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Wolfgang Reinhardt Christian Schafmeister Sebastian Nuhn University of Paderborn Institute of Computer Science Expert Finding and Visualisation in a Personal Learning Environment 1

Expert Finding and Visualisation in a Personal Learning Environment

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Slides from my talk at ICL09 in Villach, Austria focussing on the results of our project group MoKEx 4. Main content is about expert finding and visualization in a PLE-like environment.

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Page 1: Expert Finding and Visualisation in a Personal Learning Environment

Wolfgang ReinhardtChristian SchafmeisterSebastian Nuhn

University of PaderbornInstitute of Computer Science

Expert Finding and Visualisationin a Personal Learning Environment

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Page 2: Expert Finding and Visualisation in a Personal Learning Environment

if you want to tweet

#icl09

#icl09_1C

Page 3: Expert Finding and Visualisation in a Personal Learning Environment

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• MoKEx is a series of student projects

• interdisciplinary research project with universities and application partners from Germany and Switzerland

• IFIP-honoured type of education and cooperation

• students from computer science (DE) and business informatics (CH)

• combination of real-world problems with research topics and informatics education

• goal: development of solution designs and working prototypes

• show what is technically feasible

Context of the project

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• operational use of software in the context of e-learning and knowledge management

• capturing and storage of user context and use for personalised data representation

• enhancing stored data with automatically extracted metadata

• loose coupling of existing IT systems and connection via the KnowledgeBus architecture (Hinkelmann et al., 2007)

• development of the concept of a Single Point of Information to centralise search and retrieval processes

Context of the project (cont.)

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Specific goals of the MoKEx4 project

1.re-use of existing software components for the automatic extraction of content- and object-related metadata

2.derivation of expertise profiles and visualisation of experts

3.enrichment of classical search results with graphical representations of associated experts and related terms

4.development of a flexible component for rating and analysing user actions, storing the data and providing for any visualisations

• using data from e-mails, attachments and wikis

5.integration of the expert visualisation in a personal working environment (very light-weighted PLE)

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Some Background

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Knowledge Management

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YOU CANNOT STORE KNOWLEDGENonaka 2001

• „process of continuously creating new knowledge, disseminating it widely through the organisation, and embodying it quickly in new products/services, technologies and systems“ (Takeushi&Nonaka 2004)

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Expert Finding and Visualisation

• existing IT heterogeneity costs time and money (Information Builders 2007)

• right data cannot be found, no connection to contact persons

• todays IT systems lack in transparently showing employees expertise

• former Yellow Pages Systems stored employees‘ expertise in a static way

• data pool was rapidly outdated

• Ackerman‘s Answer Garden deemed as one of the first expert finders with self-updating user profiles (Ackerman, 1994)

• hardly any consideration of user context during execution of searches so far

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Graph-based Expert Visualisation

• tries to answer questions like „Who knows whom?“ or „Who works in which domain?“

• TRIER distinguishes knowledge entities that can be visualised and semantically interconnected (Trier, 2005)

• GBEV uses nodes and edges to represent entities and their connections

• well-known graph algorithms can be applied

• SNA metrics can be applied9

• processes / activities

• documents

• individuals

• topics

Page 10: Expert Finding and Visualisation in a Personal Learning Environment

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Personal Learning Environments

• mostly digital workplaces that are customisable by the user

• support the individual learning style and pace

• make learning more transparent by connecting users and content

• focus on informal learning styles

• often found in organisational settings

• awareness of processes, knowledge domains, users

• OPEN

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Page 11: Expert Finding and Visualisation in a Personal Learning Environment

Implementation

Page 12: Expert Finding and Visualisation in a Personal Learning Environment

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• SOA design pattern

• service integration

• none to minimal changes to the subsystems

• necessary logic in the service adapters of the systems

• Central KnowledgeServer (KNS)

• using adapters to connect systems

• differentiation between internal & external communication

Overall architecture

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MetaXsA MeduSA

KNSUser

Management

DMS

SPI

Wiki-Server

E-Mail-Server

RaMBo

Rating

LO

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Expert finding

• new component for analysing and rating user actions and usage behaviour

• RaMBo (Rating Module and Behaviour Profiling)

• connect users, keywords, organisational context and different types of data in multiple combinations

• development of a flexible rating scheme comprising relations, rating metric and valuation points

