Joining Knowledge Representation and Management in Digital ... · Joining Knowledge Representation...
Transcript of Joining Knowledge Representation and Management in Digital ... · Joining Knowledge Representation...
Joining Knowledge Representation and Knowledge Management
in Digital Libraries
Katrin
Weller, Isabella Peters, Wolfgang G. Stock
Dept. of Information ScienceInstitute for Language and Information
Heinrich‐Heine‐University Düsseldorf, Germany
International Conference on Digital Libraries, Delhi, India, February 2010
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Objectives & Outline
Theoretical approach: Combining…– knowledge management systems (KMS) – with knowledge representation (KR)– in digital libraries (DL).
5
main
research questions:
focusing on…– implicit and explicit knowledge– documents‘
space
– knowledge organisation
systems (KOS) types– employees
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KM, DL and KR?
Knowledge Management (KM)
Knowledge Representation (KR)
•
administration of internal and external information
e.g. knowledge sharing
and information dissemination
•
social methods + technical systems (KMS)
• implicit + explicit knowledge
Digital Libraries (DL)
?
?
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KM, DL and KR?
Knowledge Management (KM)
Knowledge Representation (KR)
•
administration of internal and external information
e.g. knowledge sharing
and information dissemination
•
social methods + technical systems (KMS)
• implicit + explicit knowledge
Corporate DL!•
corporate memory +
retrieval system•
“repositories of information
wherein contents can be searched or browsed”
(Pejtersen
et al., 2008)
Digital Libraries (DL)
?
?
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KM, DL and KR?
Knowledge Management (KM)
Knowledge Representation (KR)
•
administration of internal and external information
e.g. knowledge sharing
and information dissemination
•
social methods + technical systems (KMS)
• implicit + explicit knowledge
Corporate DL!•
corporate memory +
retrieval system•
“repositories of information
wherein contents can be searched or browsed”
(Pejtersen
et al., 2008)
Digital Libraries (DL)
?
• content descriptive metadata •
supporting access to documents: searching and
browsing based on knowledge organization systems (KOS) folksonomies, classification systems, thesauri etc.
?
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Explicit & Implicit Knowledge (Question 1 & 2)
1.
Which knowledge representation methods are appropriate to map explicit
knowledge?
(externalized) documents + KOS
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2.
Which knowledge representation methods are appropriate to map implicit
knowledge?
people (as documents) + folksonomies
Explicit & Implicit Knowledge (Question 1 & 2)
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Documents‘
Space (Question 3)
3.
Are all documents equal in regard to knowledge management? If not, how can we model the documents'
space of a company?
Based on: Krause (2006)
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KOS Types (Question 4)
4.
What kinds of KOS (which can be applied in digital libraries) do exist? Are there differences in the semantic richness of
the types of KOS?
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Emergent Semantics (Question 5)
5.
Can we construct a corporate KOS by using the employees' tags?
Tag co‐occurences
as basis
for detecting hidden
semantic relations.
http:www.bibsonomy.org Power Tags I
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Emergent Semantics (Question 5)
Co‐occurences:
Approach for detecting hidden semantic relations in texts.
Tags co‐occurring with “Web 2.0”:
Power Tags II
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Emergent Semantics (Question 5)
Co‐occurences:
Approach for detecting hidden semantic relations in texts.
Tags co‐occurring with “Web 2.0”:
Power Tags II
Intellectual identification of semantic relations
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Conclusion
DL for KM
KR
Emergent
Semantics
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Conclusion
• Digital Libraries can be used as Knowledge Management Systems. KOS support precise access to documents within DL.
• For handling implicit knowledge, we have to go beyond technical solutions and focus on the people: as documents
and as actors in social (tagging) applications.
• Document types within the DL have to be treated differently: the Shell Model
classifies different documents based on
importance.
• Different document types may be indexed with different KOS types.
• Improvement of lightweight KOS is possible with methods of emergent semantics
and semantic upgrades.
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We are looking forward to your comments & questions
Isabella Peters: isabella.peters@uni‐duesseldorf.deWolfgang G. Stock: stock@phil‐fak.uni‐duesseldorf.deKatrin
Weller: weller@uni‐duesseldorf.de
Greetings from Düsseldorf!
This presentation is available on SlideShare: http://www.slideshare.net/katrinweller
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References
• Krause, J. 2006. Shell Model, Semantic Web and Web Information Retrieval. In Information und Sprache, pp. 95‐106, edited by Harms, I., Luckhardt,
H.D., and Giessen, H.W. Munich, Germany: Saur.
• Pejtersen, A.M., Hansen, P., and Albrechtsen, H. 2008. A Work‐Centred Approach to Evaluation of a Digital Library: Annotation System for
Knowledge Sharing in Collaborative Work. World Digital Libraries
1(1): 1‐17.
• Peters, I., and Weller, K. 2008a. Paradigmatic and Syntagmatic Relations in Knowledge Organization Systems. Information –
Wissenschaft und Praxis
59(1): 100‐107.
• Peters, I., and Weller, K. 2008b. Tag Gardening for Folksonomy Enrichment and Maintenance. Webology
5(3).
• Polanyi, M. 1967. The Tacit Dimension.
Garden City, NY: Doubleday (Anchor Books).
This is a selection references. Please see the conference proceedings for a complete list.