• Share
  • Email
  • Embed
  • Like
  • Save
  • Private Content
Robust Expert Finding in Web-Based Community Information Systems
 

Robust Expert Finding in Web-Based Community Information Systems

on

  • 1,165 views

Robust Expert Finding in Web-Based Community Information Systems

Robust Expert Finding in Web-Based Community Information Systems
Ralf Klamma
Advanced Community Information Systems (ACIS) RWTH Aachen University, Germany

Statistics

Views

Total Views
1,165
Views on SlideShare
389
Embed Views
776

Actions

Likes
0
Downloads
0
Comments
0

36 Embeds 776

http://beamtenherrschaft.blogspot.com 230
http://beamtenherrschaft.blogspot.de 144
http://beamtenherrschaft.blogspot.in 63
http://beamtenherrschaft.blogspot.fr 35
http://beamtenherrschaft.blogspot.com.es 31
http://beamtenherrschaft.blogspot.co.uk 28
http://beamtenherrschaft.blogspot.it 27
http://beamtenherrschaft.blogspot.kr 25
http://beamtenherrschaft.blogspot.ca 17
http://beamtenherrschaft.blogspot.jp 14
http://cloud.feedly.com 14
http://beamtenherrschaft.blogspot.ch 13
http://beamtenherrschaft.blogspot.co.at 13
http://beamtenherrschaft.blogspot.tw 11
http://beamtenherrschaft.blogspot.ie 11
http://elgg.ell.aau.dk 9
http://beamtenherrschaft.blogspot.nl 8
http://beamtenherrschaft.blogspot.co.il 8
http://beamtenherrschaft.blogspot.gr 7
http://beamtenherrschaft.blogspot.hk 7
http://beamtenherrschaft.blogspot.sg 7
http://beamtenherrschaft.blogspot.be 7
http://beamtenherrschaft.blogspot.com.au 6
http://beamtenherrschaft.blogspot.fi 6
http://beamtenherrschaft.blogspot.pt 6
http://beamtenherrschaft.blogspot.com.br 6
http://beamtenherrschaft.blogspot.se 5
http://beamtenherrschaft.blogspot.dk 4
http://beamtenherrschaft.blogspot.ru 2
http://plus.url.google.com 2
http://translate.googleusercontent.com 2
http://beamtenherrschaft.blogspot.mx 2
http://beamtenherrschaft.blogspot.cz 2
http://beamtenherrschaft.blogspot.hu 2
http://www.beamtenherrschaft.blogspot.ru 1
http://beamtenherrschaft.blogspot.ro 1
More...

Accessibility

Categories

Upload Details

Uploaded via as Adobe PDF

Usage Rights

CC Attribution-ShareAlike LicenseCC Attribution-ShareAlike License

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

    Robust Expert Finding in Web-Based Community Information Systems Robust Expert Finding in Web-Based Community Information Systems Presentation Transcript

    • Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 1 Learning Layers This slide deck is licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License. Robust Expert Finding in Web-Based Community Information Systems Ralf Klamma Advanced Community Information Systems (ACIS) RWTH Aachen University, Germany klamma@dbis.rwth-aachen.de The Future of Scientifically Founded Databases on Experts June 30th – July 02nd 2013 in Graz (Austria)
    • Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 2 Learning Layers Responsive Open Community Information Systems Community Visualization and Simulation Community Analytics Community Support WebAnalytics WebEngineering Advanced Community Information Systems (ACIS) Group @ RWTH Aachen Requirements Engineering
    • Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 3 Learning Layers Agenda WebCommunityInformation Systems ExpertsinCommunityInformation Systems RobustExpertIdentification TrustinExperts Conclusions&Outlook
    • Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 4 Learning Layers A Brief History of Community Information Systems Digital Media Technology Communities of Practice (Web 2.0) Business Processes Meta Data Media Traces Semantic Web (XML, RDF, Ontologien) Multimedia (XML, VRML, DC, MPEG) Organisational Memories (XML, HTML, XTM) Groupware / E-Learning (XML, LOM, XML-RPC) Workflows (XML, BPEL) Web Services (XML, WSDL, SOAP,UDDI) Klamma: Social Software and Community Information Systems, 2010 Social Software (XML, HTTP, RSS)
    • Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 5 Learning Layers The Long Tail & Fragments  The Web is a scale-free, fragmented network – The power law (Pareto-Distribution etc.) – 95 % of users are located in the Long Tail (Communities) – Trust and passion based cooperation IslandTendrils IN Continent Central Core OUT Continent Tunnels [Barabasi, 2002] [Anderson, 2006]
    • Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 6 Learning Layers Communities of Practice  Communities of practice (CoP) are groups of people who share a concern or a passion for something they do and who interact regularly to learn how to do it better (Wenger, 1998)  Characterization of experts in CoP – Shared competence in the domain – Shared practice over time by interactions – Expertise based on gaining and having reputation within the CoP – Being an expert vs. being a layman, a newcomer, an amateur etc. – Informal leadership – Identity as an expert depends on the lifecycle of the communities Expertise in highly dynamic, locally distributed multi-disciplinary and heterogeneous communities?
    • Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 7 Learning Layers EXPERTS IN COMMUNITY INFORMATION SYSTEMS
    • Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 8 Learning Layers International Communities Communication / Cooperation ? Cultural heritage in Afghanistan Database Content input / request Content retrieval Surveying/ safeguarding Sketch drawing Photographing Surveying/ recording GPS positioning Experiences imparting Administration UNESCO Teaching/ presentation Asia ICOMOS Standards defining Research RWTH Aachen SPACH www.bamiyan-development.org
    • Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 9 Learning Layers Experts in International Communities  In international communities many experts from different fields meet – Intergenerational learning – Interdisciplinary learning  New Openness for Amateur Contributions  Methods, Tools & CoP co-develop – Expert role models needed – Expert identification based on complex media traces
    • Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 10 Learning Layers YouTell - A Web 2.0 Service for Collaborative Storytelling  Collaborative storytelling  Web 2.0 Service  Story search and “pro-sumption”  Tagging  Ranking/Feedback  Expert finding  Recommending Klamma, Cao, Jarke: Storytelling on the Web 2.0 as a New Means of Creating Arts Handbook of Multimedia for Digital Entertainment and Arts, Springer, 2009
    • Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 11 Learning Layers Expert Finding – Computation of Actual Knowledge  Data vector consists of – Personal data vector – Competences, skills, qualification profile – Self-entered data – Story data vector – Visits of stories – Involvement in projects – Expert data vector – Advice given – Advice received – Value = #Keywords  Date Decay  Feedback Motivation PESE: Web 2.0 –Anwen- dung für community- basiertes Storytelling Der PESE- Prototyp Evaluierung des Prototypen Zusammen- fassung Ausblick Find the most appropriate expert Data vector represents knowledge of the expert
    • Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 12 Learning Layers Knowledge-Dependent Learning Behaviour in Communities Renzel, Cao, Lottko, Klamma: Collaborative Video Annotation for Multimedia Sharing between Experts and Amateurs, WISMA 2010, Barcelona, Spain, May 19-20, 2010  Expert finding algorithm: Knowledge value of community sorted by keywords  Community behavior: Experts spent more time on the services  Experts prefers semantic tags while amateurs uses “simple” tags frequently  Community tags: Experts use more precise tags
    • Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 13 Learning Layers Threads to Expert Finding  Compromising techniques — Sybil attack [Douc 2002], Reputation theft, Whitewashing attack, etc.. — Compromising the input and the output of the expert identification algorithm  Example: Sybil attacks — Fundamental problem in open collaborative Web systems — A malicious user creates many fake accounts (Sybils) which all reference the user to boost his reputation (attacker’s goal is to be higher up in the rankings) Sybil regionHonest region Attack edges
    • Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 14 Learning Layers ROBUST EXPERT FINDING
    • Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 15 Learning Layers  HITS algorithm [Kleinberg 1999] — Authorities and hubs — HITS -mutual reinforcement between web pages “a better hub points to many good authorities, and a better authority is pointed to by many good hubs” HITS: Expert Ranking Algorithm Hub Authority — Users (hubs) — Media (authorities) — Mutual reinforcement between users and media files and trust network is considered — Expert users tend to have many correctly evaluated media, correctly rated media are rated by trusted users of high expertise Hub Authority User Set Rate MediaUpload Media Web of Trust Media Set
    • Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 16 Learning Layers  MHITS computation — Trust in range [0, 1] — Ratings: 0.5 for a fake vote, 1 for an authentic vote Symbol Description Authority score Set of users pointing to media file m Hubness score Rating of user u for media file m Average trust of the direct connected users to user u Set of media files to which user u points Coefficient that weights the influence of the two terms, in range [0, 1] MHITS: Expert Ranking Algorithm Rashed, Balasoiu, Klamma: Robust Expert Ranking in Online Communities - Fighting Sybil Attacks. 8th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing. Pittsburgh, United States, 2012.
    • Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 17 Learning Layers Countermeasures Against Sybil Attack SybilGuard [Yu et al. 2006] SybilLimit [Yu et al. 2008] SumUp [Tran et al. 2009] Protocol type Decentralized Decentralized Centralized Accepted Sybils per attack edge  SumUp Method [Tran et al. 2009] — Adaptive vote flow aggregation technique — Assigns and adjusts link capacities in the trust graph to collect the votes — Include at most a few votes from Sybil — Include most votes from honest users
    • Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 18 Learning Layers Prune the trust network Capacity assignment for resulting trust network Collect votes Compute aggregate votes Detect bogus votes Assign negative history Delete bogus links and add back pruned links Call MHITS ranking algorithm Input : Rating network Integration of SumUp with MHITS Call MHITS ranking algorithm Robust ranking results Cmax =6 Assign levels for trust network users Input : Trust network 3 1 3 1 0 1 1 0 0 0 0 L0 L1 L2 L3 0 0 0 These steps are applied for each media rated
    • Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 19 Learning Layers TRUST IN EXPERTS
    • Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 20 Learning Layers Use Case: Media Distribution Networks THOMSON REUTERS ZEITUNG FÜR DEUTSCHLAND
    • Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 21 Learning Layers Trust Management: Network Construction Approach Consumers Mediator Sources  Basic building block Renzel, Rashed, Klamma: Collaborative Fake Media Detection in a Trust-Aware Real-Time Distribution Network. 2nd Workshop on Semantic Multimedia Database Technologies 2010, pp. 17–28.
    • Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 22 Learning Layers  Aggregate for m to decide action regarding i  Trust update algorithm Trust Management: Authenticity Ratings and Trust Symbol Description Mediator Information (media) item Rating of source s towards media item i Trust level of mediator m towards source s Mediator publishes media item as x(fake, true)
    • Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 23 Learning Layers  Trust inference  TidalTrust [Golbeck 2005] — Social network topology is used — Use shortest paths — Use weighted average of trust — Accept ratings from only the highest rated neighbors  Modified TidalTrust algorithm — Directed graph for XMPP network — Stores assigned trust ratings  Extension to dynamic trust management [Gans 2008] — Inclusion of temporal dimension and confidence — Inclusion of distrust: not inverse of trust Trust Management: Trust Inference and Dynamicity of Trust SourceMediator Trustworthy? Rashed, Renzel, Klamma, Jarke: Community and trust-aware fake media detection. Multimedia Tools and Applications, pp. 1–30, 2012
    • Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 24 Learning Layers Evaluation of Trust Management  Experiment design — 16 participants rate authenticity of 20 images (10 fake, 10 authentic) — Evil group with 4 participants (should vote contrary) — Good group with 12 participants — Group membership kept secret Progression of the average trust rating of the good group Average trust rating of the good group Progression of the average trust rating of the evil group a Average trust rating of the good groupTime Time
    • Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 25 Learning Layers Conclusions & Outlook  Experts in Community Information Systems – Communities of Practice are natural resources for the development of expertise – International communities consists of heterogeneous experts with different roles – Community & expert identification are key processes on the Web – Amateurs and deceivers are future challenges  Openess of future expert databases – Robustness of established algorithms for expert identification – Computation and spread of trust essential for decision making – Near real-time support in modern mobile Web applications