Iknow ranking sem_info_v9.0__2012.09.07_anjorin

Ranking Resources in Folksonomies
By Exploiting Semantic Information
i-KNOW 2012, Saarbrücken

Users Tags Resources

Research
Talk

n
Perso
Typ
e Loca
tion
Ranking
Topic
Algorithms
Event Oth
Act er
ivit
y

Thomas Rodenhausen Slideshare

Mojisola Anjorin
Renato Domínguez García
Christoph Rensing Prof. Dr.-Ing. Ralf Steinmetz
Ralf Steinmetz KOM - Multimedia Communications Lab

Iknow_Ranking_Sem_Info_v9.0__2012.09.07_MA.pptx 7-Sep-12
© author(s) of these slides including research results from the KOM research network and TU Darmstadt; otherwise it is specified at the respective slide

Social Tagging Applications

Social tagging applications are used to organize, classify, manage
and share knowledge resources
!  Tags are freely chosen keywords attached to resources
!  Tags often describe an aspect of the resource

Lora
o
Aroy Graz
Key
note Dial E
I-know
for
2012 Events Sema
n
Pre
pa rin
W tic
eb
g
Con for
fer
enc
e

KOM – Multimedia Communications Lab 2
www.wordle.net

Recommendations in Social Tagging
Applications


Overview

!  Motivation
!  Basics
!  Folksonomy
!  Folksonomy Extended by Tag Types
!  Graph-based Resource Recommendation
!  Challenge: Concept Drift
!  AspectScore & InteliScore
!  Evaluation Methodology and Metrics
!  Results
!  Conclusion & Future Work


Folksonomy

A folksonomy is a quadruple
F:= (U, T, R, Y), where
Research
Talk

U – Users
T – Tags
R – Resources
Y ⊆ U ! T ! R - tag assignment Ranking
Algorithms

Slideshare

[Hotho et al. 2006]

Folksonomy Extended by Tag Types
An extended folksonomy
FA:= (U, T, R, A, Y) where
Research
Talk

U – Users Perso
n
Typ
T – Tags e Loca
tion
Topic
R – Resources Event Oth
Act er
A – Tag Types ivity Ranking
Algorithms

Y⊆U!T!RxA

A = {Topic, Resource Type, Location,
Person, Event, Activity, Other}
Slideshare

[Böhnstedt et al. 2009]

Graph-based Resource Recommendation

Recommendation List

Item Score
3 P2
P1 1 1 Graph-based r1 0.9
2 Ranking r2 0.7
4 1 Algorithm
2 1 r3 0.5
P3
2 P4 r4 0.2

Folksonomy Graph Scorer e.g. PageRank, HITS Ranked Resources


Adapted PageRank
[Hotho et al. 2006]

" " Dancing
$%&'()*+,& Tango ! l
0 Festiva
0
# #
"-.
"

#-.
1
!
PageRank‘s intelligent surfer model
#-.

Buenos " The ranking of a node is determined by how
"-. Aires often the surfer visits the node
0

Buenos Adjoining edges are followed with a certain
Aires probability – determined by the edge weights
0

" The query node acts as the starting point and
focus i.e. the surfer returns to this node with
! a certain probability – determined by the
node weights

Challenge: Concept Drift

Concept drift is a challenge for graph-based ranking algorithms
!  e.g. Ambiguous tags can cause concept drift as a single tag might represent
multiple semantic concepts

?

football News about Messi

?
FC Barcelona Website

Dallas Cowboys‘ Website

Overview

!  Motivation
!  Basics
!  Folksonomy
!  Challenge: Concept Drift
!  Results


InteliScore

The semantic information gained from the semantic relatedness
between tags is used to reduce concept drift

Dancing
Tango
Festival

Semantic Relatedness (XESA)

0.005

Buenos
Aires

XESA calculates the semantic
relatedness between pairs of tokens
(tags) using the English Wikipedia as
reference corpus
[Scholl et al. 2010]
Source: wikipedia.org KOM – Multimedia Communications Lab 11

AspectScore

Tag types help to alleviate concept drift
!  Tags are disambiguated with respect to different aspects of a resource that a
user may describe while tagging

topic location
Buenos
Aires

Tourism in Argentina
News about Tango


AspectScore

Tag types help to alleviate concept drift
!  e.g. by focusing on the tags describing the content of resources

topic location
Buenos
Aires

Tourism in Argentina
News about Tango
topic

Assumption:
The tags of type „Topic“
describe the content of the resources well,
Google Map of Buenos Aires therefore „Topic“ Tags are given priority.


AspectScore: Step 1

Buenos
1. Transform Query Node Tango Buenos Aires
into Query Tags 3 Aires 1
1

User query node is transformed into tag nodes,
weighted by the usage frequency of the user

Assumption:
Tags of a user describe the user‘s interests well
[Abel 2011]


AspectScore: Step 1

Query Tag
1. Transform Query Node
in Query Tags
Dancing
Tango Festiva
l

Query Node

Buenos
Aires

Buenos
Aires Query Tags


AspectScore: Step 2

into Query Tags

2. Create Folksonomy
Graph for each Query Tag


AspectScore: Step 2

1. Transform Query Node " " Dancing
$%&'()*+, Tango ! l
into Query Tags 3 Festiva
0
# #
"
2. Create Folksonomy "
! #
!

Buenos "
"
Aires
" 0

" Buenos
Aires
0 Depending on Ranking Algorithm
e.g. FolkRank
"

!


