1. Ruth Cobos, Xavier Alamán & Jose A. Esquivel Computer Science Department Universidad Autónoma de Madrid, Spain Ruth.Cobos@ii.uam.es Sharing Knowledge in the Net through a Collaborative System

2. SUMMARY Introduction Our proposal: KnowCat Some experiences Conclusions and Future Work Introduction Our proposal: KnowCat Some experiences Conclusions and Future Work

3. Introduction Our proposal: KnowCat Some experiences Conclusions and Future Work Introduction Our proposal: KnowCat Some experiences Conclusions and Future Work SUMMARY

4. INTRODUCTION Our proposal: A Web-based groupware system - called KnowCat - which allows the crystallisation of collective knowledge as the result of user interactions. âWe need systems that implement models for knowledge management. âMost solutions are oriented to the management of corporate knowledge: Microsoft ® SharePoint TM Portal Server 2001, Meta4 KnowNet ®©, Zaplet Appmail Suite, etc. âWe are interested in tools for supporting academic communities.

5. Introduction Our proposal: KnowCat Some experiences Conclusions and Future Work Introduction Our proposal: KnowCat Some experiences Conclusions and Future Work SUMMARY

6. Virtual Communities of Experts Knowledge Crystallisation Process KnowCat intends to capture established knowledge about a given topic in the Web, in an asynchronous and distributed way, and without the need of an editor for managing the task KNOWCAT: KNOWLEDGE CATALYSER

7. CHARACTERISTCS OF KNOWCAT âWeb-based client-server system. âGroupware tool: asynchronous work. âPortability âAdaptability âScalability

8. SOME APPLICATIONS OF KNOWCAT KnowCat allows communities to build places called KnowCat sites or KnowCat nodes, where we can find the relevant knowledge about an area or topic. âGeneration of quality educational materials as the automatic result of student interactions with the materials. Generation and maintenance of the collective knowledge of a research group.

9. AN EXAMPLE SCREEN OF THE SYSTEM The right side of the screen shows descriptions of a topic of the knowledge area The left side of the screen shows the structure of the knowledge area (KnowCat site) Author name and arriving date identify these contributions

10. KNOWLEDGE IN THE SYSTEM Knowledge tree structure. Each node or topic is refined: a list of other nodes are subjects of the current topic. In each moment the descriptions of a topic are competing with each other for being considered as the best description of the topic. Descriptions of a topic: a set of addresses of Web documents with such descriptions.

11. âThe Knowledge Crystallisation Process affects both the tree structure and descriptions. âThe use of descriptions, the opinions of other users about them and the time they have endured in the system, decide whether these elements of knowledge are useful, in which case they will stay longer in the system; or if they are useless, in which case they will eventually disappear from the system. KNOWLEDGE CRYSTALLISATION PROCESS (i)

12. âThe knowledge crystallisation process considers these aspects: As important as the number of users or their opinions is the quality of these users. We would like to give more credibility to the opinions of experts than to the opinions of occasional users. XWhen a new node is created the system has the Bootstrap problem because in the beginning we don't have enough critical mass in terms of people contributions, to make the system run. Virtual Communities KNOWLEDGE CRYSTALLISATION PROCESS (ii)

13. VIRTUAL COMMUNITIES (i) âVirtual communities of experts are constructed in terms of the knowledge tree, so there is a virtual community for each node of the tree. âThese virtual communities appear as a natural way of handling the knowledge area construction.

14. VIRTUAL COMMUNITIES (ii) âTwo elements that may crystallise in the system: âVirtual communities behave in a different way when they are just beginning, an also in their latter days, so KnowCat proposes a maturation process that involves several phases. XTopic contents: when a user contributes with a description of a topic and it crystallises, the author receives a certain amount of "votes" that he or she may apply for the crystallisation of other articles of other authors in the virtual community where his or her crystallised paper is located. XEvolution of tree structure: if a member of a virtual community proposes to add a new subject to a topic, to remove a subject from a topic or to move a subject from one topic to another topic. It will be necessary a minimum quorum of positive votes from other members of the community for changing the structure.

