introduction to the semantic webelite.polito.it/files/courses/01lhv/2009/1-knowledge.pdf ·...

Introduction tothe Semantic Web

F. Corno, L. Farinetti - Politecnico di Torino 2

Semantic Web

Web second generationWeb 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/


“Official” introductionThe 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” introductionThe Semantic Web is about two thingsIt 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 principlesThe Semantic Web is the Web

Same base technologies, evolutionaryDecentralized (incomplete, inconsistent)

Provide explicit statements regarding web resources

Authors, original information providersIntermediaries (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)


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

Metadata andMetadata Standards


Resource and description

ResourceContent, format, …Access method dependent on format (I can read it if I “know” its language)

Resource descriptionIndependent of the format (I can read “people’s comments” about the resource… provided that I know the language in which the comment is written)


Resource and descriptiondescription

resource

this resource was created on April 14th, 2009

the title of this resource is

“Introduction to the Semantic

Web”

the author of this resourceis 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


Resource and descriptionResource

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 informationHundreds of human languagesHundreds of computer languages (other word for formats)


Resource and descriptionResource 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) ???

FeasibleSmaller amount of information, possibly newSolution: define a standard language for writing comments (“metadata” in semantic web terminology)


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 resourceis 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


Resource and descriptiondescription

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. CornoRated by F. Corno


Semantic Web main tasksMetadata annotation

Description of resources using standard languages

SearchRetrieve relevant information according to user’s query / interest / intentionUse metadata (and possibly content) in a “smart” way (i.e. “reasoning” about the meaning of annotations)


Meaningful metadata annotations

Common language for describing resourcesResource description standards

Common language for description field namesMetadata standards

Common language for description field valuesMetadata standards + controlled vocabularies

Semantically rich descriptions to support searchKnowledge representation techniques, ontologies


Common language for describing resources



Resource Description Framework (RDF)Resource = URI (retrievable, or not)RDF is structured in statements

A statement is a tripleSubject – predicate – objectSubject: a resourcePredicate: a verb / property / relationshipObject: a resource, or a literal string



Diagram:

Simple RDF assertion (triple):

triple (hasAuthor, URI, L.Farinetti)triple (hasAuthor, URI, L.Farinetti)

URIURI L.FarinettiL.FarinettihasAuthor




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>

<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

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


Common language for field names

Solution: metadata standardsMany standardization bodies are involvedStandards 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 Web15 elements, or categories, general enough to describe most of the published resourcesExtra 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

ProblemsValue type

Title =“Introduction to

the Semantic Web”

type = string

Date =2009-04-14

type = date


type = string“standard” format? Laura Farinetti, FarinettiLaura, Farinetti L., …



ProblemsValue typeValue restrictions?

freedom vs shared understanding

Quality = high

High, medium, low?1 to 5?any value?

Level = PhD students

any value?list of possible values?


knowledge representation,

metadata}

any value?any number of values?



Solution: metadata standards + controlled vocabulariesMetadata standards

Only some, and partiallyControlled 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 classificationMetadata field values arerestricted to a list of terms(selected by experts)


informatics, knowledge

representation, metadata}


Semantically rich descriptions to support search


Semantically rich descriptions to support search

http://dictybase.org/db/html/help/GO.htmlhttp://dictybase.org/db/html/help/GO.html

Topic ={metabolism, …}

KnowledgeRepresentation


Need for knowledge representation

Semantically rich descriptions need “understanding” the meaning of a resource and the domain related to the resource

Disambiguation of termsShared agreement on meaningsDescription of the domain, with concepts and relations among concepts


Example: Dublin Core metadataMetadata of a single paper


ProblemsTitle usually offers good clues, but

it does not necessarily mention all names of all subjects the user is interested init 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 mapsIf an author forgets to list “topic maps”, his paper will never be found


Subject-based classificationAny 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 classificationImportant: 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 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


Subject-based classification techniques

Controlled vocabulariesTaxonomiesThesauriFaceted classificationOntologiesFolksonomiesOthers

… Most come from library science


Controlled vocabularyA closed list of named subjects, which can be used for classificationComposed of terms: particular name for a particular concept

similar to keywordsTerms are not concepts

A single term may be the name of one or more conceptsA single concept may have multiple namesAmbiguity avoided by forbidding duplicate terms


Controlled vocabulary

GoalPrevent authors from defining terms that are meaningless, too broad or too narrowPrevent 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”)


knowledge representation, mtadata, RDF,

topic navigation maps}topic maps


Controlled vocabularyReduce 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

Synonymdifferent words with identical or very similar meanings


Problems solved


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


Problems solved


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


Controlled vocabulary examples

Practically no “real” examplesWith very little extra effort: taxonomies and thesauri!

