Introduction to Semantic Web, Metadata, Knowledge Representation, Ontologies

Introduction to
the Semantic Web

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

Example


“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

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,…”


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’

1989:
WWW
original
proposal


Technology stack (old: pre-2008)


Technology stack (current: 2008)


The real world

Not
yet...!


The real world

Not
Not always yet...!
necessary...


Current “hot” topics


Metadata and
Metadata Standards

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


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)


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


 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)


 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)


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


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}


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)

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


Common language for describing
resources


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


resources
Author =
L. Farinetti

 Diagram:
hasAuthor
URI L.Farinetti

 Simple RDF assertion (triple):
triple (hasAuthor, URI, L.Farinetti)


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>


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

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)


Metadata standards examples


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


DC metadata element set


Example of description using
Dublin Core (in RDF)

 A paper in the
“Ariadne” journal

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., …


 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?


 Solution: metadata standards + controlled
vocabularies
 Metadata standards
 Only some, and partially
 Controlled vocabularies
 Explicit list of possible values


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


… + 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}

Semantically rich descriptions to
support search


support search
http://dictybase.org/db/html/help/GO.html

Topic =
{metabolism, …}


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


Example: Dublin Core metadata
Metadata of a single paper


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

Search results for “topic maps”


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


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

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"

techniques
 Controlled
vocabularies
 Taxonomies
 Thesauri
 Faceted classification
 Ontologies
 Folksonomies
 Others

 … Most come from library science

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


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”)

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


Problems solved

Synonym
different words with identical or very similar meanings


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


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

student and pupil (noun)
buy and purchase (verb)
sick and ill (adjective)


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!

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)

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


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

Taxonomy example: INSPEC

http://www.theiet.org/publishing/inspec/index.cfm

Taxonomy example: INSPEC


INSPEC
journal
article
database


Taxonomy example: anatomy terms
http://www.cbil.upenn.edu/anatomy.php3


Taxonomy example


Taxonomy example

http://www.acm.org/class/1998/ccs98.html


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?


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


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


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


Thesaurus example

http://www.ukat.org.uk/thesaurus/

Thesaurus example

http://www.swinburne.edu.au/corporate/registrar/rms/keywords.htm

Thesaurus example
 Library of Congress
Subject Heading

http://www.loc.gov/cds/lcsh.html

W3C
standard:
SKOS

UK Archival Thesaurus


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

Faceted classification example

http://flamenco.berkeley.edu/


Advantages
 Multi-
dimensionality
 Persistence
 Scalability
 Flexibility

http://freeable.polito.it/


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)

Ontology structure

 Concepts
 Relationships
Is-a
Other
 Instances


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

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


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


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


summary


support search
http://dictybase.org/db/html/help/GO.html

Topic =
{metabolism, …}


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


“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


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


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

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

Organization
Department
School
University
Program
ResearchGroup
Institute
Publication
Article
TechnicalReport
JournalArticle
ConferencePaper
UnofficialPublication
Book
Software
Manual
Specification
Work
Course
Research
Schedule


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 ………..


Example of ontology engineering

chair


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



A piece of furniture consisting of a seat, legs, back,
and often arms, designed to accommodate one
person.

chair



chair seat stool bench



Something I can sit on

???





“sittable”





“sittable”

table




“sittable”

Something designed for sitting

“for_sitting”

table



Ontology structure

“sittable”

“for_sitting”

table



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.


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
either by
or by its shape.
design or by its shape.
design or


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


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


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

Individuals
 Also known as “instances”
 can be concrete objects
 animals
 molecules
 trees

 or abstract objects
 numbers
 words


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


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”

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


Subsumption
 A concept (class) can subsume / be
subsumed by any other class
 Subsumption is used to establish class
hierarchies


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


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


Class partition
 Exhaustive partition
 every concrete object in the super-class is an
instance of at least one of the partition
classes


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}


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

Relationships
 Examples
 Car(MiniMinor) → individual definition
 Car(Mini) → individual definition
 Successor(Mini,MiniMinor) → relationship

domain range


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

Example

http://www.yeastgenome.org/help/GO.html

Ontology alignment
http://www.webology.ir/2006/v3n3/a28.html


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.


Introduction to Semantic Web, Metadata, Knowledge Representation, Ontologies

More Related Content

Viewers also liked

Similar to Introduction to Semantic Web, Metadata, Knowledge Representation, Ontologies

Recently uploaded

Introduction to Semantic Web, Metadata, Knowledge Representation, Ontologies