Ralf Klamma RWTH Aachen University KASW Workshop, I-Media, September 3, 2008 Community-Oriented Knowledge Acquisition and ...
Agenda <ul><li>Media & Knowledge </li></ul><ul><li>Communities </li></ul><ul><li>Case Studies </li></ul><ul><ul><li>Distur...
Learning & Knowledge Management Individual  /  Community  Perspective [Nonaka & Takeuchi, 1995] [Ullman, 2004] Semantic Kn...
Semiotics in the Tradition of  Ferdinand de Saussure (1957 - 1913) comprehension / articulation activation of community in...
Hypotheses 1. A semiotic knowledge system is dynamic and  it changes every time it is activated. 2. The meaning of a conce...
Cross-Media Theory of transcription Pre-“texts“ Transcript Cross-Media Transcription Understand and Criticize Jäger, Stani...
Babylonian Talmud:  A very old Hypertext <ul><ul><li>Scroll/book/printed book  </li></ul></ul><ul><ul><li>Talmud schools (...
CESE:   Multi-lingual Cross-Media System  Published in:  DS-NELL 2000, ICALT 2002, ICWL 2002, WWW 2003
Research Approach: Reflective Learning Network Collaborative adaptive learning network Mining tools for Communities Measur...
Solution idea for Reflective Support: Cross-Media Social Network Analysis <ul><li>Interdisciplinary  multidimensional mode...
Simplified Meta Model  for ANT using Latour Actor Attribute has isA isA Latour: On Recalling ANT , 1999 Klamma, Spaniol, C...
Modeling dependencies  using the i* framework Eric S. K. Yu, Towards Modeling and Reasoning Support for Early-Phase Requir...
Disturbances in  Cross-media Social Networks <ul><li>What is a disturbance? </li></ul><ul><ul><li>Sensing an incompatibili...
Pattern Language for  PALADIN : Example Troll <ul><li>Troll Pattern : This pattern tries to discover the cases when a trol...
Pattern Discovery Process Digital Social Network 1.  Set pattern parameters 2.  Instantiate disturbances 3.  Evaluate dist...
PALADIN  Case Study 10 patterns of disturbance over 119 social network instances, 17359 individuals, 215 345 mails Occurs ...
Impact of research community on individuals <ul><li>Academic event modeling </li></ul><ul><ul><li>Unstructured data of aca...
AERCS: Evolution of Scientific Communities
Evolution of community VLDB 1990 VLDB 1995 VLDB 2000 VLDB 2006
Community visualization –  ACM SIGMOD example ACM SIGMOD
Models of Community Success <ul><li>Reference Model: D&M IS Success Model (1992)  </li></ul><ul><ul><li>Based on >100 Empi...
Success Classification & Measurement <ul><li>Quantitative: Monitoring User-Service  </li></ul><ul><ul><li>Communication Log...
MobSOS Monitoring Module <ul><li>Client: Capture & Transmit Mobile Context Information  </li></ul><ul><li>Testbed: Log Com...
Mobile Service Oracle for Success <ul><li>Monitoring of service invocations </li></ul><ul><li>Time and position tracking o...
Storytelling Expertfinding <ul><li>New Measure for Knowledge in a Community </li></ul>Expert value Mean:  0,2624 # Entries...
Story-tellling Expert Finding Keywords Expert values <ul><li>Knowledge Value of Community sorted by keywords </li></ul># R...
Conclusions <ul><li>Media and Communities shape knowledge structures </li></ul><ul><ul><li>Semiotic systems depend on medi...
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KASW'08 - Invited Talk

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Workshop Knowledge Acquisition on the Social Web 2008, TRIPLE-I Conference, Graz, Austria, September 3-5, 2008

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  • Sehr verehrte Mitglieder der Fachgruppe Informatik, meine Damen und Herren. Ich möchte sie auf das herzlichste zu meinen Vortrag „Social Software und Community Informationssysteme“ begrüßen. Meine Name ist Ralf Klamma und ich bin akademischer Oberrat am Lehrstuhl für Informatik 5.
