organizing resources on tagging systems using t-org

<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>




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>




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>




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>




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>




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>




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>




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


EiffelEiffel tower

BigEyefulParis

FranceMiniatures


<is 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


EiffelEiffel tower

BigEyefulParis

FranceMiniatures


<is 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


User A

User B

EiffelEiffel tower

BigEyefulParis

FranceMiniatures


<is 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


Group 2

Group 1

EiffelEiffel tower

BigEyefulParis

FranceMiniatures


<is 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>




Experimental Setup – Classifiers

Two human classifiers: K (gold standard) and S T-KNOW

<is 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>




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>




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>




Conclusion and Future Work

-Austria -Germany -Pakistan -USA

+Animals +Cameras +Colours

+Events +Languages +People

+Places +Programming +Resources

+Cities +Countries +Lakes

+Markets +Universities

<is web>




Questions/Comments?

Q&A

organizing resources on tagging systems using t-org

Education

tag ford president

torg tag organization

tagging system torg

representation of tag

tag cloud

highest similarity

search results

overviewsocial tagging