15. KNOWLEDGE EVOLUTION If activity raises to a minimum again, the node may switch to the active status again. A new node is created, there may not be many accredited experts to form the virtual community: the node works in the supervised mode, there is a steering committee in charge of many of the decisions that is distributed in later phases. Activity in the knowledge structure. SUPERVISED PHASE The steering committee may decide to advance the node to the active status. In this moment, the committee is dissolved, and knowledge crystallisation is based on virtual communities. Activity in the contents of the node. ACTIVE PHASE The active community may reach the stable phase: changes are rare, and most of the activity is consultation and few contributions arrive. STABLE PHASE

16. SUMMARY Introduction Our proposal: KnowCat Some experiences Conclusions and Future Work Introduction Our proposal: KnowCat Some experiences Conclusions and Future Work

17. SOME EXPERIENCES â"Operating Systems" KnowCat site. It has been created by several classes of students enrolled in an Operating Systems course, at the Computer Science Department, during 3 years. â"Uncertain Reasoning" KnowCat site. It has been created by several classes of students of a graduate Computer Science course about "Uncertain Reasoning, during 3 years. â"Mathematics for Children's Training" KnowCat site. It has been created by students enrolled in "Mathematics for Children's Training", at the Pedagogy School, during 1 year.

18. INITIALLY COMMUNITY ABOUT OPERATING SYSTEMS (i) 200 Students âObjective: to check the hypothesis that when you get enough documents and enough votes from knowledgeable peers, the result is a reasonable description of the topic. Without Contents

19. MECHANISM & EVALUATION COMMUNITY ABOUT OPERATING SYSTEMS (ii) âThe instructor graded papers independently, and this grading was used to check the adequacy of the voting system to capture the quality of the documents. 3 Votes to

20. AT THE END OF THE FIRST YEAR COMMUNITY ABOUT OPERATING SYSTEMS (iii)

21. CONCLUSIONS FROM THE FIRST YEAR COMMUNITY ABOUT OPERATING SYSTEMS (iv) For most topics the two most popular papers collected 50% of the total votes: remarkable consensus. In 10 out of the 12 topics at least two of the three papers selected by the instructor as the three best papers were also selected by the students as such: consensus in quality articles.

22. NEXT YEARS COMMUNITY ABOUT OPERATING SYSTEMS (v) âThey could score not only veteran papers but also the new ones through the system voting mechanism. 3 Votes to

23. CONCLUSIONS FROM THE LAST YEARS âKnowledge in the system is in evolution and is possible for a document that arrives later to crystallise and achieve the first positions of the rank. 50 % of the topics of the initial tree structure have in their first positions documents that have been added during the second and third years 20 % of the topics had little participation 10 % of the topics may contain the best description since the first year In 50 % of the topics, the description selected as the best during the first year obtained so high crystallisation degree that descriptions added in following years were not able to reach it COMMUNITY ABOUT OPERATING SYSTEMS (vi)

24. COMMUNITY ABOUT UNCERTAIN REASONING (i) âObjective: to check the feasibility of a group of students making a good structure by using our proposed voting mechanism.FROM â10/15 students each year. âNo initial topics. Only the tittle of the course: Uncertain Reasoning as the root node.MECHANISM âThey had to propose structures to the knowledge area and give their opinions about the proposals of other classmates. âThey could add documents about the topics and vote them.

25. TO The number of topics in the current structure is almost twice the initial number of topics of the structure that was created the first year. The tree structure is five levels deep. In the opinion of the instructor: the resulting structure contains a credible overview of the topics of the course, and the crystallised papers show a high quality. COMMUNITY ABOUT UNCERTAIN REASONING (ii)

26. SUMMARY Introductiogn Our proposal: KnowCat Some experiences Conclusions and Future Work Introductiogn Our proposal: KnowCat Some experiences Conclusions and Future Work

27. CONCLUSIONS âKnowCat is a Web-based system that allows us sharing, evaluating and structuring community knowledge. This is possible through the knowledge crystallisation process, supported by virtual communities of experts. âThe experiences have shown evidence that the system is useful for motivating communities in sharing their knowledge and incrementally constructing an active repository of knowledge of reasonable quality. âKnowCat enables the building of Web sites where relevant knowledge about an area or topic can be found.

28. FUTURE WORK An author can submit another document in the same topic as an improvement of a previous one: version document New Knowledge Units âTo provide a mechanism that extends the way to express the user opinions in the system: An author can annotate another authors document, thereby making a version of it: note of a document http://www.ii.uam.es/~rcobos/investigacion/knowcat/eng/fKC.htm