2. Markup and Validation Agents in Vijjana – A Pragmatic model for Self- Organizing, Collaborative, Domain- Centric Knowledge Networks S. Devalapalli, R. Reddy, L. Wang, S. ReddySIPLab, Department of Computer Science & Electrical EngineeringWest Virginia University, Morgantown, WV 26506, USA

3. Presentation Outline Motivation Vijjana Architecture Keyword Extraction Vijjana Browser Extensions Markup and Validation Agents Conclusion and Future Work

4. Motivation-Knowledge acquisition Process People gain knowledge through thousands of ways People accumulate knowledge in a systematic way People form their own knowledge network A large knowledge network are formed by collaboration

5. Motivation -Methodology in Knowledge acquisition Management Science on Knowledge organization Machine facilitates people to gather knowledge Collaborative channel is needed in communication Knowledge network publication

6. Vijjana Defined as a Pragmatic model for Collaborative, Self- Organizing, Domain-Centric Knowledge Networks A Semantic web A portal for collaboration A discussion forum And much more!

7. The Vijjana Model Vijjana-X = {J, T, R| dA, oA, cA, vA, sA, rA}; where X = the domain name, J= the collection of JAN’s in the Vijjana-X, T = the Taxonomy OR pattern set used for classification of JAN’s, R= the domain specific relations; dA = the discovery agent which finds relevant JAN’s, oA = the organizing agent which interlinks the JAN’s based on R, cA = the consistency/completeness agent, vA = the visualization agent, sA = the search agent, rA = the rating agent.

8. Vijjana Architecture- A standard way to exploit the knowledge Find Organize Update Maintain Consistency and Completeness Distill Tools for Visualization Present on demand contextually relevant knowledge!

9. Vijjana Client Interface Architecture

10. Vijjana Architecture-Knowledge Representation Semantic Networks Logic Frames

11. graph views of the Vijjana-Computer Science network

12. How useful is Vijjana No unproductive browsing sessions anymore Search by concept, not by keywords Semantic visualization Social networking Receive alerts on topics of interest Combine resources on the Web and a user’s local machine to form a “User JAN Space” Integrate and share “JAN Spaces” among users

13. Vijjana Network

14. Vijjana Markup Agents--Web Interface Prototype Browse JAN’s with in the user interest. Comment on the JAN, for discussion. Rate the JAN, to get best & useful content. Visualization of Taxonomy, for addition of JAN’s manually. Visualization of Knowledge Domains for easy navigation and User friendly search.

15. Keyword Extraction Effectively summarize long documents Provide a context to the document Very valuable in web advertising Vijjana tags JANs with keywords describing them Examples of keyword assignment and usage include youtube, Gmail, search engines etc

16. Keyword extraction in Vijjana KEA algorithm used Simple and effective algorithm based on the Bayesian model Domain specific keyword extraction Less overhead in training needed Available at http://www.nzdl.org/kea

17. Vijjana Browser Extensions Firefox browser required Extensions are provided as toolbar buttons and menus Extensions must be downloaded and installed on the user’s browser Current extensions provide navigation to Vijjana homepage, the Markup feature and Validation of JANs in the database

18. Vijjana Extensions in Firefox

19. Markup Process Part of building up the database Similar to, but more involved than bookmarking Process of adding meta-data to a JAN Pages added to the database simply by clicking the “Markup” button in the browser extension Invokes the Organizing Agent which adds a JAN to the database

20. Markup Example

21. Markup Example (contd.)

22. Markup Process

23. Validation Agent There may be hundreds or thousands of JANs in a user’s space JANs are usually URLs or documents that might relocate or cease to exist JANs must be validated (manually or automatically) The visualization must reflect most recent state of the JAN

24. Validation Agent Validation can be time and memory intensive task Time taken to validate is proportional to the number of JANs in a user’s space Best carried out as an overnight operation

25. Validation Process Flow

26. Validation Confirmation

27. Database view before Validate process

28. Database view after Validation

29. Conclusion A Firefox browser extension with options to navigate to Vijjana Homepage, Markup the current page and validate the user’s JANs has been developed The KEA model was trained using a set of 24 documents pertaining to various technical domains and the results have been good

30. Future Work A heuristic based key extraction algorithm called VKE is under evaluating Automated periodic JAN “Validator” which runs as clients work instead of server work to manage load balance. A series security mechanism should be applied for protecting privacy issue.

31. References 1. Vijjana – A Pragmatic model for Collaborative, Self-Organizing, Domain-Centric Knowledge Networks - Reddy, Dr. Ramana. Morgantown : IKE08, 2008. 2. KEA: Practical Automatic Key phrase extraction - Witten I.H., Paynter G.W., Frank 3. The KEA project - http://www.nzdl.org/Kea/ 4. Mozilla Developer Center – Building an extension, http://developer.mozilla.org/en/docs/Building_an_Extension 5. Twine – Radar Networks Inc., http://www.twine.com/http://www.twine.com/ 6. Del.icio.us social bookmarking - http://del.icio.us/

32. Questions?