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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Expert Finding and Visualisation in a Personal Learning Environment
- 1. Expert Finding and Visualisation in a Personal Learning Environment Wolfgang Reinhardt Christian Schafmeister Sebastian Nuhn University of Paderborn Institute of Computer Science 1
- 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 3 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 4 Wolfgang Reinhardt
- 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) 5 Wolfgang Reinhardt
- 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 7 Wolfgang Reinhardt
- 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 8 Wolfgang Reinhardt
- 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 9 Wolfgang Reinhardt
- 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 10 Wolfgang Reinhardt
- 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 12 Wolfgang Reinhardt
- 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 13 Wolfgang Reinhardt
- 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 14 Wolfgang Reinhardt
- 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 15 Wolfgang Reinhardt
- 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 16 Wolfgang Reinhardt
- 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 17 Wolfgang Reinhardt
- 18. Prototype
- 19. Search Wolfgang Reinhardt 19 © Wolfgang Reinhardt, University of Paderborn
- 20. Wolfgang Reinhardt Search results 20 © Wolfgang Reinhardt, University of Paderborn
- 21. Expert and Keyword meshes © Wolfgang Reinhardt, University of Paderborn 21 Wolfgang Reinhardt
- 22. Wolfgang Reinhardt 22 © 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 23 Wolfgang Reinhardt
- 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 25 Wolfgang Reinhardt
- 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 29