software
Manual
Specification
Work
Course
Research
Schedule
F. Corno, L. Farinetti - Politecnico di Torino 95
96. A more complex ontology
Relation Argument 1 Argument 2
======================================================
publicationAuthor Publication Person
publicationDate Publication .DATE
publicationResearch Publication Research
softwareVersion Software .STRING
softwareDocumentation Software Publication
teacherOf Faculty Course
teachingAssistantOf TeachingAssistant Course
takesCourse Student Course
age Person .NUMBER
emailAddress Person .STRING
head Organization Person
undergraduateDegreeFrom Person University
mastersDegreeFrom Person University
doctoralDegreeFrom Person University
advisor Student Professor
subOrganization Organization Organization ………..
F. Corno, L. Farinetti - Politecnico di Torino 96
97. Example of ontology engineering
chair
F. Corno, L. Farinetti - Politecnico di Torino 97
98. Example of ontology engineering
1.A piece of furniture consisting of a seat, legs, back, and often
arms, designed to accommodate one person.
2.A seat of office, authority, or dignity, such as that of a bishop.
a.An office or position of authority, such as a professorship.
b.A person who holds an office or a position of authority,
such as one who presides over a meeting or administers a
department of instruction at a college; a chairperson.
3.The position of a player in an orchestra.
4.Slang. The electric chair.
5.A seat carried about on poles; a sedan chair.
6.Any of several devices that serve to support or secure, such as
a metal block that supports and holds railroad track in position.
chair
F. Corno, L. Farinetti - Politecnico di Torino 98
99. Example of ontology engineering
A piece of furniture consisting of a seat, legs, back,
and often arms, designed to accommodate one
person.
chair
F. Corno, L. Farinetti - Politecnico di Torino 99
100. Example of ontology engineering
chair seat stool bench
F. Corno, L. Farinetti - Politecnico di Torino 100
101. Example of ontology engineering
Something I can sit on
???
chair seat stool bench
F. Corno, L. Farinetti - Politecnico di Torino 101
102. Example of ontology engineering
Something I can sit on
“sittable”
chair seat stool bench
F. Corno, L. Farinetti - Politecnico di Torino 102
103. Example of ontology engineering
Something I can sit on
“sittable”
table
chair seat stool bench
F. Corno, L. Farinetti - Politecnico di Torino 103
104. Example of ontology engineering
Something I can sit on
“sittable”
Something designed for sitting
“for_sitting”
table
chair seat stool bench
F. Corno, L. Farinetti - Politecnico di Torino 104
105. Ontology structure
“sittable”
“for_sitting”
table
chair seat stool bench
F. Corno, L. Farinetti - Politecnico di Torino 105
106. Concepts
Synthetic title
Furniture to sit on
“sittable” Definition
Shorthand name Some piece of furniture that can
be used to sit on, either by
design or by its shape.
F. Corno, L. Farinetti - Politecnico di Torino 106
107. Internationalization
Synthetic title
Furniture to sit on
Furniture to sit on
Furniture to sit on
Furniture to sit on
Furniture to sit on
Furniture to sit on
Furniture to sit on
“sittable” Definition
Shorthand name Some piece of furniture that can
Some piece of furniture that can
Some piece of furniture that can
beSome topieceoffurniture that can
used pieceon, furniture
sit of
beSome topieceofeither by that can
used piece on, furniture that can
beSome tosit on, either by that can
used tosit on,either by
beSome its of furniture
design usedtosit shape.
beused by its on,either by
designor by tosit shape.
beused tosit on,either by
designor by its shape.
be used sit on,either by
designor by its shape.
either by
designor by its shape.
or by its shape.
design or by its shape.
design or
F. Corno, L. Farinetti - Politecnico di Torino 107
108. Relationships
material
room
is_a
is_a
is_a “sittable”
wood
classroom is_a
dining room “for_sitting” is_a
is_a table
is_a is_a is_a
chair seat stool bench
F. Corno, L. Farinetti - Politecnico di Torino 108
109. Relationships
made_of
material
room
is_a
is_a
is_a “sittable”
wood
classroom is_a
made_of
dining room “for_sitting” is_a
is_a table
is_a is_a is_a
chair seat stool bench
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110. Ontology building blocks
Ontologies generally describe:
Individuals
the basic or “ground level” objects
Classes
sets, collections, or types of objects
Attributes
properties, features, characteristics, or parameters
that objects can have and share
Relationships
ways that objects can be related to one another
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111. Individuals
Also known as “instances”
can be concrete objects
animals
molecules
trees
or abstract objects
numbers
words
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112. Concepts
Also known as “Classes”
abstract groups, sets, or collections of objects
They may contain
individuals
otherclasses
a combination of both
Examples
Person: the class of all people
Vehicle: the class of all vehicles
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113. Concepts
Can be defined extensionally …
By defining every object that falls under the definition
of the concept
A class C is extensionally defined if and only if for
every class C', if C' has exactly the same members of
C, C and C' are identical
E.g.: DayOfWeek = {Monday, Tuesday, Wednesday,
Thursday, Friday, Saturday, Sunday}
… or intensionally
By defining the necessary and sufficient conditions for
belonging to the concept
E.g.: “bachelor” is an “unmarried man”
F. Corno, L. Farinetti - Politecnico di Torino 113
114. Concepts
Defined by
Name: any identifier, usually carefully chosen
Definition: describes the well agreed meaning
of the concept, in a human readable form
Terms (Lexicon): list of terms (synonyms, etc.)
usually adopted to identify the concept
F. Corno, L. Farinetti - Politecnico di Torino 114
115. Subsumption
A concept (class) can subsume / be
subsumed by any other class
Subsumption is used to establish class
hierarchies
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116. Class partition
A set of related classes and associated
rules that allow objects to be placed into
the appropriate class
GEOMETRIC
FIGURE
GEOMETRIC TWO
POINT DIMENSIONAL
FIGURE
ONE
DIMENSIONAL
FIGURE
F. Corno, L. Farinetti - Politecnico di Torino 116
117. Class partition
Disjoint partition
A disjoint partition rule guarantees that a
single instance of a class cannot be in more
than one sub-classes
VEHICLE
E.g. one specific truck
cannot be in both
4-axle and TRUCK CAR
6-axle classes
6-AXLE 4-AXLE
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118. Class partition
Exhaustive partition
every concrete object in the super-class is an
instance of at least one of the partition
classes
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119. Attributes
Describe specific features
Can be complex (e.g.: list of values)
Defined for a class/concept (e.g. car)
Examples:
number-of-doors: 4
number-of-wheels: 4
engine: {3.0L,4.0L}
F. Corno, L. Farinetti - Politecnico di Torino 119
120. Relationships
Attributes that relate two or more concepts
two concepts → binary relationship
three concepts → ternary relationship
Domain
the concept(s) from which the relationship
departs
Range
the concept(s) to which the relationship
applies
F. Corno, L. Farinetti - Politecnico di Torino 120
121. Relationships
Examples
Car(MiniMinor) → individual definition
Car(Mini) → individual definition
Successor(Mini,MiniMinor) → relationship
domain range
F. Corno, L. Farinetti - Politecnico di Torino 121
122. Commonly used relationships
Subsumption
the most important
is-superclass-of
usually denoted by its inverse is-a
(is-subclass-of)
Meronymy
is-part-of
describes how object are combined together
to form composite objects
F. Corno, L. Farinetti - Politecnico di Torino 122
124. Ontology alignment
http://www.webology.ir/2006/v3n3/a28.html
F. Corno, L. Farinetti - Politecnico di Torino 124
125. 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 125