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. ;

• tags

• tags

• ( mapping)tags

E

• search query disambiguation / search refinement

• visualization

• tag recommendations

• ontology evolution / ontology population

• , taxonomies folksonomies

11.

/

• ..apple ;

• ..truck lorry ,nyc new_york_city

• ..san_francisco, sanfrancisco, san.francisco

• ..cat, cats

granularity

• ..ajax webdevelopment programming

• ..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

• tags

• tags

:co-occurrence

• : To tagx tagy P( x|y ) > t ,t

• : x , y tags, : P( x|y ) > t P( y|x ) < t xsubsumey

• :P(linux | ubuntu) > 80% P(ubuntu | linux) < 80%

linuxubuntu 14. 2 /2

2: folksonomies

• tags

• supervised ,

• WordNet,

• ( .. Google ),

15. Specia & Motta

Unsupervised:

clustering : tags

tags

datasets Flickr del.icio.us

16.

Preprocessing

• tags

Clustering

• tags

Concept and Relation Identification

• 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

• : n n , n tags

• tags.

• ( ) 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

• (.. ,manhattan)

• outliers

• : 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

• Tags (..apple) .

• tags tags tags .

• :

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

• tags

• ( fruit, mac).

• • clustering!

• • 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

:

• 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 :

• cluster , .

• clusters T dif , .

• tags tags cluster.

tag clusters

• tags

T difT dif 29. Clustering

clusters

• :visualization, tag extension, suggestion

:

• clusters . .

:

• T sim tags (tag-vectors).

• T dif clusters.

30. Clustering 1/2

:

• T sim>0.80

• 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
  • • tags cluster ;
  • • tagsconcepts / instances/properties ;
  • :
  • • Semantic Web Search Engine (SWSE)
  • • Sindice
  • • Yahoo! Microsearch
  • • Swoogle

  33. Concept andRelation Identification 2 /5

  • tags cluster
  • • 1) Swoogle 2tags.
  • • 2) tags :
  • • • 2.1)Wikipedia. A ..nycNew York City
  • • • 2.2)Google.did you mean ..Sanfranciscodid you mean: San Francisco?
  • • • 2.3) 1.

  34. Concept andRelation Identification 3 /5

  • • 3) , .
  • • 4) clustertag tag.
  • • 5) tags:
  • • • 5.1) :concepts, instances, properties
  • • • 5.2) tags :
  • • • • :concept, instanceproperty ;
  • • • • ( concept)
  • • • • DomainRange( property)

  35. Concept andRelation Identification 4 /5

  • • 6 ) tags :
  • • • 6.1) tag
  • • • 6.2) tag range value properties domain tag
  • • • 6 .3) tags ( )
  • • • 6.4) tags
  • • • 6.5) tags

  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
  • • 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