Collaborative knowledge engineering: Socialising expert systems

Debbie Richards

doi:10.1109/CSCWD.2007.4281510

Collaborative knowledge engineering: Socialising expert systems

Debbie Richards^*

^*Corresponding author for this work

School of Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

11 Citations (Scopus)

54 Downloads (Pure)

Abstract

Social software such as Wikis and WebLogs have enabled global knowledge sharing communities to emerge. As with most of the current Web content, the knowledge captured is for human consumption only and too unstructured to allow automated reasoning. On the other hand, while sharing and reuse of knowledge are often the goal behind the development of a knowledge base or ontology, there is still currently little support even at the level of a version control system that notifies users when a module being checked-in has been edited by someone else. A collaborative learning approach is introduced¹ which extends a combined rule and case based knowledge acquisition technique known as multiple classification ripple down rules to allow multiple users to view, define and refine a knowledge base over time and space.

Original language	English
Title of host publication	Proceedings of the 2007 11th International Conference on Computer Supported Cooperative Work in Design, CSCWD
Editors	Weiming Shen, Yun Yang, Jianming Yong, Igor Hawryszkiewycz, Jongkai Lin, Jean-Paul Barthes, Mary Lou Maher, Qi Hao, Minh Hong Tran
Place of Publication	Piscataway, NJ
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	635-640
Number of pages	6
ISBN (Print)	1424409632, 9781424409631
DOIs	https://doi.org/10.1109/CSCWD.2007.4281510
Publication status	Published - 2007
Event	11th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2007 - Melbourne, VIC, Australia Duration: 24 Apr 2007 → 28 Apr 2007

Other

Other	11th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2007
Country/Territory	Australia
City	Melbourne, VIC
Period	24/04/07 → 28/04/07

Bibliographical note

Copyright 2007 IEEE. Reprinted from Proceedings of the 11th international conference on computer supported cooperative work in design (2007). This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Macquarie Universityâ€™s products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.

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10.1109/CSCWD.2007.4281510

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Richards, D. (2007). Collaborative knowledge engineering: Socialising expert systems. In W. Shen, Y. Yang, J. Yong, I. Hawryszkiewycz, J. Lin, J.-P. Barthes, M. L. Maher, Q. Hao, & M. H. Tran (Eds.), Proceedings of the 2007 11th International Conference on Computer Supported Cooperative Work in Design, CSCWD (pp. 635-640). Article 4281510 Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/CSCWD.2007.4281510

Richards, Debbie. / Collaborative knowledge engineering : Socialising expert systems. Proceedings of the 2007 11th International Conference on Computer Supported Cooperative Work in Design, CSCWD. editor / Weiming Shen ; Yun Yang ; Jianming Yong ; Igor Hawryszkiewycz ; Jongkai Lin ; Jean-Paul Barthes ; Mary Lou Maher ; Qi Hao ; Minh Hong Tran. Piscataway, NJ : Institute of Electrical and Electronics Engineers (IEEE), 2007. pp. 635-640

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abstract = "Social software such as Wikis and WebLogs have enabled global knowledge sharing communities to emerge. As with most of the current Web content, the knowledge captured is for human consumption only and too unstructured to allow automated reasoning. On the other hand, while sharing and reuse of knowledge are often the goal behind the development of a knowledge base or ontology, there is still currently little support even at the level of a version control system that notifies users when a module being checked-in has been edited by someone else. A collaborative learning approach is introduced1 which extends a combined rule and case based knowledge acquisition technique known as multiple classification ripple down rules to allow multiple users to view, define and refine a knowledge base over time and space.",

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note = "Copyright 2007 IEEE. Reprinted from Proceedings of the 11th international conference on computer supported cooperative work in design (2007). This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Macquarie University{\^a}€{\texttrademark}s products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.; 11th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2007 ; Conference date: 24-04-2007 Through 28-04-2007",

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doi = "10.1109/CSCWD.2007.4281510",

language = "English",

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Richards, D 2007, Collaborative knowledge engineering: Socialising expert systems. in W Shen, Y Yang, J Yong, I Hawryszkiewycz, J Lin, J-P Barthes, ML Maher, Q Hao & MH Tran (eds), Proceedings of the 2007 11th International Conference on Computer Supported Cooperative Work in Design, CSCWD., 4281510, Institute of Electrical and Electronics Engineers (IEEE), Piscataway, NJ, pp. 635-640, 11th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2007, Melbourne, VIC, Australia, 24/04/07. https://doi.org/10.1109/CSCWD.2007.4281510

Collaborative knowledge engineering: Socialising expert systems. / Richards, Debbie.
Proceedings of the 2007 11th International Conference on Computer Supported Cooperative Work in Design, CSCWD. ed. / Weiming Shen; Yun Yang; Jianming Yong; Igor Hawryszkiewycz; Jongkai Lin; Jean-Paul Barthes; Mary Lou Maher; Qi Hao; Minh Hong Tran. Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2007. p. 635-640 4281510.

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

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T2 - 11th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2007

AU - Richards, Debbie

N1 - Copyright 2007 IEEE. Reprinted from Proceedings of the 11th international conference on computer supported cooperative work in design (2007). This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Macquarie Universityâ€™s products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.

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N2 - Social software such as Wikis and WebLogs have enabled global knowledge sharing communities to emerge. As with most of the current Web content, the knowledge captured is for human consumption only and too unstructured to allow automated reasoning. On the other hand, while sharing and reuse of knowledge are often the goal behind the development of a knowledge base or ontology, there is still currently little support even at the level of a version control system that notifies users when a module being checked-in has been edited by someone else. A collaborative learning approach is introduced1 which extends a combined rule and case based knowledge acquisition technique known as multiple classification ripple down rules to allow multiple users to view, define and refine a knowledge base over time and space.

AB - Social software such as Wikis and WebLogs have enabled global knowledge sharing communities to emerge. As with most of the current Web content, the knowledge captured is for human consumption only and too unstructured to allow automated reasoning. On the other hand, while sharing and reuse of knowledge are often the goal behind the development of a knowledge base or ontology, there is still currently little support even at the level of a version control system that notifies users when a module being checked-in has been edited by someone else. A collaborative learning approach is introduced1 which extends a combined rule and case based knowledge acquisition technique known as multiple classification ripple down rules to allow multiple users to view, define and refine a knowledge base over time and space.

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SN - 1424409632

SN - 9781424409631

SP - 635

EP - 640

BT - Proceedings of the 2007 11th International Conference on Computer Supported Cooperative Work in Design, CSCWD

A2 - Shen, Weiming

A2 - Yang, Yun

A2 - Yong, Jianming

A2 - Hawryszkiewycz, Igor

A2 - Lin, Jongkai

A2 - Barthes, Jean-Paul

A2 - Maher, Mary Lou

A2 - Hao, Qi

A2 - Tran, Minh Hong

PB - Institute of Electrical and Electronics Engineers (IEEE)

CY - Piscataway, NJ

Y2 - 24 April 2007 through 28 April 2007

ER -

Richards D. Collaborative knowledge engineering: Socialising expert systems. In Shen W, Yang Y, Yong J, Hawryszkiewycz I, Lin J, Barthes JP, Maher ML, Hao Q, Tran MH, editors, Proceedings of the 2007 11th International Conference on Computer Supported Cooperative Work in Design, CSCWD. Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE). 2007. p. 635-640. 4281510 doi: 10.1109/CSCWD.2007.4281510

Collaborative knowledge engineering: Socialising expert systems

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