Presentation of M.Sc. thesis at KOM Multimedia Communications Lab, TU Darmstadt, 2012

1. KOM - Multimedia Communications Lab
Prof. Dr.-Ing. Ralf Steinmetz (Director)
Dept. of Electrical Engineering and Information Technology
Dept. of Computer Science (adjunct Professor)
TUD – Technische Universität Darmstadt
Rundeturmstr. 10, D-64283 Darmstadt, Germany
Tel.+49 6151 166150, Fax. +49 6151 166152
www.KOM.tu-darmstadt.de
© author(s) of these slides 2012 including research results of the research network KOM and TU Darmstadt otherwise as specified at the respective slide
httc –
Hessian Telemedia Technology
Competence-Center e.V - www.httc.de
Thomas Rodenhausen
t.rodenhausen@stud.tu-darmstadt.de
12. Januar 2012
2
1
Source: www.google.com
Ranking Resources in Folksonomies
by Exploiting Semantic and Context-
specific Information
3

2. KOM – Multimedia Communications Lab 2Source: www.icon-finder.com, www.flickr.com, www.delicious.com, www.crokodil.de
Definition of folksonomy, adapted from
[HJSS06]
 Users 𝑈
 Resources 𝑅
 Tags 𝑇
 Tag assignment relation 𝑇𝐴𝑆 ⊆ 𝑈 × 𝑅 × 𝑇
Folksonomies
Bob sugar loaf
A tag assignment

3. KOM – Multimedia Communications Lab 3
Task of Ranking of Resources: “Rank resources, such that they are in
descending order of relevance towards an information need.”
user
given as query-entity
Interests
match
More
like this
resource
adapted from [Bog09]
Guided
search
tag
Find me a
resource
Ranking Resources in Folksonomies

4. KOM – Multimedia Communications Lab 4
“How probable do I go to B being at A”
1/5
1/4
3/5
1/3
1/2
FolkRank [HJSS06] state-of-the-art graph-based
 Based on PageRank’s random surfer [PBMW99]
|𝑅 𝑢, 𝑡 |
How to Actually Rank in Folksonomies?
Restart
3
1
1
2
1
1/4
3/4
1/4 2/3
1/5
Ranking 𝒗
 describes context
45%
29%
16%
10%
α = 1/3α
 Estimates relevance
fcbarcelona.com
messi
barca
barcaFan
 Estimates authority

5. KOM – Multimedia Communications Lab 5
 Assumption about folksonomy-structure violated
Source: www.icon-finder.com
Challenges of FolkRank
Concept drift
 Ambiguity
 Multi-facetedness of entities
Including quality attributes of a resource
 Authority Signals (e.g. PageRank on the Web)
 Hub signals
authority signals
hub signals
1
1
AI
(topic)
Barcelona
(location)
?
IJCAI-Proceedings.pdf
Artificial
Intelligence
(topic)
1
1
1
?1
football 1
1
?1
soccer
news
football

6. KOM – Multimedia Communications Lab 6
Structure
Background
• Folksonomies and Resource Ranking
• State-of-the-art FolkRank
• Challenges of FolkRank
Proposed
Approaches
• HITSonomy
• VSScore
Evaluation
• Setup
• Results

7. KOM – Multimedia Communications Lab 7
Proposed Approaches
IncentiveScore
 Concept drift
 Concept drift
InteliScore
 Inclusion of quality attributes of resources
HITSonomy
 Extensive description of resources/query-entity
VSScore

8. KOM – Multimedia Communications Lab 8
HITSonomy
FolkRank ‘thinks’ unidirectional
Combined scores yield ranking 𝒗
 Estimates relevance & authority
 Estimates relevance & hub
A B
21 2
A B
1/3 2/3 2/4
2/4
A B
1/3
2/3
2/42/4
“How probable do I go to B being at A”
A B
21 2
Additionally:
“How probable did I come from B being at A”
HITSonomy ‘thinks’ bidirectional
 Inspired by HITS [Kle99]
 Describes context

9. KOM – Multimedia Communications Lab 9
VSScore
Idea
 Port ranking task to vector space model [MRS08] used in text retrieval
Cowboys
1
…
0
0.8
…
0.3
…
0.2
barca
barcaFan
dallascowboys.com
 A term (usually) represents a semantic concept
Problem
 No content information of resources (in this work)
Solution
 Entities in folksonomy can be viewed as semantic concepts
 Represent resources’ content by their context
 Represent any entity by their context (e.g. a query-entity)
δ
Barcelona
Cowboys
Barcelona…
Messi...
Barcelona…
Barcelona…
FCB…
2
…
0
0
…
3
Cowboys
Barcelona
Dallas…
Cowboys…
Football…
Cowboys…
Dallas…

