Christoph Rensing: Recommending and finding multimedia resources in knowledge acquisition based on Web resources - MCCC - 05.08.2010

KOM - Multimedia Communications LabProf. Dr.-Ing. Ralf Steinmetz (director)

Dept. of Electrical Engineering and Information TechnologyDept. of Computer Science (adjunct professor)

TUD – Technische Universität Darmstadt Merckstr. 25, D-64283 Darmstadt, Germany

Tel.+49 6151 166150, Fax. +49 6151 166152 www.KOM.tu-darmstadt.de

httc –Hessian Telemedia Technology

Competence-Center e.V - www.httc.de

12. August 2010© 2010 author(s) of these slides including research results from the KOM research network and TU Darmstadt. Otherwise it is specified at the respective slideICCCN_2010_08_05

Recommending and finding multimedia resources in knowledge acquisition

based on Web resources

Dr.-Ing. Christoph RensingTel. +49 6151 16 [email protected]

This work was supported by funds from the German Federal Ministry of Education and Research under the mark 01 PF 08015 A and from the European Social Fund of the European Union (ESF).

KOM – Multimedia Communications Lab 2

Outline

Scenario: Learning / Knowledge Acquisition based on Web Resources

CROKODIL – a platform for collaborative Knowledge Acquisition based on Web Resources

Concept & Usage of Semantic NetworksFinding and Recommendation of Resources

Conclusion & Outlook


Resources used for Learning and Knowledge Acquisition

Learning Resources Animation

…

Web BasedTrainings

Teaching Material

E-Lecture

Social (Web)Resources WiKis

FAQsBulletin/

DiscussionBoards Video Coll.

Picture Coll.

Blogs

(Sources: slidestar.net, youtube.com, wehow.com)


Challenges of Resource-Based Learning using Web Resources

RBL applied to learning with web resources rise many challenges:Huge variety and quantity of possibly relevant resources

Finding relevant / authoritative / trusted resources is no easy taskNot explicitly intended for learning – different audiencesAll relevant information is not necessarily in one placeHow to store web resources efficiently in order to find them again

Further ChallengesNavigational disorientation

„lost in hyperspace“

Conceptual disorientationLack of integrating information in the own knowledge structure

Cognitive OverloadFinding information, analyzing found hits, organizing found information, …

See [SBB09+]


Challenges of Resource based Learning using Web Resources

Define Strategyfor Search Search

Select

PersistAnnotate

Share

Classificate

Use


Support of Resource based Learning

Define Strategyfor Search Search

Select

PersistAnnotate

Share

Classificate

Use

Linklists(Yahoo,…)

Searchengines(Google,…)

Expertsearch(Xing,…)

Ranking(Pagerank,…)

Rating Systems(StumbleUpon,…)

Tagg (Flickr, Del.icio.us,…)

Save as (Harddisk,…)

Browser Plugins

(Scrapbook,…)

Editors (Wikis, Excel,

Word,...)

Online-Annotation-Tools

(Diigo,…)

Social Bookmarking(Del.icio.us,…)

Electronic Communikation

(E-Mail,…)

Blogs (RSS,…)


Why a new application?

Using different applications add load to the user

Our scenario is characterized by:Heterogeneous resources

Text based formats like Wikis, FAQs, Blogs, PDF-DocumentsVideos Images...

Users are following individual tasks“Small” communityExisting applications do not fit for this characteristics in many casesGoal: CROKODIL: Support the user in the management of Web Resources using for Knowledge Acquisition


Outline

Scenario: Resource based Learning / Knowledge Acquisition





Semantic Networks

Used for knowledge representation

Formal modelConcepts(Semantic) Relations

Directed or undirected graph

Distinction usually in types and instances“Anne is a instance of the type person”

Challenge: How can be semantic information added effitiently?