  • KASW'08 - Invited Talk

    1. 1. Ralf Klamma RWTH Aachen University KASW Workshop, I-Media, September 3, 2008 Community-Oriented Knowledge Acquisition and Analysis
    2. 2. Agenda <ul><li>Media & Knowledge </li></ul><ul><li>Communities </li></ul><ul><li>Case Studies </li></ul><ul><ul><li>Disturbances </li></ul></ul><ul><ul><li>Scientific Communities </li></ul></ul><ul><ul><li>Community Measures </li></ul></ul><ul><li>Conclusions & Outlook </li></ul>Agency & Patienthood in Digital Networks
    3. 3. Learning & Knowledge Management Individual / Community Perspective [Nonaka & Takeuchi, 1995] [Ullman, 2004] Semantic Knowledge semiotic concepts documentation Verbal words linguistic data Non-verbal image, icon, index video blogs, diagrams, images, photographies Episodic Knowledge memory of experiencing past episodes web blogs, narratives Declarative Knowledge Procedural Knowledge sensomotoric skills, procedural scripts non-documented routines and operations
    4. 4. Semiotics in the Tradition of Ferdinand de Saussure (1957 - 1913) comprehension / articulation activation of community information system human neural network Artifacts of community information system in presentia in absentia performance Parole competence Langue
    5. 5. Hypotheses 1. A semiotic knowledge system is dynamic and it changes every time it is activated. 2. The meaning of a concept is determined by how it interacts with other concepts and by how it can be distinguished from other concepts in the knowledge system. (Positive and Negative Knowledge) 3. The knowledge system is carried by a material medium. The modality of the medium influences knowledge structures.
    6. 6. Cross-Media Theory of transcription Pre-“texts“ Transcript Cross-Media Transcription Understand and Criticize Jäger, Stanitzek: Transkribieren - Medien/Lektüre 2002 <ul><li>Strategies of transcriptivity </li></ul><ul><ul><li>Collection of learning materials are re-structured by new media </li></ul></ul><ul><ul><li>Design is specific for media and communities by default </li></ul></ul><ul><li>Strategies of addressing </li></ul><ul><ul><li>Social Software promotes the globalization of address spaces </li></ul></ul><ul><ul><li>Personalization and adaptive strategies are mission critical for CoPs </li></ul></ul><ul><li>Strategies of localisation </li></ul><ul><ul><li>Re-organization of local practices is stimulated by new media like Social Software </li></ul></ul><ul><ul><li>Need to model practice explicitly </li></ul></ul>
    7. 7. Babylonian Talmud: A very old Hypertext <ul><ul><li>Scroll/book/printed book </li></ul></ul><ul><ul><li>Talmud schools (Jeshiwot) </li></ul></ul><ul><ul><li>Authoritative knowledge source </li></ul></ul><ul><ul><li>Dialogic encyclopedia </li></ul></ul><ul><ul><li>Structure of complex texts </li></ul></ul><ul><ul><li>Connected knowledge </li></ul></ul><ul><li>Transcribe? </li></ul><ul><li>Address? Localize? </li></ul>
    8. 8. CESE: Multi-lingual Cross-Media System Published in: DS-NELL 2000, ICALT 2002, ICWL 2002, WWW 2003
    9. 9. Research Approach: Reflective Learning Network Collaborative adaptive learning network Mining tools for Communities Measure, Analyse, Simulate Social Software Development Assessment requirements for Communities Support evolving learning communities (repeated assessment of community requirements) Based on Preece 2001, cf. I-KNOW 2006 for details
