Ruth Cobos, Xavier Alamán & Jose A. Esquivel Computer Science Department
Universidad Autónoma de Madrid, Spain [email protected]
Sharing Knowledge in the Net
through a Collaborative System
SUMMARY
Introduction
Our proposal: KnowCat
Some experiences
Conclusions and Future Work
Introduction
Our proposal: KnowCat
Some experiences
Conclusions and Future Work
Introduction
Our proposal: KnowCat
Some experiences
Conclusions and Future Work
Introduction
Our proposal: KnowCat
Some experiences
Conclusions and Future Work
SUMMARY
INTRODUCTION
Our proposal:
A Web-based groupware system - called KnowCat - which allows the crystallisation of collective knowledge as the
result of user interactions.
We need systems that implement models for knowledge management.
Most solutions are oriented to the management of corporate knowledge: Microsoft® SharePointTM Portal Server 2001, Meta4 KnowNet®©, Zaplet Appmail Suite, etc.
We are interested in tools for supporting academic communities.
Introduction
Our proposal: KnowCat
Some experiences
Conclusions and Future Work
Introduction
Our proposal: KnowCat
Some experiences
Conclusions and Future Work
SUMMARY
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
CHARACTERISTCS OF KNOWCAT
Web-based client-server system.
Groupware tool: asynchronous work.
Portability
Adaptability
Scalability
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.
Generation 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.
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
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.
The “Knowledge Crystallisation Process” affects both the tree structure and descriptions.
The 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)
The 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.
When 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)
VIRTUAL COMMUNITIES (i)
Virtual communities of experts are constructed in terms of the knowledge tree, so there is a virtual community for each node of the tree.
These virtual communities appear as a natural way of handling the knowledge area construction.
VIRTUAL COMMUNITIES (ii)
Two elements that may crystallise in the system:
Virtual 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.
Topic 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.
Evolution 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.
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
PHASEThe 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
SUMMARY
Introduction
Our proposal: KnowCat
Some experiences
Conclusions and Future Work
Introduction
Our proposal: KnowCat
Some experiences
Conclusions and Future Work
SOME EXPERIENCES
"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.
"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.
"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.
INITIALLYINITIALLY
COMMUNITY ABOUT OPERATING SYSTEMS (i)
200 Students
Objective: 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
MECHANISM & EVALUATIONMECHANISM & EVALUATION
COMMUNITY ABOUT OPERATING SYSTEMS (ii)
The 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
AT THE END OF THE FIRST YEARAT THE END OF THE FIRST YEAR
COMMUNITY ABOUT OPERATING SYSTEMS (iii)
CONCLUSIONS FROM THE FIRST YEARCONCLUSIONS 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.
NEXT YEARSNEXT YEARS
COMMUNITY ABOUT OPERATING SYSTEMS (v)
They could score not only veteran papers but also the new ones through the system voting mechanism.
3 Votes to
3 Votes to
CONCLUSIONS FROM THE LAST YEARSCONCLUSIONS FROM THE LAST YEARS
Knowledge 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)
COMMUNITY ABOUT UNCERTAIN REASONING (i)
Objective: to check the feasibility of a group of students making a good structure by using our proposed voting mechanism.
FROMFROM
10/15 students each year.
No initial topics. Only the tittle of the course: “Uncertain Reasoning” as the root node.
MECHANISMMECHANISM
They had to propose structures to the knowledge area and give their opinions about the proposals of other classmates.
They could add documents about the topics and vote them.
TOTO • The number oftopics in the currentstructure is almosttwice the initial
number of topics of the structure that was created the first year.• The tree structureis five levels deep.
In the opinion of the instructor:the resulting structure contains a credible
overview of the topics of the course, and thecrystallised papers show a high quality.
COMMUNITY ABOUT UNCERTAIN REASONING (ii)
SUMMARY
Introductiogn
Our proposal: KnowCat
Some experiences
Conclusions and Future Work
Introductiogn
Our proposal: KnowCat
Some experiences
Conclusions and Future Work
CONCLUSIONS
KnowCat 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.
The 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.
KnowCat enables the building of Web sites where relevant knowledge about an area or topic can be found.
FUTURE WORK
An author can submit another document in the same topic as an improvement of a previous one: version document New Knowledge
Units
To provide a mechanism that extends the way to express the user opinions in the system:
An author can annotate another author’s document, thereby making a version of it: note of a document
http://www.ii.uam.es/~rcobos/investigacion/knowcat/eng/fKC.htm
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