Image source:http://www.tojet.net/articles/236.htm


Tagging

Simple mean to organize websites, bookmarks, pictures,…without constraints of strict classification

Folksonomic structures by aggregating tags of all users

Example: DeliciousSocial bookmarking application (saving, annotating, managing, sharing web pages)Tag suggestionsBrowsing via tagsNetworks between users


Concept – Semantic Tagging

Construction of a personal net of resources by semantic tagging

1 Annotation

2 Semantic Tagging3 Sharing with a community

Tag

Topic

Event

Person

Location

Goal/Task

related to

Photos

Blog

Script

NewsAvi

PaperWiki

Prepare Talk

TUD

TU Berlin

Workshop

Talk

Presenter

Hurricane

Lecture

ProfWind

to read

Tornado

eyewitness report

Blizzard

See [BSRS09] [BSR+]


CROKODIL - Persist


CROKODIL - Classificate / annotate



Outline






Finding Resources

By browsing the Semantic Network By browsing the CROKODIL Portal


Recommendations

Recommender systemsPresent information items to users that are likely of interestBased on implicit / explicit user profilesUse technologies from Information Retrieval

Different paradigms of generating / making recommendationsCollaborative Filtering

Based on similarity between users, good if there is enough dataOften so-called cold-start problem (not enough ratings available)

Content-based recommendationBased on properties or contents of resources

Recommendation based on folksonnomies and frequency of utilizationActive recommendation by users for:username

Amazon book and CD recommendations (Source: amazon.com)

Not applicable in small communities

& often biased results

Not applicable for heterogenous resources

Using a combination of different methods in CROKODIL


Structure based Recommendation in Semantic Networks

Recommending by traversing the Semantic Network

Recommendation independent of Type of Resources

Protocols

Communication Networks

paper script

WikiissubtopicRecommendation


Content-Based Recommendations

Similarity / relatedness between snippets is calcualted by comparision with Wikipedia articles

See [SBD+10]

?Protocols

Communication Networks

paperscript

Wiki

issubtopic

Computer Networks

WPWPWP

WPWPWP

Recommendation


Outline



Concept & Usage of Semantic NetworksRecommending and Finding of Resources




CROKODILan social application for managing heterogeneous Web Resources used for Knowledge Acquisitionbased on a combination of semantic networks and taggingIntegrated recommender systems based on different methods

OutlookDeployment of the prototype in industry and universityLong term evaluation of the overall approach and the different methods for recommendationExtensions

Optimization of recommendation methodsRecommending tagsMerging personal knowledge networks


References

[SBB09+] Philipp Scholl, Bastian F. Benz, Doreen Böhnstedt, Christoph Rensing, Bernhard Schmitz, Ralf Steinmetz: Implementation and Evaluation of a Tool for Setting Goals in Self-Regulated Learning with Web Resources. In: Ulrike Cress, VaniaDimitrova, Marcus Specht: Learning in the Synergy of Multiple Disciplines, EC-TEL 2009, vol. LNCS Vol 5794, p. 521-534, Springer-Verlag Berlin Heidelberg, October 2009. ISBN 978-3-642-04635-3.

[BSRS09] Doreen Böhnstedt, Philipp Scholl, Christoph Rensing, Ralf Steinmetz: Modeling Personal Knowledge Networks to Support Resource Based Learning. In: Klaus Tochtermann, Hermann Maurer: Proceedings of 9th International Conference on Knowledge Management and Knowledge Technologies (I-KNOW'09), p. 309-316, September 2009. ISBN 978-3-85125-060-2.

[BSR+09] Doreen Böhnstedt, Philipp Scholl, Christoph Rensing, Ralf Steinmetz: Collaborative Semantic Tagging of Web Resources on the Basis of Individual Knowledge Networks. In: Houben, G.-J.; McCalla, G.; Pianesi, F.; Zancanaro, M.: Proceedings of First and Seventeenth International Conference on User Modeling, Adaptation, and Personalization UMAP 2009, p. 379-384, Springer-Verlag Berlin Heidelberg 2009, ISBN 978-3-642-02246-3.

[SBD+10] Philpp Scholl, Doreen Böhnestedt, Renato Dominguez-Garcia, ChristophRensing, Ralf Steinmetz: Extended Explicit Semantic Analysis for Calculating Semantic Relatedness of Web Resources. Accepted for publication in: Proceedings of the Fifth European Conference on Technology Enhanced Learning, Barcelona, September 2010.


This work was supported by funds from the German Federal Ministry of Education and Research under the mark 01 PF 08015 A and from the European Social Fund of the European Union (ESF).
