 Copyright 2009 Digital Enterprise Research Institute. All rights reserved. Digital Enterprise Research Institute Expert (and Novice) Finding Within Shared Workspaces 03/02/2008 DERI Conference Room Peyman Nasirifard

Digital Enterprise Research Institute How do I do it? Live Demo! Challenges Conclusion and Future Work Questions Agenda

Digital Enterprise Research Institute What do we use?  We do NOT use  We do NOT use Wikipedia  We do NOT use Google (co-occurence, etc.)  We do NOT use DBLP  We DO use Online Shared Workspaces – Currently BSCW We use two main elements / components of shared workspace  Log file: Contains the transactions within shared workspace  Document: Provides input for expertise extractor How Do I Do It?

Digital Enterprise Research Institute How Do I Do It? II Analyzing documents to extract key phrases Analyzing log files to see which persons did what events on what documents Rule of Thumb: A user is expert in topic X, if s/he has created or revised a document that contains topic X. A user is familiar with topic Y, if s/he has just read a document that contains topic Y

Digital Enterprise Research Institute Live Demo! Demo-based computing: Demos are always better than boring presentations!

Digital Enterprise Research Institute Challenges Ambiguity: “output parameter” is not really an expertise!  There is no 100% accurate and automated Key phrase extraction algorithm from a document  Solution: We had a semi-automated approach and we removed the terms with low confidences  Future work: We will enable end users to remove inappropriate terms from their profiles and the algorithm can “learn” or “be trained” Organization Expertise profile vs. Person Expertise Profile  We noticed that in some organizations, a single person tackles always with shared workspace and this may generate some noises in expertise profiles  Solution: Building organization expertise profile besides person can address this issue Purifying Log files  Some log records are noisy: They do not have the pre-assumed structure  Solution: We defined some patterns and excluded the records that do not follow the patterns Expertise Granularity  Rule of Thumb is not fine-grained enough  Knud: Taking to account the version history of deliverables Similar phrases: “Semantic Web” and “SIOC” are from the same domain.  This issue is still a hot challenge in research community  Solution: We are trying to address this issue using directory searches like DMOZ or Google directory search

Digital Enterprise Research Institute Future Work Lots of enhancement can be considered  See previous slide Developed as a hobby  Later it attracted Ecospace consortium  Tomorrow it will be demonstrated in the review meeting

Digital Enterprise Research Institute Have a nice day! Thank you! Try tools yourself: Questions?