Introduction to
the Semantic Web
F. Corno, L. Farinetti - Politecnico di Torino 2
Semantic Web
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!!!
http://www.w3.org/2001/sw/
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“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
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Example
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“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
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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,…”
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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‟
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1989:
WWW
original
proposal
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Technology stack (old: pre-2008)
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Technology stack (current: 2008)
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The real world
Not
yet...!
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The real world
Not
yet...!Not always
necessary...
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The real world
Not
yet...!Not always
necessary...
Information
retrieval
Statistics
Current “hot” topics
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Metadata and
Metadata Standards
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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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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)
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Resource and description
description
resource
this resource
was created on
April 14th, 2009
the title of this
resource is
“Introduction to
the Semantic
Web”
the author of
this resource
is L. Farinetti
this resource is
related to
computer
science,
knowledge
representation
and metadata
the quality of
this resource
is high,
according to F.
Corno
this resource is suitable
for PhD students
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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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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 21
Resource and description
this resource
was created on
April 14th, 2009
the title of this
resource is
“Introduction to
the Semantic
Web”
the author of
this resource
is L. Farinetti
this resource is
related to
computer
science,
knowledge
representation
and metadata
the quality of
this resource
is high,
according to F.
Corno
this resource is suitable
for PhD students
Metadata
Field name = field value
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Resource and description
description
resource
Date =
2009-04-14
Title =
“Introduction to
the Semantic
Web”
Author =
L. Farinetti
Topic =
{computer
science,
knowledge
representation,
metadata}
Quality = high
Level = PhD students
Rated by F. Corno
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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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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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Common language for describing
resources
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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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Common language for describing
resources
Diagram:
Simple RDF assertion (triple):
triple (hasAuthor, URI, L.Farinetti)
URI L.FarinettihasAuthor
Author =
L. Farinetti
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Common language for describing
resources
RDF in XML syntax:
Author =
L. Farinetti
<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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Common language for field names
Title = ...
Problem
Author = …
Creator, Maker,
Contributor …
Synonymy
Topic = …
Topics, Subject, Subjects,
Argument, Arguments
Singular / plural
Level = …
Difficult to clearly
define concept in a
few words
Educational level,
destination, suitability, …
Date = …
Date of creation, date of
last modification, date of
revision, …
Different concepts:
need for more details
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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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Metadata standards examples
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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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DC metadata element set
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Example of description using
Dublin Core (in RDF)
A paper in the
“Ariadne” journal
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Common language for field values
Problems
Value type
Title =
“Introduction to
the Semantic
Web”
type = string
Date =
2009-04-14
type = date
Author =
L. Farinetti
type = string
“standard” format?
Laura Farinetti, Farinetti
Laura, Farinetti L., …
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Common language for field values
Problems
Value type
Value restrictions?
freedom vs shared understanding
Quality = high
High, medium, low?
1 to 5?
any value?
Level = PhD students
any value?
list of possible values?
Topic =
{computer
science,
knowledge
representation,
metadata}
any value?
any number of values?
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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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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
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Examples from IEEE LOM
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Examples from IEEE LOM
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Examples from IEEE LOM
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… + controlled vocabularies
A closed list of named subjects, which can
be used for classification
Metadata field values are
restricted to a list of terms
(selected by experts)
Topic =
{computer
science,
informatics,
knowledge
representation,
metadata}
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Semantically rich descriptions to
support search
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Semantically rich descriptions to
support search
http://dictybase.org/db/html/help/GO.html
Topic =
{metabolism, …}
Knowledge
Representation
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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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Example: Dublin Core metadata
Metadata of a single paper
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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
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Search results for “topic maps”
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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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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 themMetadata describe objects
Subject-based classification is the approach to describe subject
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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 pigse. mermaids f. fabulous ones g. stray dogs h. those that are included in this classificationi. 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"
From The Celestial Emporium of Benevolent Knowledge, Borges
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Subject-based classification
techniques
Controlledvocabularies
Taxonomies
Thesauri
Faceted classification
Ontologies
Folksonomies
Others
… Most come from library science
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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
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Controlled vocabulary
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”)
Topic =
{computer
science,
knowledge
representation,
mtadata, RDF,
topic navigation
maps}topic maps
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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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Problems solved
Synonym
different words with identical or very similar meanings
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Problems solved
Synonym
different words with identical or very similar meanings
close“Will you please close that door!”