    10. 10. Solution idea for Reflective Support: Cross-Media Social Network Analysis <ul><li>Interdisciplinary multidimensional model of digital networks </li></ul><ul><ul><li>Social network analysis (SNA) is defining measures for social relations </li></ul></ul><ul><ul><li>Actor network theory (ANT) is connecting human and media agents </li></ul></ul><ul><ul><li>I* framework is defining strategic goals and dependencies </li></ul></ul><ul><ul><li>Theory of media transcriptions is studying cross-media knowledge </li></ul></ul><ul><li>social software </li></ul><ul><ul><li>Wiki, Blog, Podcast, IM, Chat, Email, Newsgroup, Chat … </li></ul></ul>i*-Dependencies (Structural, Cross-media) Members ( Social Network Analysis : Centrality, Efficiency) network of artifacts Microcontent, Blog entry , Message, Burst, Thread, Comment, Conversation, Feedback (Rating) network of members Communities of practice Media Networks
    11. 11. Simplified Meta Model for ANT using Latour Actor Attribute has isA isA Latour: On Recalling ANT , 1999 Klamma, Spaniol, Cao: A model for social software, IJKL 2007 Member Network Learning Service Medium Artifact stores creates is affected by belongs go represents consumes performs ranks … Match Retrieval Browse Search
    12. 12. Modeling dependencies using the i* framework Eric S. K. Yu, Towards Modeling and Reasoning Support for Early-Phase Requirements Engineering, RE 1997 Network Coordinator Gatekeeper Hub Member Iterant Broker URL isA isA isA Coordination Artifact Communication isA <ul><li>Legend: </li></ul><ul><ul><li>Agent </li></ul></ul><ul><ul><li>Goal </li></ul></ul><ul><ul><li>Resource </li></ul></ul><ul><ul><li>Task </li></ul></ul>
    13. 13. Disturbances in Cross-media Social Networks <ul><li>What is a disturbance? </li></ul><ul><ul><li>Sensing an incompatibility between theories exposed and theories-in-use </li></ul></ul><ul><li>Disturbances are starting points of learning processes </li></ul><ul><ul><li>Disturbances disturb, prevent … but they are creating reflection </li></ul></ul><ul><li>Disturbances are hard to detect or to forecast </li></ul>
    14. 14. Pattern Language for PALADIN : Example Troll <ul><li>Troll Pattern : This pattern tries to discover the cases when a troll exists in a digital social network. A troll in the network is considered a disturbance. </li></ul><ul><li>Disturbance : </li></ul><ul><li>(EXISTS [medium | medium.affordance = threadArtefact]) & </li></ul><ul><li>(EXISTS [troll |(EXISTS [thread | (thread.author = troll) & </li></ul><ul><li> (COUNT [message | (message.author = troll) & </li></ul><ul><li> (message.posted = thread)]) > minPosts]) & </li></ul><ul><li> (~EXISTS[ thread 1 , message 1 | (thread 1 .author 1 != troll) & </li></ul><ul><li> (message 1 .author = troll & message 1 .posted = thread 1 ]))])]) </li></ul><ul><li>Forces : medium; troll; network; member; thread; message; url </li></ul><ul><li>Force Relations : neighbour(troll, member); own thread(troll, thread) </li></ul><ul><li>Solution : No attention must be paid to the discussions started by the troll . </li></ul><ul><li>Rationale : The troll needs attention to continue its activities. If no attention is paid, he/she will stop participating in the discussions. </li></ul><ul><li>Pattern Relations : Associates Spammer pattern. </li></ul>
    15. 15. Pattern Discovery Process Digital Social Network 1. Set pattern parameters 2. Instantiate disturbances 3. Evaluate disturbances 4a. Change Pattern Parameters 4b. Apply Pattern Solution Pattern Disturbance Variables Pattern Template Disturbance Variables Pattern Parameters Pattern Template Instance Pattern Instance Disturbance Variables Pattern Parameters Forces Force Relations Rationale Dependencies Description Solution Pattern Relations Disturbance Instances Variables Pattern Parameters
