Semantic - Enhanced Community Modelling to Support Knowledge Sharing

School of somethingFACULTY OF OTHER

School of ComputingFACULTY OF ENGINEERING

Semantic - Enhanced Community Modelling to Support Knowledge Sharing

Kleanthous Styliani

www.comp.leeds.ac.uk/stellak

School of ComputingFACULTY OF ENGINEERING

Oct 30th 2007 Reading Group Session

Overview

•This Research•Algorithms•Study•Initial Results

•Community•Relationship Model•Centrality•Individual User Model

This Research…

Research Focus:Provide holistic personalised support in VC

Main Assumptions:

• Providing adaptation tailored to the community as a whole will help the community function better.

• By promoting the building of TM, development of SMM, and establishment of CCs and identifying CCen inside the community, will improve the functioning of this community


This Research

Algorithms

Study

Initial Results

This Research…

Research Questions

R1: How to extract a computational model to represent the functioning and evolution of the community as a whole, using semantically enhanced tracking data?

R2: Using that model, how to provide personalised functionality to support the development of TM, building of SMM, establishment of CCs and identification of CCen?

R3: How can personalised support of the above processes affect the functioning of the community?


This Research

Algorithms

Study

Initial Results

TM

CCs

CCen

SMM

Community Model

Com

mun

ity M

odel

Acq

uisi

tion

Com

munity M

odel Application


This Research

Algorithms

Study

Initial Results

The Example Community - BSCW


This Research

Algorithms

Study

Initial Results


Input Formalisation…ENVIRONMENT E: <HF, F, R, M, D>

Folder F:

HF : Taxonomy of Folders

<FTitle, FCreator, FDescription, FDate>

Resource R: <RCreatedData, RMetadata>

RCreatedData: <RFolder, RName, RDescription, RRating, RCreator, RDate, RAssessor, RReader>

RMetadata: <RTitle, RAuthor, RSource, RKeywords, RDatePublish>

Based on Dublin Core Metadata element set

Member M: <MName, MEmail, MDateJoin>

This Research

Algorithms

Study

Initial Results

Community Model

Relationships Model

User Interests Participation

Cognitive Centrality

Relationships Personal Hierarchies

ReadRes

InterestSim

UploadSim

ReadDisc

Individual User Models

Community Context

Popular Topics

Peripheral Topics

Cognitively Central

Members

ReadSim


This Research

Algorithms

Study

Initial Results

Modelling Relationships…

ReadResRelationship because A read resources uploaded by B

σ ( , )i jr rN ReadRes i j

1

( , )

i jr

i

N

ri

ReadRes i j V

1cN2cN

T),rdsSim(RKeywoV iri

n321 cccc .....NN,N,N:T

WordNet

irV


This Research

Algorithms

Study

Initial Results


ReadSim & UploadSimReadSim: Relationship because A reads resources similar to those B reads.

UploadSim: Relationship because A uploads resources similar to those B uploads.

( , )( , )i jr r i j

c csimV Sim N N

n321 cccc .....NN,N,N:T

( , )i jr rsimReadSim V

iCN j

CN

WordNet

i jr rsimV( , )


This Research

Algorithms

Study

Initial Results


InterestSimSimilarity between two members’ interests

ii kσk),w(L IΙ

),Sim(mInterestSi ji

WordNet

i jSim( , )


This Research

Algorithms

Study

Initial Results

Capturing Centrality…

5

1 1

( ) α ( , )n

D zi z

C j i j

mnMi

ReadRes

ReadSim

UploadSim

InterestSim

ReadDisc 1n

,aC

n

1iki

(pk)

ppD

This Research

Algorithms

Study

Initial Results



•Run from Oct 2005 – Dec 2006

•BSCW data anonymised & converted into .txt

•Extracted data using Java

•Data stored on a MySQL Database

•Input to algorithms to extract the Community Model

This Research

Algorithms

Study

Initial Results

The Study…


Members 34

Isolates 8

Only uploading 4

Only downloading 13

Uploading & Downloading 9

Total Resources Uploaded 244

Initial Results•Community•Relationship Model•Centrality•Individual User Model

Overview…


0

30

60

90

120

150

180

Members

No.

