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<is web> Information Systems & Semantic Web
University of Koblenz ▪ Landau, Germany
Organizing Resources on Tagging Systems using T-ORG
Rabeeh AbbasiSteffen Staab
(University of Koblenz-Landau, Germany)
Philipp Cimiano(University of Karlsruhe, Germany)
Bridging the Gap between Semantic Web and Web 2.0Innsbruck, Austria
June 07, 2007
<is web>
Rabeeh Abbasi ([email protected])Steffen Staab ([email protected])Philipp Cimiano ([email protected])
Bridging the Gap…2 of 19
ISWeb - Information Systems & Semantic Web
Overview
Social Tagging Systems Browsing a Tagging System T-ORG
T-KNOW Experiments Results Conclusion and Future Work
<is web>
Rabeeh Abbasi ([email protected])Steffen Staab ([email protected])Philipp Cimiano ([email protected])
Bridging the Gap…3 of 19
ISWeb - Information Systems & Semantic Web
Social Tagging Systems / Folksonomies
In a social tagging system, people add keywords (called tags) to their resources and share these resources with others
Advantages low-cost classification, improve search, reputation
systems, personal organization, no fixed vocabulary, collaboration…
<is web>
Rabeeh Abbasi ([email protected])Steffen Staab ([email protected])Philipp Cimiano ([email protected])
Bridging the Gap…4 of 19
ISWeb - Information Systems & Semantic Web
Social Tagging Systems – Browsing?
I want to “browse” vehicle images!!! how can I do it?
• can I do it using a Tag Cloud?
Perhaps I need to structure the tags and resources! how can I do it?
• Put them into categories (like Vehicles, People, etc)!– Do it Manually or with Training?
» Might not be possible on a large scale!– Automatically and without any training!
» Using T-ORG!
<is web>
Rabeeh Abbasi ([email protected])Steffen Staab ([email protected])Philipp Cimiano ([email protected])
Bridging the Gap…5 of 19
ISWeb - Information Systems & Semantic Web
PresidentGeraldFordNixonPardon
T-ORG – Classification
Organize resources by putting their tags into categories depending upon their context Users can browse categories to retrieve required resources
User A
User B
Group 2
Group 1
EiffelEiffel tower
BigEyefulParis
FranceMiniatures
SingenCarsMotorsFord1955
Person Location Vehicle
Categories
<is web>
Rabeeh Abbasi ([email protected])Steffen Staab ([email protected])Philipp Cimiano ([email protected])
Bridging the Gap…6 of 19
ISWeb - Information Systems & Semantic Web
T-ORG
Tag Organization using T-ORG
Select ontologies related to the
categories(e.g. Vehicle, People, etc.)
Prune and refine these ontologies according to the
desired categories (add missing
concepts, filter existing concepts)
Apply the classification algorithm T-KNOW to classify the
tags and resources
Browse the categories to explore the tags
and resources
<is web>
Rabeeh Abbasi ([email protected])Steffen Staab ([email protected])Philipp Cimiano ([email protected])
Bridging the Gap…7 of 19
ISWeb - Information Systems & Semantic Web
Classifying the tags using T-KNOW
Use well-known linguistic patterns to
generate queriesSearch these patterns on
Google and download search results
Compare each Google search result with the context of the tag and
extract the concept
Select the concept which has the highest similarity with the context of the tag
<is web>
Rabeeh Abbasi ([email protected])Steffen Staab ([email protected])Philipp Cimiano ([email protected])
Bridging the Gap…8 of 19
ISWeb - Information Systems & Semantic Web
T-KNOW – Computing Similarity
Compute similarity using cosine measure between Bag of Words (BOW) representation of “Tag Context” and “Search Result”
1955 = 1as = 0cars = 1ford = 1foundation = 0international = 0motors = 1organizations = 0singen = 1such = 0
1955 = 0as = 1cars = 0ford = 1foundation = 2international = 1motors = 0organizations = 1singen = 0such = 1
Tag Contextsingencarsmotorsford1955
Search Result
BOW
cos(ĉ,â) = ĉ x â / |ĉ||â| = 0.15
ĉ â
Only consider the results having similarity above a certain Threshold Result having the highest similarity is considered as final
<is web>
Rabeeh Abbasi ([email protected])Steffen Staab ([email protected])Philipp Cimiano ([email protected])
Bridging the Gap…9 of 19
ISWeb - Information Systems & Semantic Web
T-KNOW – Computing Similarity – Resource Context
Getting the context of the tag “Ford” from middle image using Resource Context
• Select all tags of the current resource – President, Gerald, Nixon, Pardon
PresidentGeraldFordNixonPardon
EiffelEiffel tower
BigEyefulParis
FranceMiniatures
SingenCarsMotorsFord1955
<is web>
