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Digital Enterprise Research Institute
Review and Alignment of Tag Ontologies for Semantically-Linked Data in Collaborative Tagging Spaces
Hak-Lae Kim, Alexandre Passant, John G. Brelsin, Simon Scerri and Stefan Decker
ICSC, San Fransisco, August 8, 2008
IPEEC 2008 Daejeon, Korea/ 30th June 2008
© Copyright 2008 Digital Enterprise Research Institute. All rights reserved.
www.deri.org
The State of the Art in Tag Ontologies: A Semantic Model for Tagging and Folksonomies
Haklae Kim, Simon Scerri, John G. Breslin, Stefan Decker and Honggee Kim
1
DC 2008 Berlin, Germany, September 25th , 2008
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This Presentation
I. Background
II. Tagging Models and Tag Ontologies
III. Comparison of Existing Tag Ontologies
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I. Background
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HOW MANY MODERN SOCIAL SITES DO NOT SUPPORT TAGGING?
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Easy-to-use
Created by user participation
Collective ‘intelligence’
Emergent semantics
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II. Tagging Models and Tag Ontologies
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• Core Concepts – Tag: a word or phrase that is recognisable
by people and computers
– Resource: a thing to be tagged, identifiable by a URI or a similar naming service
– Tagger: someone/thing doing the tagging, e.g. user of an application
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TAGGING We tag a specific resource with keywords
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Tagging := (user, tag, resource)
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‘Picture Credit: Maarten Janssen, 2006’
Personal tagging (non-social)
Folksonomies: collaborative tagging
Social interaction across different systems?
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Tagging := (user, tag, resource)*
Folksonomy := (Tag Set, User Group, Source, Tagging, Occurrences )
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CURRENTLY
We don’t know the relationships
User assigns a Tag to a Resource in a specific system.
System
Tagging
No standard to enable reuse among different systems
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STRUCTURE The structure of Tagging elements (People, Tags, Resources, ...) can be defined in human-readable AND machine-processable ways.
<tag:name> bear
</tag:name>
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MEANING The MEANING of Tagging elements (People, Tags, Resources, ...) can also be defined in a machine-processable way.
bear Bear
toy
BEAR bears
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ONTOLOGIES
Enable:
• Increased Knowledge Representation Sophistication
• Machine Processable Representation
• Facilitation of Knowledge Exchange
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Allow us to represent tagging elements and their relationships at a semantic level.
“A little semantics goes a LONG way”
System
Tagging
:hasTagging
TAG ONTOLOGIES
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III. Comparison of Existing Tag Ontologies
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Selected Tag Ontologies
Ontology URL Update Applications
Gruber - - -
Newman http://www.holygoat.co.uk/projects/tags/ Nov ’05 http://revyu.com
Knerr http://code.google.com/p/tagont/ Jan ’07 -
Echarte http://eslomas.com/tagontology-1.owl ’07 -
SCOT http://scot-project.org Jun ’08 http://int.ere.st http://relaxseo.com
http://openlinksw.com MOAT http://moat-project.org Feb ’08 http://openlinksw.com
lord.info
NAO http://www.semanticdesktop.org/ontologies/nao/ Aug ‘07 Nepomuk
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Ontology Class Comparison (selection)
Model Resource Tag Tagging User Group Source
Gruber Object Tag Tagging Tagger Source
Newman rdfs:Resource :Tag :Tagging foaf:Agent
Knerr rdfs:Resource :Tag :Tagging :Tagger foaf:Group :ServiceDomain
Echarte :Resource :Tag :Annotation :User :Source
SCOT sioc:Item :Tag tags:Tagging sioc:User sioc:Usergroup sioc:Site
MOAT rdfs:Resource tag:Tag tags:Tagging foaf:Agent
NAO rdfs:Resource :Tag :Party
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Ontology Property Comparison (selection)
Resource User Tag
Resource tags:taggedWithTag scot:hasTag nao:hasTag
User
Tag
tags: isTagOf scot:tagOf nao:isTagFor ec:hasRelatedResource
scot:usedBy nao:creator
tags:equivalentTag tags:relatedTag scot:aggregatedTag scot:spellingVariant scot:delimited tagont:sameTag ec:hasTag
Table 4. Object Type Properties. The table shows relationships between core concepts, interpreted as domain (row) – property – range (column)
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Inclinations and Representation Levels of Selected Ontologies
Folksonomy Feature Support
Tagging Activity Feature Support
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MOAT (Meaning Of A Tag)
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http://en.wikipedia.org/wiki/Watch
moat:tagMeaning moat:tagMeaning
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SCOT (Social Semantic Cloud of Tags)
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Linking among Tag Ontologies
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Folksonomies: Linking Tag Clouds
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Conclusion
Tagging := (User, Tag, Resource)
Folksonomy := (Users, Tags, Source, Tagging*, Occurrences*)
• Existing Tag Ontologies do not represent Collaborative Tagging well enough on their own.
• SCOT + MOAT + additional vocabularies (SIOC, FOAF, DC, etc.) provide sufficient representation for Collaborative Tagging & Folksonomies
• Thus, Tag Ontologies provide the possibility for machine-processable representations that can be shared across social tagging systems.