of R

esou

rces

Uploading Dow nloading

Activity…


Uploading…


020406080

100120140

1 2 3 4 5 6 7 8 9 10111213141516171819202122232425262728293031323334

Members

No.

of R

esou

rces

01/10/2005 - 31/12/2005 01/01/2006 - 28/02/200601/03/2006 - 31/05/2006 01/06/2006 - 31/08/200601/09/2006 - 31/12/2006


Downloading…


0

10

20

30

40

50

1 2 3 4 5 6 7 8 9 10111213141516171819202122232425262728293031323334

Members

No.

of R

esou

rces

01/10/2005 - 31/12/2005 01/01/2006 - 28/02/2006 01/03/2006 - 31/05/200601/06/2006 - 31/08/2006 01/09/2006 - 31/12/2006


ReadRes…


Support:

•Identify complementary knowledge

•Who holds information I am interested in?

Improve TM


Reading Only…


Have ReadRes with the same members

Support:

•Identify people who are interested in what I am interested.

Encourage Collaboration

-Building SMM

-Improve TM


Member 5


Member 2 Member 9

Only downloading

Have exactly the same ReadRes relations

Support:

•Encourage collaboration

•Motivate contribution


ReadSim…


•Support:

•Identify relationships that members are not aware of

•Who is reading resources similar to those I am reading?

•Who is interested in similar resources as I am?

Improve TM



Reading resources from the same people

Support:

•Develop awareness of this similarity

Improve TM/SMM

Encourage collaboration

Facilitate knowledge Sharing



UploadSim…

Very strongly connected

Support:

•Identify people who are not uploading & encourage them to contribute

•Make people aware of their similarities

Improve SMM/TM

Support Collaboration


InterestSim…


Support:

•Identify interest similarity & complementarities

•Who has interests similar to a given member?

Motivate contribution

Encourage collaboration

Improve SMM/TM


Cognitive Centrality…


0

5

10

15

20

25

1 2 3 4 5 6 7 8 9 10111213141516171819202122232425262728293031323334Members

Support:

Where important knowledge is located?

Where unique knowledge is located?

Improves TM/SMM

Motivation mechanism



Member 12 uploaded only one resource

29.4% of the community read his resource

Support:

•Display similar members, motivate to contribute/ read

•Use ReadSim to motivate

Improve TM/SMM

ReadRes Ego Network UploadSim Ego Network


Newcomer Integration…


0

5

10

15

20

25

30

35

40

01/10/2005 -31/12/2005

01/01/2006 -28/02/2006

01/03/2006 -31/05/2006

01/06/2006 -31/08/2006

01/09/2006 -31/12/2006

Uploading Dow nloading

ReadRes Ego Network of Member 19

Support:

•Use ReadRes to help member integrate.

•Who holds knowledge important to this member?

Improve TM


Member 33 uploaded 11 resources

Never read a resource

Support:

•Help members like 33 to integrate

•Identify similar members & motivate this member to contribute

Improve TM/SMM

Encourage Collaboration

Support Newcomer IntegrationOct 30th 2007 Reading Group Session

Ego Network

UploadSim & InterestSim Ego Network

Integration Problem…


Future Work…

•Ontology integration

• What will it be different?

•Community model evaluation

•Model community changes over time

• Relationships

• Individual

•Extend an existing system

•Evaluation with users


This Research

Algorithms

Study

Initial Results

Summary…

•TM, SMM, CCen can be used to support Virtual Communities

•Modelling semantic-enhanced relationships can help us to identify what support is needed

•A holistic support may provide the foundations for a sustainable virtual community


This Research

Algorithms

Study

Initial Results

Semantic - Enhanced Community Modelling to Support Knowledge Sharing

Documents

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