    16. 16. PALADIN Case Study 10 patterns of disturbance over 119 social network instances, 17359 individuals, 215 345 mails Occurs in big networks where the members are distributed in different clusters. 40 No Leader Occurs for members having neighbors with only one contact. 67 Structural Hole Occurs in large networks where disconnected subnetworks exist. Scalability is necessary. 13 Independent Discussions The pattern occurs in the network centered around a member. 37 Leader Spammers can be found often in discussion groups. False positives exist. 86 Spammer Troll occurs very rarely in cultural communities. True negatives exist. 2 Troll Occurs in small networks. The effects of the lack of an answering person must be further checked with content analysis. 61 No Answering Person The existence implies that the network is not popular. 67 No Questioner The existence implies little communication in the network. 76 No Conversationalist The pattern finds out topics which were very important for certain period of time. Scalability is necessary. 22 Burst Remarks Occurrences Pattern
    17. 17. Impact of research community on individuals <ul><li>Academic event modeling </li></ul><ul><ul><li>Unstructured data of academic events </li></ul></ul><ul><ul><li>Diversity of additional media: Photos, Videos, Blogs, Wikis… </li></ul></ul><ul><ul><li>=> A model for academic events and their communities documentation </li></ul></ul><ul><li>Events recommendation tool for researchers </li></ul><ul><ul><li>Design a community based recommendation algorithm </li></ul></ul><ul><li>Events communities analysis and visualization. </li></ul><ul><ul><li>Community analysis from community of practice point of view </li></ul></ul>
    18. 18. AERCS: Evolution of Scientific Communities
    19. 19. Evolution of community VLDB 1990 VLDB 1995 VLDB 2000 VLDB 2006
    20. 20. Community visualization – ACM SIGMOD example ACM SIGMOD
    21. 21. Models of Community Success <ul><li>Reference Model: D&M IS Success Model (1992) </li></ul><ul><ul><li>Based on >100 Empirical/Conceptual Studies </li></ul></ul><ul><ul><li>Validated by Independent Studies Updated </li></ul></ul><ul><li>Model: Integration of Current Concepts </li></ul><ul><ul><li>Mobility (Mobile Context) </li></ul></ul><ul><ul><li>Multimedia Communities </li></ul></ul>
    22. 22. Success Classification & Measurement <ul><li>Quantitative: Monitoring User-Service </li></ul><ul><ul><li>Communication Logging </li></ul></ul><ul><ul><li>Mobile Context Information </li></ul></ul><ul><ul><li>MobSOS Monitoring Module </li></ul></ul><ul><li>Quantitative & Qualitative: Survey </li></ul><ul><ul><li>Online User Surveys (Questionnaire) </li></ul></ul><ul><ul><li>MobSOS Survey Module </li></ul></ul>Subjective Objective Quantitative Monitoring Survey Qualitative Survey
    23. 23. MobSOS Monitoring Module <ul><li>Client: Capture & Transmit Mobile Context Information </li></ul><ul><li>Testbed: Log Communication & Mobile Context </li></ul>
    24. 24. Mobile Service Oracle for Success <ul><li>Monitoring of service invocations </li></ul><ul><li>Time and position tracking of a service call </li></ul><ul><li>Recognition of patterns in user behaviour </li></ul>
    25. 25. Storytelling Expertfinding <ul><li>New Measure for Knowledge in a Community </li></ul>Expert value Mean: 0,2624 # Entries: 99.778 Frequency
    26. 26. Story-tellling Expert Finding Keywords Expert values <ul><li>Knowledge Value of Community sorted by keywords </li></ul># Recommendations Expert Amateur
    27. 27. Conclusions <ul><li>Media and Communities shape knowledge structures </li></ul><ul><ul><li>Semiotic systems depend on media </li></ul></ul><ul><ul><li>Communities set goals and means </li></ul></ul><ul><li>Case Studies </li></ul><ul><ul><li>Social Patterns in Communities </li></ul></ul><ul><ul><li>Evolution of Scientific Communities </li></ul></ul><ul><ul><li>Community Success Models </li></ul></ul><ul><ul><li>Expert finding in Communities </li></ul></ul><ul><li>Further research </li></ul><ul><ul><li>Uncertainty in Tagging systems </li></ul></ul><ul><ul><li>Continuous elicitation of community needs </li></ul></ul><ul><ul><li>Emotional dimension of collective intelligence </li></ul></ul>

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