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Future of Knowledge Management Systems

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  1. Future of Knowledge Management Systems Logan Buchanan December 8, 2005

  2. References • Ackerman, M. S (1994). Augmenting Organizational Memory: A Field Study of Answer Garden. • Ackerman, M. S. & McDonald, D.W. (1996) Answer Garden 2: Merging Organizational Memory with Collaborative Help. • McDonald, David & Ackerman, Mark. (2000)Expertise Recommender: A Flexible Recommendation System and Architecture. Proceedings of CSCW'00. ACM Press. • Bayardo, R., Agrawal, R., Gruhl, D., Somani, A. (2002) YouServ: A Web Hosting and Content Sharing Tool for the Masses. WWW2002, May 7-11, 2002, Honolulu, Hawaii, USA • Malone, Lai, & Fry. Experiments with Oval: A Radically Tailorable Tool for Cooperative Work • Lieberman, H. (1997). Autonomous Interface Agents. ACM Conference on Human-Computer Interface [CHI-97], Atlanta, ACM Press. • Voss, A. and Kreiflets, T. (1997) SOAP: Social Agents Providing People With Useful Information. Proceedings of GROUP'97, ACM Press, pp. 291-298. • Shardanand, U. & Maes, P. (1995) Social information filtering: algorithms for automating "word of mouth". Conference proceedings on Human factors in computing systems. ACM Press. • Maes, P. (1994) Agents That Reduce Work and Information Overload. Communications of the ACM, 37(7), 31-40. • Berners-Lee, T., Hendler, J. and Lassila, O. (2001) The Semantic Web. Scientific American, May 2001. • Stojanovic, Nenad & Handschuh, Siegfried. (2002) A Framework for Knowledge Management on the Semantic Web. Proceedings of the World Wide Web conference 2002. Honolulu. • Yu, Bin & Singh, M. (2002) An Agent-Based Approach to Knowledge Management. CIKM'02. McLean, VA. ACM Press.

  3. Field study Group of users and group of experts. Users Need for speed Social network and information retrieval system worked well together. Status implications. Experts Some refused to answer based on their workload. Incentives questionable. Answer Garden 2 No separate groups. Contextualize answers. Escalation. Being anonymous is optional. Answer Garden

  4. Expertise Recommender • Architecture open and flexible enough to address different organizational environments. • Organizationally specific implementations. • User profiles created from organizationally relevant data sources such as work products. • Allows for user choice. • Recommendations can be revisited and escalated if necessary.

  5. YouServ • Pool existing desktop computing resources for high quality web hosting and file sharing. • Assigned a domain name. • Pool resources of a group - content can be accessed when your computer is off. • Can publish even if behind a firewall. • Low cost. • Alternative to e-mailing attachments. • Photo sharing is predicted most common use.

  6. Oval • Create applications by combining and modifying objects, views, agents, and links (or Oval). • Tailorable = end users can modify a working system (such as a spreadsheet), changes are made in the context of a working application • Radical = large changes can be made • User interface is simple and provides a “large amount of functionality for creating and modifying a wide range of applications”

  7. Oval cont. • gIBIS - a tool for helping a group explore and capture the qualitative factors that go into making decisions • Sibyl - goals for decisions and previous decisions • Coordinator - an e-mail based system that helps people structure conversations and track tasks. • Lotus Notes • Intelligent Lens - automatically filters and sorts incoming e-mail • Answer Garden • And… a database of people, an organization chart, a project management system, a system for tracking software bug reports, a system for supporting marketing decision making, a system for supporting quality management processes in manufacturing, and a workflow system for purchase-order approval.

  8. Autonomous Interface Agents • Interface agents = software that actively assists a user in operating an interactive interface • Autonomous agents = software that takes action without user intervention and operates concurrently • Letizia • An autonomous interface agent for Web browsing. • Records URLs chosen by the user and reads the pages to compile a profile of the user’s interests. • Work best in situations where their decisions are not critical, such as web browsing.

  9. SOAP • Social agents interact with one another on behalf of their clients • Examples: agents for electronic markets, workflow agents, information brokers • New users register in order to obtain a personal user agent. A user agent links the user and the other agents. • For each query, a task agent is created. • There are also group agents, recommender agents, and search agents, directory agents, etc.

  10. Social Information Filtering • Social information filtering automates the process of “word-of-mouth” recommendations. • General trends and patterns within the preferences of a person and between groups • Considers thousands of people and thousand of items • Personalized recommendations from databases based on similarities between the interest profile of the user and those of other users. • Ringo, makes personalized recommendations for music albums and artists. • As more people use the system, Ringo is able to make better predictions.

  11. Agents • Building Agents • Two main problems • Competence: how does an agent acquire knowledge? • Trust: how can the user feel comfortable delegating tasks to an agent? • One approach (as used in Oval) is to make the end-user program the interface agent. This does not meet the competence criterion. • Another, the knowledge-based approach, gives the agent extensive background knowledge about the application and the user. Both competence and trust are problems in this approach. • Use machine learning techniques

  12. The Semantic Web • An extension of the current Web, in which information is given well-defined meaning. • Computers must have access to structured collections of information. • XML and RDF • Ontologies • Digital signatures

  13. KM on the Semantic Web • Semantic Web can serve as a platform for developing knowledge management systems. • Problem: How to represent knowledge in a machine-understandable form, so that appropriate knowledge can be found by agents? • Use a conditional statement for the semantic annotation of knowledge sources. Statements used in the annotation can be put into the context of each other, which leads to efficient searching.

  14. An Agent Based Approach to Knowledge Management • MARS, a multiagent referral system for knowledge management • MARS assigns an agent to each user • Agents facilitate their users’ interactions • Manage their personal social networks • Agents cooperate with one another