Rabeeh Abbasi ([email protected])Steffen Staab ([email protected])Philipp Cimiano ([email protected])
Bridging the Gap…10 of 19
ISWeb - Information Systems & Semantic Web
T-KNOW – Computing Similarity – Tag Context
Getting the context of the tag “Ford” from middle image using Tag Context
• Select all tags of all the resources having this tag “Ford”– President, Gerald, Nixon, Pardon, Singen, Cars, Motors, 1955
PresidentGeraldFordNixonPardon
EiffelEiffel tower
BigEyefulParis
FranceMiniatures
SingenCarsMotorsFord1955
<is web>
Rabeeh Abbasi ([email protected])Steffen Staab ([email protected])Philipp Cimiano ([email protected])
Bridging the Gap…11 of 19
ISWeb - Information Systems & Semantic Web
T-KNOW – Computing Similarity – User Context
Getting the context of the tag “Ford” from middle image using User Context
• Select all tags of all the resources from the user who use this resource– President, Gerald, Nixon, Pardon, Eiffel, Eiffel tower, Big, Eyeful, Paris, France, Miniatures
PresidentGeraldFordNixonPardon
User A
User B
EiffelEiffel tower
BigEyefulParis
FranceMiniatures
SingenCarsMotorsFord1955
<is web>
Rabeeh Abbasi ([email protected])Steffen Staab ([email protected])Philipp Cimiano ([email protected])
Bridging the Gap…12 of 19
ISWeb - Information Systems & Semantic Web
T-KNOW – Computing Similarity – Group Context
Getting the context of the tag “Ford” from middle image using Group Context
• Select all tags of all the resources present in the group to which this resource belong– President, Gerald, Nixon, Pardon, Eiffel, Eiffel tower, Big, Eyeful, Paris, France,
Miniatures, Singen, Cars, Motors, Ford, 1955
PresidentGeraldFordNixonPardon
Group 2
Group 1
EiffelEiffel tower
BigEyefulParis
FranceMiniatures
SingenCarsMotorsFord1955
<is web>
Rabeeh Abbasi ([email protected])Steffen Staab ([email protected])Philipp Cimiano ([email protected])
Bridging the Gap…13 of 19
ISWeb - Information Systems & Semantic Web
Experimental Setup
Person
Location
Vehicle
Organization
Other
Author, Singer, Human, …Country, District, City, Village,…
Vehicle, Car, Truck, Motorbike, Train, …Company, Organization, Firm, Foundation, …
4+1 Categories 932 Concepts
189 random Images from 9 Flickr groups 1754 Tags
<is web>
Rabeeh Abbasi ([email protected])Steffen Staab ([email protected])Philipp Cimiano ([email protected])
Bridging the Gap…14 of 19
ISWeb - Information Systems & Semantic Web
Experimental Setup – Classifiers
Two human classifiers: K (gold standard) and S T-KNOW
<is web>
Rabeeh Abbasi ([email protected])Steffen Staab ([email protected])Philipp Cimiano ([email protected])
Bridging the Gap…15 of 19
ISWeb - Information Systems & Semantic Web
Experimental Setup – Evaluation
F-MeasureA = set of correct classification by test (user S or T-KNOW)
B = set of all classification by Gold Standard (user K)
C = set of all classifications by test Precision = A / C Recall = A / B F-Measure = 2 * Precision * Recall / (Precision + Recall)
Cohen’s Kappa Considers classification done by chance Used to measure classifiers reliability
• P0 = observed agreement between classifiers
• Pc = agreement occurred due to chance
c
c
P
PPK
1
0
<is web>
Rabeeh Abbasi ([email protected])Steffen Staab ([email protected])Philipp Cimiano ([email protected])
Bridging the Gap…16 of 19
ISWeb - Information Systems & Semantic Web
Results – F-Measure
0.51
0.56
0.61
0.66
0.71
0.76
0.00 0.05 0.10 0.15 0.20 0.25 0.30Threshold
F-M
ea
su
re
Tag Context
Resource Context
User Context
Group Context
User S
- Results comparable to Human Classification
51%
79%
<is web>
Rabeeh Abbasi ([email protected])Steffen Staab ([email protected])Philipp Cimiano ([email protected])
Bridging the Gap…17 of 19
ISWeb - Information Systems & Semantic Web
Results – Cohen’s Kappa
0.00
0.10
0.20
0.30
0.40
0.50
0.00 0.05 0.10 0.15 0.20 0.25 0.30
Threshold
Ka
pp
a V
alu
e
Tag Context
Resource Context
User Context
Group Context
User S
- Might be a good measure when there is a chance of classification by chance
0%
53%
<is web>
Rabeeh Abbasi ([email protected])Steffen Staab ([email protected])Philipp Cimiano ([email protected])
Bridging the Gap…18 of 19
ISWeb - Information Systems & Semantic Web
Conclusion and Future Work
-Austria -Germany -Pakistan -USA
+Animals +Cameras +Colours
+Events +Languages +People
+Places +Programming +Resources
+Cities +Countries +Lakes
+Markets +Universities
<is web>
Rabeeh Abbasi ([email protected])Steffen Staab ([email protected])Philipp Cimiano ([email protected])
Bridging the Gap…19 of 19
ISWeb - Information Systems & Semantic Web
Questions/Comments?
Q&A