AspectScore: Step 3

1. Transform Query Node "#"$ "#"$ Dancing
'()*+",-. Tango ! l
0
"%"$ "%"$
"#"$
2. Create Folksonomy "#"$
Graph for each Query Tag "%"$
!
!

Buenos "#"$
"#"$
3. Adapt Edge Weights Aires
"#"$ 0

Buenos
"#"$&
Aires
0 Edge Weights are adapted
"#"$
(in several iteration steps)
depending on Query Tag

!


AspectScore: Step 4

1. Transform Query Node "#"$ "#"$ Dancing
'()*+",-. Tango ! l
0
"%"$ "%"$
"#"$
2. Create Folksonomy "#"$
Graph for each Query Tag "%"$
!
!

Buenos "#"$
"#"$
3. Adapt Edge Weights Aires
"#"$ 0

Buenos
"#"$&
Aires
4. Run Ranking Algorithm 0 Run e.g. FolkRank on the
"#"$
adapted folksonomy graph

!


AspectScore: Step 5

into Query Tags

2. Create Folksonomy

3. Adapt Edge Weights

The resulting rankings are accumulated
giving preference to certain tag types
4. Run Ranking Algorithm e.g. topic tags
Buenos
Tango Buenos Aires
5. Accumulate Results Aires 1δ Topic
3δ Topic
for each Query Node 1δ Location


Overview

!  Motivation
!  Basics
!  Folksonomy
!  Challenge Concept Drift
!  Results


Evaluation Methodology: LeavePostOut

A post is a Pu,r= {(u,r,t)|(u,r,t) ! Y}

Dancing Dancing
Tango Tango
Festival Festival

Buenos Buenos
Aires Aires

For LeavePostOut, the recommendation task
with user as input is harder as with tag as input
[Jäschke et al. 2007]

Evaluation Methodology: LeaveRTOut

RTr,t= {(u,r,t)|(u,r,t) ! Y}

Dancing Dancing
Tango Tango
Festival Festival

Buenos Buenos
Aires Aires

For LeaveRTOut, the recommendation task
with tag as input is harder as with user as input


Evaluation Corpus

Bibsonomy corpus with a p-core extraction at level 5 to reduce noise
and to focus on the dense portion of the corpus

Before After Tag Type Count
Users 7243 69 Topic 2225
Bookmark resources 281550 9 Other 486
Bibtex resources 469654 134 Resource Type 198
Tags 216094 179 Event 182
Tag assignments 2740834 3269 Person/Organisation 143
Bookmark posts 330192 51 Activity 35
Bibtex posts 526691 959

FReSET – Domínguez García et al 2012
http://www.kom.tu-darmstadt.de/research-results/downloads/software/freset/
Knowledge and Data Engineering Group, University of Kassel: Benchmark Folksonomy Data from Bibsonomy, version of July 7th 2011

Evaluation Metrics

Mean Average Precision:
The mean of the Average
|Q| mj Precision over several queries Q
1 1
MAP(Q) = Precision(Rjk )
|Q| j=1 mj k=1 [Manning et al 2008]

Mean Normalized Precision:
The mean of the normalized
Precision at k
|Q|
1 Precisionj (k) over several queries Q
MNP(Q, k) =
|Q| j=1 Precisionmax,j (k)


Visualization of Results with Violin Plots

A violin plot is a combination of a box plot and a density trace

3rd Quartile

Median
1st Quartile
[Hintze et al. 1998]

Evaluation Results LeavePostOut

Evaluation results for the recommendation task having tag as input


Evaluation Results for LeavePostOut


Approaches MAP
AspectScore 0.2240
FolkRank 0.2136
InteliScore 0.1801
Popularity 0.0937


Evaluation Results for LeaveRTOut



Evaluation Results for LeaveRTOut


Approaches MAP
Popularity 0.0834
AspectScore 0.0589
FolkRank 0.0529
InteliScore 0.0433


Conclusion and Future Work

Exploiting semantic information for resource ranking in folksonomies
AspectScore InteliScore
Tag disambiguation importance of Based on semantic
tags (based on type) n
relatedness between tags
Perso
Typ
e Loca
tion
e.g. XESA
Topic
Event Oth
Act er
ivit
y

Limitations
!  Manually labeled tag type dataset – error prone, subjective
!  XESA based on English Wikipedia – No semantic relatedness measurable for
27% of tags in corpus

Future Work
!  Evaluation using CROKODIL corpus – an e-learning application with tag types
!  User Study www.crokodil.de

Questions Contact


Iknow ranking sem_info_v9.0__2012.09.07_anjorin

Recomendados

Recomendados

Mais conteúdo relacionado

Mais procurados

Mais procurados (20)

Destaque

Destaque (8)

Semelhante a Iknow ranking sem_info_v9.0__2012.09.07_anjorin

Semelhante a Iknow ranking sem_info_v9.0__2012.09.07_anjorin (20)

Último

Último (20)

Iknow ranking sem_info_v9.0__2012.09.07_anjorin