“The tiger was now so close that I could smell it...”
pupilstudent
opening in the iris of the eye
axes
('æk.səz) plural of axe
('æk.siz) plural of axis
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Problems solved
Synonym
different words with identical or very similar meanings
student and pupil (noun)
buy and purchase (verb)
sick and ill (adjective)
to get
take (I'll get the drinks)
become (she got scared)
wood
understand (I get it)
a piece of a tree
a geographical area with many trees
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Controlled vocabulary examples
Practically no “real” examples
With very little extra effort: taxonomies and
thesauri!
Circuit theory
Electronic circuits
Microwave technology
Electron tubes
Semiconductor materials and devices
Dielectric materials and devices
Magnetic materials and devices
Superconducting materials and devices
…
Blood
Cord blood
Erythrocyte
Leukocyte
Basophil
Eosynophil
Lymphoblast
Lymphocyte
Monocyte
Neutrophil
…
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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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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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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
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Taxonomy example: INSPEC
http://www.theiet.org/publishing/inspec/index.cfm
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Taxonomy example: INSPEC
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INSPEC
journal
article
database
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Taxonomy example: anatomy terms
http://www.cbil.upenn.edu/anatomy.php3
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Taxonomy example
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Taxonomy example
http://www.acm.org/class/1998/ccs98.html
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Taxonomy limits
Only two kinds of relationships between terms
Parent = broader term
Child = narrower term
topic navigation mapssynonym
no more in use
difference?
synonymXML topic map
difference?
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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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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
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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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Thesaurus example
http://www.ukat.org.uk/thesaurus/
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Thesaurus example
http://www.swinburne.edu.au/corporate/registrar/rms/keywords.htm
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Thesaurus example
Library of Congress
Subject Heading
http://www.loc.gov/cds/lcsh.html
W3C
standard:
SKOS
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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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Faceted classification example
http://flamenco.berkeley.edu/
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Advantages
Multi-
dimensionality
Persistence
Scalability
Flexibility
http://freeable.polito.it/
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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)
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Ontology structure
Concepts
Relationships
Is-a
Other
Instances
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Folksonomy
Internet-mediatedsocial environments
Tags compiledthrough 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
Knowledge Management - Intro 84
Example (flickr - del.icio.us)
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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
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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
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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
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Subject-based classification
summary
Ontologies
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Semantically rich descriptions to
support search
http://dictybase.org/db/html/help/GO.html
Topic =
{metabolism, …}
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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
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“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
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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
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An ontology
HNC
HND
Certificate
Diploma
Award
2 years
1 year
Is_a
Is_a
Is_a
Is_a
takes
takes
takes
takes
Is_equivalent_to
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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
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A more complex ontologyOrganization
Department School University Program ResearchGroup Institute
Publication Article
TechnicalReport JournalArticle ConferencePaper
UnofficialPublication Book Software Manual Specification
Work Course Research
Schedule
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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 PersonundergraduateDegreeFrom Person UniversitymastersDegreeFrom Person UniversitydoctoralDegreeFrom Person University advisor Student Professor subOrganization Organization Organization ………..
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Example of ontology engineering
chair
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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
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Example of ontology engineering
A piece of furniture consisting of a seat, legs, back,
and often arms, designed to accommodate one
person.
chair
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Example of ontology engineering
chair seat stool bench
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Example of ontology engineering
Something I can sit on
chair seat stool bench
Something I can sit on
???
