Integrating Folksonomies with the Semantic Web
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- 1. Integrating Folksonomies with the Semantic Web
2.
- folksonomies
- folksonomies
- MottaSpecia: folksonomies Flickrdel.icio.us
3. Tagging 4. TaggingWeb 2.0 blog posts 5. Folksonomies Folksonomy =Folk+Taxonomy
- ags: , , .
- Social tagging systems: tags.
- Folksonomy: tags .
tags 6. Taxonomy vs. Folksonomy
- Taxonomy
- /
- Folksonomy
- ,
7. folksonomies
- tagging systems
- tags
tag bundlesdel.icio.us relationsBibsonomy 8.
- folksonomy F := ( U , T , R , Y ,) :
- U (users)
- tags
- R (resources)
- YU T R tags (tag assignments)
- U T tags
- :
- ur := { t T | (u,t,r) Y } tags u r
- P := { (u, ur,r) | u U, r R } (posts)folksonomy
9. URL Scheme semantics
- u
- C u :={ (u , ur, r)P| u = u }
http://bibsonomy.org/user/ u http://del.icio.us/ u http://bibsonomy.org/tag/ t 1 ++t n http://del.icio.us/tag/ t 1 ++t n tagst 1 , , t n C t1, ,tn :={ (u, ur, r)P |{t 1 , , t n} ur } http://bibsonomy.org/user/ u / t 1 ++t n http://del.icio.us/ u / t 1 ++t n u tagst 1 , , t n C u,t1, ,tn:=C u C t1, ,tn 10. ;
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- tags
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- tags
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- ( mapping)tags
- E
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- search query disambiguation / search refinement
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- visualization
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- tag recommendations
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- ontology evolution / ontology population
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- , taxonomies folksonomies
11.
- /
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- ..apple ;
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- ..truck lorry ,nyc new_york_city
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- ..san_francisco, sanfrancisco, san.francisco
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- ..cat, cats
- granularity
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- ..ajax webdevelopment programming
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- ..maryandjohnswedding, toread, cool, me, 07032008 ( )
12. Narrow vs. Broad Folksonomies
- Narrow Folksonomies
- (..Flickr)
- tags
- tag
- tags
- tags
- Broad Folksonomies
- ( ..del.icio.us)
- tags
- tag
- tags
- tags
13. 1/2
- 1: folksonomies
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- tags
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- tags
- :co-occurrence
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- : To tagx tagy P( x|y ) > t ,t
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- : x , y tags, : P( x|y ) > t P( y|x ) < t xsubsumey
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- :P(linux | ubuntu) > 80% P(ubuntu | linux) < 80%
linuxubuntu 14. 2 /2
- 2: folksonomies
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- tags
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- supervised ,
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- WordNet,
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- ( .. Google ),
15. Specia & Motta
- Unsupervised:
- clustering : tags
- tags
- datasets Flickr del.icio.us
16.
- Preprocessing
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- tags
- Clustering
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- tags
- Concept and Relation Identification
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- tags
-
- ,
17. 1:Preprocessing tags 10 tags {catcats } { tipography typographtypography} {web-basedweb_based webbased } Levenshtein similarity(83%) ________________________________________________________________ : WordNet tags 1984_private/etc 3d802.11n tags tags , 18. Preprocessing clustering 2.696 17.956 tags 127.098 167.130 tags 44.032 49.087 44.032 49.087 1.265 11.960 tags 70.194 89.978 tags 13.579 14.211 18.882 19.605 19. 2:Clustering
- clusters
- clusters
- cluster
- patterns
- clusters
20. Pre-Clustering1/6
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- : n n , n tags
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- tags.
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- ( ) tag
-
- :
- M ij(ij) tags ij .
- M ii tag i .
- M ii M ix ,M xi xi
audio mp3 playlist music audio 7 5 3 6 mp3 5 9 7 2 playlist 3 7 8 3 music 6 2 3 6 21. Pre-Clustering2/6
- :Angular Separation
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- (.. ,manhattan)
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- outliers
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- : 0 1
- tags , .
22. Pre-Clustering3/6
- tags
- tag, tags,
audio mp3 playlist music 1 0.97 mp3 0.99 playlist 0.99 mp3 0.95 audio 2 0.95 music 0.97 audio 0.90 music 0.90 playlist 3 0.82 playlist 0.60 music 0.82 audio 0.60 mp3 4 0.75 radio 0.72 streaming 0.40 files 0.50 rock 23. Pre-Clustering4/ 6
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- Tags (..apple) .
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- tags tags tags .
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- :
apple, apple, apple, ! apple 0.90 mac 0.87 ipod 0.75 fruit 0.69 osx 0.54 pie 0.01 boxer 24. Pre-Clustering5/ 6
- top-ktags tag
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- tags
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- ( fruit, mac).
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- clustering!
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-
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- tags . vectors.
-
- :
- ( apple,mac )
- (apple, fruit)
- (apple, boxer)
apple 0.90 mac 0.87 ipod 0.75 fruit 0.69 osx 0.54 pie 0.01 boxer 25. Pre-Clustering6/6
- T sim tags .
