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

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