Wolfgang ReinhardtChristian SchafmeisterSebastian Nuhn
University of PaderbornInstitute of Computer Science
Expert Finding and Visualisationin 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
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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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Implementation
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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
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75
250
search read edit create
Documents
Wiki Articles
Search
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
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search read edit create
Documents
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Search
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?
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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
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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Conclusions
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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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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
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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