• two groups of relations

• simple count of joint occurrence of metadata

• recording of weighted ratings13

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Expert finding - Relations• Keyword - Keyword - Counter

• Keyword - Taxonomy - Counter

• Taxonomy - Taxonomy - Counter

• User - Keyword - Rating

• User - Taxonomy - Rating

• User - Source - Rating

• User A - User B - Keyword - Source - Rating

• User A - User B - Taxonomy - Source - Rating14

relations that simply count co-occurrence

relations that use complex weighted ratings

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• valuation points

• used metric for ratings as matrix of action and source

Expert finding - Valuation points & metric

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search 1

read

edit

create

10

75

250

search read edit create

Documents

Wiki Articles

Search

E-Mail

E-Mail (To)

1 1 1 1

0,8 0,8 0,8 0,8

0,2 0 0 0

0,4 0 0 0,4

0 0,4 0 0,4

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MetaXsA MeduSA

KNSUser

Management

DMS

SPI

Wiki-Server

E-Mail-Server

RaMBo

Rating

120+ 14

134

LOM

RaMBo

Rating

120+ 14

134

LOM

Relationen:User - KeywordUser - User - KeywordKeyword - Keyword - Counter

Keywords: Web 2.0, FLEX

Sender: Wolle

Receiver: Johannes

User Keyword Rating

Wolle Web 2.0 100

Wolle FLEX 100

Johannes Web 2.0 4

Johannes FLEX 4

search 1

read

edit

create

10

75

250

search read edit create

Documents

Wiki Articles

Search

E-Mail

E-Mail (To)

1 1 1 1

0,8 0,8 0,8 0,8

0,2 0 0 0

0,4 0 0 0,4

0 0,4 0 0,4

User User Keyword Rating

Wolle Johannes Web 2.0 100

Wolle Johannes FLEX 100

Keyword Keyword Counter

Web 2.0 FLEX 1

How does it work?

Page 17: Expert Finding and Visualisation in a Personal Learning Environment

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How do we build meshes?

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SPI

MetaXsA MeduSA

KNSUser

Management

DMS

Wiki-Server

E-Mail-Server

RaMBo

Rating

120+ 14

134

RaMBo

Rating

120+ 14

134Web 2.0

User Keyword Rating

Wolle Web 2.0 100

Wolle FLEX 100

Johannes Web 2.0 4

Johannes FLEX 4

Robin Web 2.0 50

Robin AJAX 50

User User Keyword Rating

Wolle Johannes Web 2.0 100

Wolle Johannes FLEX 100

Wolle Robin Web 2.0 50

Wolle Robin AJAX 50

Keyword Keyword Counter

Web 2.0 FLEX 1

Web 2.0 AJAX 4

FLEX AJAX 6

Experts for Web 2.0

related keywords for Web 2.0

Wolle

Johannes Robin

Expert mesh Keyword mesh

KWeb 2.0

KFLEX

KAJAX

Page 18: Expert Finding and Visualisation in a Personal Learning Environment

Prototype

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Search

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Search results

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Expert and Keyword meshes

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Taxonomy browser

• users partially overwhelmed by the proposed way of searching and retrieving

• wish for a more common way of browsing data (Explorer-style)

• usage of the underlying organisational taxonomies

• tree-based view onall data objects

• classical controlconcept, hover yieldsadditional information,click opens objects

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Page 24: Expert Finding and Visualisation in a Personal Learning Environment

Conclusions

Page 25: Expert Finding and Visualisation in a Personal Learning Environment

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Conclusion

• Graph-based expert visualisation can help creating a more transparent way of cooperation and IT-supported communication

• SOA architecture to connect heterogenous IT systems

• flexible and extensible way of analysing, rating and storing of user actions and usage behaviour (RaMBo)

• RIA acts as SPI for employees and connects classical search results with expert meshes and related keywords and taxonomies

• successfully tested with an application partner from the Steel industry

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Outlook• Improvement of semantical analysis

• Personal Learning Environment

• more data sources

• more widgets

• improved personalisation

• using RDF and SNA

• Artefact-Actor-Networks

• Use of the expert finding component in other settings with other input (APML instead of LOM)

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Page 27: Expert Finding and Visualisation in a Personal Learning Environment

Want to know more? http://twitter.com/wollepbhttp://isitjustme.de

Thank you

Wolfgang ReinhardtUniversity of Paderborn

Institute of Computer ScienceWorking Group Didactics of Informatics

http://ddi.upb.de

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