a virtual catalyst in the knowledge acquisition process geraldo boz junior, tecpar milton pires...
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A Virtual Catalyst in the Knowledge Acquisition Process
Geraldo Boz Junior, TecparMilton Pires Ramos, Tecpar
Gilson Yukio Sato, UTFPRJulio Cesar Nievola, PUCPR
Emerson Cabrera Paraiso, PUCPR
The 23rd International Conference on Software Engineering and Knowledge Engineering (SEKE 2011)
Structure
Introduction
Tool Features
Virtual Catalyst Noctua
Introduction• AI – Artificial Intelligence
• Knowledge Based Systems• Analysis, diagnosis• Preservation of knowledge
• KA – Knowledge Acquisition• Expert + Knowledge Engineer• Problems: deadlines, expenses, time availability,
knowledge representation
• CKC – Collaborative Knowledge Construction• Multiple remote collaborators• Problems: authorship, validation
• Noctua = AI + KA + CKC
3
4
Knowledge Acquisition (KA)
Definition• Conceptual Knowledge x Procedural Knowledge [1]• Knowledge Acquisition is the explanation and the capture of
knowledge in a structured format. [2]KA techniques
• Interviews, simulation, scenarios [1] [3]Knowledge Representation
• Knowledge Pages, Production Rules [1] [2]Problems
• Faulty documentation, elicitation difficulty, disorganization, ignorance, availability [4]
5
Collaborative Knowledge Construction (CKC)
Distance Collaboration• Synchronous collaborative sessions x asynchronous collaboration
[5]Incentive to Collaboration
• Productivity awards, reputation inside the group, social translucence [6]
Effectiveness: stimulus and measurements• Fostering interaction, distribution of roles, metacognition [7]• Quantity of logins, produced artifacts, quantity of messages and
comments, etc. [8]Tool characteristics
• Web, simple, forum, questions, synchronous/asynchronous, authorship, search [9]
Building consensus• Authority, consensus [10]
6
Proactivity (CKC)
Intelligent Systems• Ability to understand and act on the environment according
to their own objectives. [5]• Perceptions and actions; memory, knowledge and goals;
planning and decision making [11]Profile of collaborators
• Interaction vary with interests, knowledge, history of activities [6]
Artificial element action• Familiarization, discussion [8]• Types of questions (“Evaluate...” , “What if...?”) [12]
Structure
Introdution
Tool Features
Virtual Catalyst Noctua
Introdution
8
Noctua Project
Input Variables
Base de Regras
Internal Variables
Constants
Auxiliary Conclusions
Output Conclusions
Terminal Conclusions
Inference Engine
HiperglossárioHyper Glossary
Rule Base
RuleBase
Hyper Glossary
Tags
?QuestionsInstant
Messages
!Comments
Images
Expert(s)
KnowledgeEngineer(s)
Log
Profiles
Project Memory9
Structure
Introduction
Tool Features
Virtual Catalyst Noctua Structure
Knowledge Page
11
Production Rule
12
Structure
Introduction
Tool Features
Virtual Catalyst Noctua
Tool Features
Tool Features
Rule Base
Hyper Glossary
Tags
?QuestionsInstant
Messages
!Comments
Images
Expert(s)
KnowledgeEngineer(s)
VirtualCatalyst
Log
Profiles
Project Memory14
Catalyst Action
If DIM >= lactation_initial_phase_limit DIM < lactation_end_limitThen last phase lactation
Rule Y
Profile
Tags
Rules
If SCC >= SCC_normal_limit SCC < SCC_high_limitThen high SCC
Rule X
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Catalyst Action
Rule ?
Mr. Expert, is it possible to conclude something from these conditions?
If SCC >= SCC_normal_limit SCC < SCC_high_limit
Then ???
DIM >= lactation_initial_phase_limit
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Structure
Introduction
Tool Features
Virtual Catalyst NoctuaVirtual Catalyst Noctua
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Experimentation
instigated17%
spontaneous83%
Entries/Rules
instigated23%
spon-taneou
s77%
Opinions
Project Gourmethttp://projetos.dia.tecpar.br/noctua
Pairing Foods and Wine
•9 distant collaborators•111 questions made by Noctua•204 instant messages
•50 rules and entries (17% instigated)•60 opinions validating knowledge (23% instigated)
This experimentation inspired an improvement in the tool, which now also integrates input variables to the rules and entries of the project.
Expected Results
• A method for Knowledge Acquisition with characteristics of Collaborative Knowledge Construction and a Virtual Catalyst.
• More efficient Knowledge Acquisition• Decrease the need to face meetings• Lower costs• Shorter development time• Procedural knowledge
integrated with conceptual knowledge
19
Conclusion
Work already done• Theoretical foundation• Defining tool features• Development of the tool (Noctua)
Work in progress• More experiments• Assessment of experiment results• New tool features
20
References
21
[1] MILTON, N.R Knowledge Acquisition in Practice, Springer-Verlag London Limited, 2007
[2] ROLSTON, D.W. Principles of Artificial Intelligence and Expert Systems Development. McGraw-Hill Book Co, 1988.
[3] GROVER, M.D. A Pragmatic Knowledge Acquisition Methodology. Psychological Review, 1982, pp. 1-3.
[4] MASTELLA, L.S. Técnicas de Aquisição de Conhecimento para Sistemas Baseados em Conhecimento, UFRGS, 2004.
[5] SCHWARTZ, D.G. Encyclopedia of Knowledge Management. Idea Group Reference, 2006.
[6] NABETH, T.; RODA, C.; ANGEHRN, A. e MITTAL, P. Using artificial agents to stimulate participation in virtual communities. ADIS International Conference CELDA (Cognition and Exploratory Learning in Digital Age), 2005, pp. 2-5.
[7] PETTENATI, M.C. e RANIERI, M. Informal learning theories and tools to support knowledge management in distributed CoPs. Proceedings of the 1st International Workshop on Building Technology Enhanced Learning solutions for Communities of Practice, held in conjunction with the 1st European Conference on Technology Enhanced Learning Crete, Greece, 2006, pp. 345-355.
[8] ANGEHRN, A.A. Designing Intelligent Agents for Virtual Communities. CALT Report 11-2004, 2004, pp. 1-29.
[9] NOY, N.F.; CHUGH, A. e ALANI, H. The CKC Challenge: Exploring Tools for Collaborative Knowledge Construction. IEEE Intelligent Systems, vol. 23, 2008, pp. 64-68.
[10] DIENG, R.; CORBY, O.; GIBOIN, A.; GOLEBIOWSKA J.; MATTA N. e RIBIÈRE M. Méthodes et outils pour la gestion des connaissances. Dunod, 2000.
[11] KENDAL, S e CREEN, M. An Introduction to Knowledge Engineering. Springer-Verlag London Limited, 2007.
[12] MCGRAW, K. e HARBISON-BRIGGS K. Knowledge acquisition: principles and guidelines. Prentice-Hall, Inc. Upper Saddle River, NJ, USA, 1989.
A Virtual Catalyst in the Knowledge Acquisition Process
Geraldo Boz Junior, TecparMilton Pires Ramos, Tecpar
Gilson Yukio Sato, UTFPRJulio Cesar Nievola, PUCPR
Emerson Cabrera Paraiso, PUCPR
The 23rd International Conference on Software Engineering and Knowledge Engineering (SEKE 2011)
Thank you!