-
- : T sim= 0.80
tags clustering audio mp3 playlist music 1 0.97 mp3 0.99 playlist 0.99 mp3 0.95 audio 2 0.95 music 0.97 audio 0.90 music 0.90 playlist 3 0.82 playlist 0.60 music 0.82 audio 0.60 mp3 4 0.75 radio 0.72 streaming 0.40 files 0.50 rock audio mp3 playlist music 1 0.97 mp3 0.99 playlist 0.99 mp3 0.95 audio 2 0.95 music 0.97 audio 0.90 music 0.90 playlist 3 0.82 playlist 0.60 music 0.82 audio 0.60 mp3 4 0.75 radio 0.72 streaming 0.40 files 0.50 rock 26. Clustering 1/ 3
- :
-
- clusters : tags
audio mp3 audio music audio playlist mp3 playlist mp3 audio playlist mp3 playlist music playlist audio music audio music playlist 4 audio 0.82 playlist 0.82 3 playlist 0.90 music 0.90 audio 0.97 music 0.95 2 audio 0.95 mp3 0.99 playlist 0.99 mp3 0.97 1 music playlist mp3 audio 27. Clustering 2/ 3
- :
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- clustertags cluster
-
- : clusters
audio mp3 playlist ? audio mp3 playlist music ? 4 audio 0.82 playlist 0.82 3 playlist 0.90 music 0.90 audio 0.97 music 0.95 2 audio 0.95 mp3 0.99 playlist 0.99 mp3 0.97 1 music playlist mp3 audio 28. Clustering 3/3
- clusters :
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- cluster , .
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- clusters T dif , .
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- tags tags cluster.
- tag clusters
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- tags
T difT dif 29. Clustering
- clusters
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- :visualization, tag extension, suggestion
- :
-
- clusters . .
- :
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- T sim tags (tag-vectors).
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- T dif clusters.
30. Clustering 1/2
- :
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- T sim>0.80
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- T dif0.8 3.632.860 799.480 4,983 2.298 T sim>0.8 2.696
Flickr 1.265 del.icio.us tags dataset 882 410 clusters 206 Flickr
47 del.icio.us clusters 2tags dataset 31. Clustering 2/2
- clusters:
32. 3:Concept and RelationIdentification 1/5
- clustering
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- tags cluster ;
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- tagsconcepts / instances/properties ;
- :
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- Semantic Web Search Engine (SWSE)
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- Sindice
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- Yahoo! Microsearch
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- Swoogle
33. Concept andRelation Identification 2 /5
- tags cluster
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- 1) Swoogle 2tags.
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- 2) tags :
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- 2.1)Wikipedia. A ..nycNew York City
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-
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- 2.2)Google.did you mean ..Sanfranciscodid you mean: San Francisco?
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-
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- 2.3) 1.
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34. Concept andRelation Identification 3 /5
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- 3) , .
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- 4) clustertag tag.
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- 5) tags:
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- 5.1) :concepts, instances, properties
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-
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- 5.2) tags :
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-
-
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- :concept, instanceproperty ;
-
-
-
-
-
- ( concept)
-
-
-
-
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- DomainRange( property)
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-
35. Concept andRelation Identification 4 /5
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- 6 ) tags :
-
-
- 6.1) tag
-
-
-
- 6.2) tag range value properties domain tag
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-
-
- 6 .3) tags ( )
-
-
-
- 6.4) tags
-
-
-
- 6.5) tags
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36. Concept andRelation Identification 5 /5
- tags , .apple = (1) fruit (2) computer brand tag.
- tagscluster.
- tags 6.
37. Concept and Relation Identification 1/2
- ToSwoogle
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- keywords
-
-
- concepts, instances, properties
-
-
- :
-
-
- concepts,exact matching
-
-
- : !
309 569 67 126 WordNet 5031 3152 94 97 tags WordNet 882 410 clusters Flickr del.icio.us dataset 38. Concept and Relation Identification 2/2
- cluster
-
- tags , tags
39. 40.
- LSpecia,EMotta (2007)Integrating Folksonomies with the Semantic Web.European Semantic Web Conference (ESWC 2007), Innsbruck, Austria.
- M Sabou, M d'Aquin, E Motta (2006)Using the Semantic Web as Background Knowledge for Ontology Mapping . International Workshop on Ontology Matching (OM-2006), International Semantic Web Conference (ISWC 2006).
- A Hotho, R Jaschke, C Schmitz, G Stumme(2006)BibSonomy: A Social Bookmark and Publication Sharing System . Proceedings of the First Conceptual Structures Tool Interoperability Workshop at the 14th International Conference on Conceptual Structures, Aalborg Universitetsforlag, Aalborg.
- P Schmitz(2006)Inducing ontology from flickr tags .Collaborative WebTaggingWorkshop at WWW2006, Edinburgh
- RPrieto-Diaz(2003) A faceted approach to building ontologies .Information Reuse and Integration
- T dif0.8 3.632.860 799.480 4,983 2.298 T sim>0.8 2.696
Flickr 1.265 del.icio.us tags dataset 882 410 clusters 206 Flickr
47 del.icio.us clusters 2tags dataset 31. Clustering 2/2