Circuit theoryElectronic circuitsMicrowave technology Electron tubes Semiconductor materials and devicesDielectric materials and devicesMagnetic materials and devicesSuperconducting materials and devices…

Circuit theoryElectronic circuitsMicrowave technology Electron tubes Semiconductor materials and devicesDielectric materials and devicesMagnetic materials and devicesSuperconducting materials and devices…

BloodCord bloodErythrocyte Leukocyte BasophilEosynophilLymphoblastLymphocyte MonocyteNeutrophil…

BloodCord bloodErythrocyte Leukocyte BasophilEosynophilLymphoblastLymphocyte MonocyteNeutrophil…


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)


TaxonomyAllow related terms to be grouped together

It is clear that “topic maps” and “XTM” are relatedEasier to classify documentsEasier to choose search keywords


Taxonomies and metadata

Metadata are stored as usual with the resourceThe “subject” will contain only controlled termsControlled terms belong to a hierarchy, shared by all papers


Taxonomy example: INSPEC

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


Taxonomy example: INSPEC


INSPEC journal article database

INSPEC journal article database


Taxonomy example: anatomy terms

http://www.cbil.upenn.edu/anatomy.php3http://www.cbil.upenn.edu/anatomy.php3


Taxonomy example


Taxonomy example

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


Taxonomy limitsOnly two kinds of relationships between terms

Parent = broader termChild = narrower term

topic navigation mapssynonym

no more in use

difference?

synonymXML topic map

difference?


Thesaurus

Extends taxonomiessubjects are arranged in a hierarchy

Other statements can be made about the subjectsTwo ISO standards

ISO2788 for monolingual thesauriISO5964 for multilingual thesauri


Thesaurus relationshipsBT – broader term

Refers to a term with wider or less specific meaningSome systems allow multiple BTs for one term, while others do notInverse property: NT - narrower termA taxonomy only uses BT and NT

SN – scope noteString explaining its meaning within the thesaurusUseful when the precise meaning of the term is not obvious from context


Thesaurus relationshipsUSE

Another term that is to be preferred instead of this termImplies that the terms are synonymousInverse property: UF

TT – top termThe topmost ancestor of this termThe BT of the BT of the BT...

RT – related termA 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/http://www.ukat.org.uk/thesaurus/


Thesaurus example

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


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

Thesaurus exampleLibrary of Congress Subject Heading


Faceted classification

Proposed by S.R. Ranganathan in the ‘30sFacets are the different axes along which documents can be classifiedEach facet contains a number of terms

Usually with a thesaurus organizationUsually a term belongs to one facet onlyA document is classified by selecting one term from each facet


Faceted classification example

http://flamenco.berkeley.edu/http://flamenco.berkeley.edu/


Advantages

Multi-dimensionalityPersistenceScalabilityFlexibility

http://freeable.polito.it/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 vocabulariesHas open relationship types (not just BT/NT, RT and USE/UF)


Ontology structure

ConceptsRelationships

Is-aOther

Instances


Ontology example

http://onto.stanford.edu:8080/wino/index.jsphttp://onto.stanford.edu:8080/wino/index.jsp


FolksonomyInternet-mediatedsocial environmentsTags compiledthrough social taggingSocial tagging

Decentralized practice where individuals and groups create, manage and share tags to annotate digital resources in an online social environmentGenerally characterized by non-standard tagging

Knowledge Management - Intro 80

Example (flickr - del.icio.us)


Other subject-based techniques

Synonym ringsConnect together a set of terms as being equivalent for search purposeSimilar to UF/USE relationship of thesauri, but no preferred term


Other subject-based techniques

Authority fileSimilar to a synonym ring, but consists of UF/USE relationships instead of synonym relationshipsOne term in each synonym ring is indicated as the preferred term for that subjecte.g. Library ofCongress NameAuthority File


Subject-based classification summary

Terminology is rarely used in a consistent way

Controlled vocabularies are thesauri, thesauri are ontologies, …

Terminology is rarely used in a consistent way

Controlled vocabularies are thesauri, thesauri are ontologies, …

http://www.iesr.ac.uk/profile/vocabs/index.html/#CtrldVocabsListhttp://www.iesr.ac.uk/profile/vocabs/index.html/#CtrldVocabsList


Subject-based classification summary


Coming next …

More on RDFMore on Ontologies

introduction to the semantic webelite.polito.it/files/courses/01lhv/2009/1-knowledge.pdf ·...

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