10. KOM – Multimedia Communications Lab 10
Structure
Background
• Folksonomies and Resource Ranking
• State-of-the-art FolkRank
• Challenges of FolkRank
Proposed
Approaches
• HITSonomy
• VSScore
Evaluation
• Setup
• Parameters
• Results

11. KOM – Multimedia Communications Lab 11
Evaluation Setup
BibSonomy corpus
Methodologies
 LeavePostOut [JMH+07]
 LeaveRTOut
Assumption: “Tag assignment indicates
relevance of resource towards information need
represented by user or tag”
Post: All tag assignments
between user and resource
RT: All tag assignments
between tag and resource

12. KOM – Multimedia Communications Lab 12
Evaluation Parameters
FolkRank
 LeavePostOut, given user as query-entity find me resources
 Restart propability

13. KOM – Multimedia Communications Lab 13
Evaluation Parameters
HITSonomy
 LeavePostOut, given user as query-entity find me resources
 Restart propability
 Weighted arithmetic mean of authority and hub score

14. KOM – Multimedia Communications Lab 14
Evaluation Results
LeavePostOut: 1 out
 Given user as query-entity find me resources
HITSonomy and VSScore significantly more effective than FolkRank
 Wilcoxon signed rank test on AveragePrecision

15. KOM – Multimedia Communications Lab 15
Evaluation Results
LeavePostOut: 33% out
 Given user as query-entity find me resources
HITSonomy and VSScore significantly more effective than FolkRank
 Wilcoxon signed rank test on AveragePrecision

16. KOM – Multimedia Communications Lab 16
Conclusion
HITSonomy and VSScore can beat the state-of-the-art
 In different resource ranking tasks
 Depending on LeavePostOut/LeaveRTOut, thus the conditions of the query-entity
Other proposed algorithms not as well
Methodology Interests match Guided search
LeavePostOut HITSonomy HITSonomy
LeaveNPostsOut HITSonomy HITSonomy
LeaveRTOut FolkRank,
HITSonomy,
IncentiveScore,
InteliScore
VSScore
LeaveNRTsOut FolkRank,
HITSonomy,
IncentiveScore
HITSonomy,
VSScore
Most pairwise statistical significance comparisons won:

17. KOM – Multimedia Communications Lab 17
Contributions
Disambiguation algorithms not evaluated
 Tag Assignment Context
 Post Context
Taxonomy for graph-based scoring/ranking algorithms
Implemented and evaluated
 Presented algorithms for ranking in folksonomies
 AInheritScore and Ascore for ranking in by activities extended folksonomies
Various other ideas for ranking described
Tag type labeling of evaluation corpus
Analysis for CROKODIL application scenario
Graph-based ranking framework

18. KOM – Multimedia Communications Lab 18
Future Work
Parameterization of proposed algorithms
Ranking task
Evaluation
Creation of corpora
Efficient computation
Explainability
Preprocessing of folksonomy corpus
…
 E.g. VSScore using HITSonomy result as context-description

19. KOM – Multimedia Communications Lab 19
Bibliography
[Bog09] T. Bogers. Recommender Systems for Social Bookmarking. PhD Thesis, Tilburg University,
2009.
[BSB+08] D. Böhnstedt, P. Scholl, B. Benz, C. Rensing, R. Steinmetz, and B. Schmitz. Einsatz
persönlicher Wissensnetze im Ressourcen-basierten Lernen. In Proceedings of the 6th
e-Learning Fachtagung Informatik, pages 113–124, 2008.
[HJSS06] A. Hotho, R. Jäschke, C. Schmitz, and G. Stumme. Information Retrieval in Folksonomies:
Search and Ranking. In Proceedings of the 3rd European Semantic Web Conference on the
Semantic Web: Research and Applications, pages 411–426, 2006.[JMH+07] Robert
Jäschke, Leandro Marinho, Andreas Hotho, Schmidt-Thie Lars, and Stum Gerd. Tag
recommendations in folksonomies. 2007
[MRS08] C. Manning, P. Raghavan, and H. Schütze. Introduction to Information Retrieval. Cambridge
University Press, 2008.
[Kle99] J. Kleinberg. Authoritative Sources in a Hyperlinked Environment. Journal of the ACM,
46:604–632, 1999.
[PBMW99] L. Page, S. Brin, R. Motwani, and T. Winograd. The PageRank Citation Ranking: Bringing
Order to the Web. Technical Report 1999-66, Stanford InfoLab, 1999.