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.

1. Expert Finding and Visualisation
in a Personal Learning Environment
Wolfgang Reinhardt
Christian Schafmeister
Sebastian Nuhn
University of Paderborn
Institute of Computer Science
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2. if you want to tweet
#icl09
#icl09_1C

3. Context of the project
• 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
© Wolfgang Reinhardt, University of Paderborn
• 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
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Wolfgang Reinhardt

4. Context of the project (cont.)
• 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
© Wolfgang Reinhardt, University of Paderborn
• 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
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5. 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
© Wolfgang Reinhardt, University of Paderborn
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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6. Some
Background

7. Knowledge Management
• „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)
YOU CANNOT STORE KNOWLEDGE
© Wolfgang Reinhardt, University of Paderborn
Nonaka 2001
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8. 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
© Wolfgang Reinhardt, University of Paderborn
• 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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9. 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)
• processes / activities • individuals
© Wolfgang Reinhardt, University of Paderborn
• documents • topics
• GBEV uses nodes and edges to represent entities and their connections
• well-known graph algorithms can be applied
• SNA metrics can be applied
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10. 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
© Wolfgang Reinhardt, University of Paderborn
• often found in organisational settings
• awareness of processes, knowledge domains, users
• OPEN
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11. Implementation

12. Overall architecture
• SOA design pattern
• service integration
• none to minimal changes to the subsystems
MetaXsA MeduSA DMS
• necessary logic in the service adapters of the
© Wolfgang Reinhardt, University of Paderborn
systems
• Central KnowledgeServer (KNS) SPI
KNS
User
Management
• using adapters to connect systems
Ratin
LOg
• differentiation between internal & external E-Mail- RaMBo
Wiki-
communication Server Server
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13. 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
© Wolfgang Reinhardt, University of Paderborn
• 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 ratings
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14. Expert finding - Relations
• Keyword - Keyword - Counter
relations that simply
• Keyword - Taxonomy - Counter count co-occurrence
• Taxonomy - Taxonomy - Counter
• User - Keyword - Rating
© Wolfgang Reinhardt, University of Paderborn
• User - Taxonomy - Rating
relations that use complex
• User - Source - Rating weighted ratings
• User A - User B - Keyword - Source - Rating
• User A - User B - Taxonomy - Source - Rating
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15. Expert finding - Valuation points & metric
search 1
read 10
edit 75
create 250
• valuation points
© Wolfgang Reinhardt, University of Paderborn
search read edit create
Documents 1 1 1 1
Wiki Articles 0,8 0,8 0,8 0,8
Search 0,2 0 0 0
E-Mail 0,4 0 0 0,4
E-Mail (To) 0 0,4 0 0,4
• used metric for ratings as matrix of action and source
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16. How does it work?
Keywords: Web 2.0, FLEX
Rating
LOM
120 Sender: Wolle
+ 14
134
Receiver: Johannes
RaMBo MetaXsA MeduSA DMS
User Keyword Rating User User Keyword Rating
Wolle Web 2.0 100 Wolle Johannes Web 2.0 100
Wolle FLEX 100 Wolle Johannes FLEX 100
© Wolfgang Reinhardt, University of Paderborn
Johannes Web 2.0 4
Johannes FLEX 4 Keyword Keyword Counter
KNS
Web 2.0 FLEX 1 SPI User
Management
search 1 search read edit create Relationen:
Rating
Documents 1 1 1 1 User - Keyword 120
read 10 LOM14
Wiki Articles 0,8 0,8 0,8 0,8 User - User - Keyword +
134
edit 75 Search 0,2 0 0 0 Keyword - Keyword - Counter
create 250
E-Mail 0,4 0 0
E-Mail-
0,4
Wiki- RaMBo
E-Mail (To) 0 0,4 0 0,4
Server Server
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17. How do we build meshes?
Rating
120 Experts for Web 2.0
Web 2.0 + 14
134 related keywords for Web 2.0
RaMBo MetaXsA MeduSA DMS
User Keyword Rating User User Keyword Rating Keyword Keyword Counter
Wolle Web 2.0 100 Wolle Johannes Web 2.0 100 Web 2.0 FLEX 1
Wolle FLEX 100 Wolle Johannes FLEX 100 Web 2.0 AJAX 4
Wolle Robin Web 2.0 50 FLEX AJAX 6
Johannes Web 2.0 4
Johannes FLEX 4 Wolle Robin AJAX 50
© Wolfgang Reinhardt, University of Paderborn
Robin Web 2.0 50
Robin AJAX 50 KNS
SPI User
Management
Expert mesh Keyword mesh
K
Wolle Web 2.0 Rating
120
+ 14
134
K K RaMBo
Johannes Robin FLEX
E-Mail-
AJAX Wiki-
Server Server
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18. Prototype

19. Search
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© Wolfgang Reinhardt, University of Paderborn

20. Wolfgang Reinhardt
Search results
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© Wolfgang Reinhardt, University of Paderborn

21. Expert and Keyword meshes
© Wolfgang Reinhardt, University of Paderborn
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22. Wolfgang Reinhardt
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© Wolfgang Reinhardt, University of Paderborn

23. 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 on
© Wolfgang Reinhardt, University of Paderborn
all data objects
• classical control
concept, hover yields
additional information,
click opens objects
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24. Conclusions

25. 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)
© Wolfgang Reinhardt, University of Paderborn
• 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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26. Outlook
• Improvement of semantical analysis
• Personal Learning Environment
• more data sources
• more widgets
© Wolfgang Reinhardt, University of Paderborn
• 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) 26
Wolfgang Reinhardt

27. Thank you
Want to know more?
http://twitter.com/wollepb
http://isitjustme.de
Wolfgang Reinhardt
University of Paderborn
Institute of Computer Science
Working Group Didactics of Informatics
http://ddi.upb.de

29. Image sources
• http://www.chromasia.com/images/chaos_theory_2_b.jpg
• http://www.ics.hit-u.ac.jp/community/wsj_nonaka01.jpg
• http://www.sxc.hu/photo/150038
• http://www.terracotta.org/attach/img/solutions/social-networking/social-graphs.png
• http://i303.photobucket.com/albums/nn157/suzQ_photo/Eva%20Kits/Boston-Bistro-sneak-peak.jpg
• http://de.fotolia.com/id/3805293
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