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chair seat stool bench
Something I can sit on
“sittable”
Example of ontology engineering
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chair seat stool bench
table
Example of ontology engineering
Something I can sit on
“sittable”
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Example of ontology engineering
Something I can sit on
chair seat stool bench
“for_sitting”
table
“sittable”
Something designed for sitting
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Ontology structure
chair seat stool bench
“for_sitting”
table
“sittable”
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Concepts
Some piece of furniture that can
be used to sit on, either by
design or by its shape.
Furniture to sit on
Shorthand name
Synthetic title
Definition“sittable”
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Internationalization
Some piece of furniture that can
be used to sit on, either by
design or by its shape.
Furniture to sit on
Shorthand name
Synthetic title
Definition
Furniture to sit onFurniture to sit onFurniture to sit onFurniture to sit onFurniture to sit onFurniture to sit on
Some piece of furniture that can
be used to sit on, either by
design or by its shape.
Some piece of furniture that can
be used to sit on, either by
design or by its shape.
Some piece of furniture that can
be used to sit on, either by
design or by its shape.
Some piece of furniture that can
be used to sit on, either by
design or by its shape.
Some piece of furniture that can
be used to sit on, either by
design or by its shape.
Some piece of furniture that can
be used to sit on, either by
design or by its shape.
“sittable”
F. Corno, L. Farinetti - Politecnico di Torino 109
Relationships
chair seat stool bench
“for_sitting”
table
“sittable”
is_ais_a is_a
is_a
is_a
is_a
roommaterial
wood
is_a
classroom
dining room
is_ais_a
F. Corno, L. Farinetti - Politecnico di Torino 110
Relationships
chair seat stool bench
“for_sitting”
table
“sittable”
is_ais_a is_a
is_a
is_a
is_a
roommaterial
wood
is_a
classroom
dining room
is_ais_a
made_of
made_of
F. Corno, L. Farinetti - Politecnico di Torino 111
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
F. Corno, L. Farinetti - Politecnico di Torino 112
Individuals
Also known as “instances”
can be concrete objects
animals
molecules
trees
or abstract objects
numbers
words
F. Corno, L. Farinetti - Politecnico di Torino 113
Concepts
Also known as “Classes”
abstract groups, sets, or collections of objects
They may contain
individuals
other classes
a combination of both
Examples
Person: the class of all people
Vehicle: the class of all vehicles
F. Corno, L. Farinetti - Politecnico di Torino 114
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 115
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 116
Subsumption
A concept (class) can subsume / be
subsumed by any other class
Subsumption is used to establish class
hierarchies
F. Corno, L. Farinetti - Politecnico di Torino 117
Class partition
A set of related classes and associated
rules that allow objects to be placed into
the appropriate class
GEOMETRICFIGURE
GEOMETRICPOINT
TWODIMENSIONAL
FIGUREONE
DIMENSIONALFIGURE
F. Corno, L. Farinetti - Politecnico di Torino 118
Class partition
Disjoint partition
A disjoint partition rule guarantees that a
single instance of a class cannot be in more
than one sub-classes
E.g. one specific truck
cannot be in both
4-axle and
6-axle classes
VEHICLE
CARTRUCK
6-AXLE 4-AXLE
F. Corno, L. Farinetti - Politecnico di Torino 119
Class partition
Exhaustive partition
every concrete object in the super-class is an
instance of at least one of the partition
classes
F. Corno, L. Farinetti - Politecnico di Torino 120
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 121
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 122
Relationships
Examples
Car(MiniMinor) → individual definition
Car(Mini) → individual definition
Successor(Mini,MiniMinor) → relationship
domain range
F. Corno, L. Farinetti - Politecnico di Torino 123
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 124
Example
http://www.yeastgenome.org/help/GO.html
F. Corno, L. Farinetti - Politecnico di Torino 125
http://www.webology.ir/2006/v3n3/a28.html
Ontology alignment
F. Corno, L. Farinetti - Politecnico di Torino 126
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