Article

An integrated environment for knowledge acquisition

Authors:

Surya Ramachandran,

Yolanda GilAuthors Info & Claims

IUI '01: Proceedings of the 6th international conference on Intelligent user interfaces

Pages 13 - 20

https://doi.org/10.1145/359784.359819

Published: 01 January 2001 Publication History

Abstract

This paper describes an integrated acquisition interface that includes several techniques previously developed to support users in various ways as they add new knowledge to an intelligent system. As a result of this integration, the individual techniques can take better advantage of the context in which they are invoked and provide stronger guidance to users. We describe the current implementation using examples from a travel planning domain, and demonstrate how users can add complex knowledge to the system.

References

[1]

G. Abbrett and M. Burstein. The kreme knowledge editing environment. International Journal of Man-Machine Studies, 27(2):103-126, August 1987.

Digital Library

[2]

J. Blythe. Extending the role-limiting approach: Supporting end users to acquire problem-solving knowledge. In ECAI 2000 Workshop on Applications of Ontologies and Problem-Solving Methods, 2000.

[3]

J. Blythe and S. Ramachandran. Knowledge acquisition using and english-based method editor. In Proc. Twelfth Knowledge Acquisition for Knowledge-Based Systems Workshop, Banff, Alberta, 1999.

[4]

J. H. Boose and J. M. Bradshaw. Expertise transfer and complex problems: Using aquinas as a knowledge acquisitiong workbench for knowledge-based systems. International Journal of Man-Machine Studies, 26, 1987.

Digital Library

[5]

A. Cypher. Bringing programming to end users. In Watch What I Do: Programming by Demonstration. MIT Press, 1993.

Digital Library

[6]

A. Farquhar, R. Fikes, and J. Rice. The ontolingua server: A tool for collaborative ontology construction. In Proc. Tenth Knowledge Acquisition for Knowledge-Based Systems Workshop, Banff, Alberta, 1996.

[7]

B. R. Gaines and M. L. G. Shaw. Eliciting knowledge and transferring it effectively to a knowledge-based system. IEEE Transactions on Knowledge and Data Engineering, 5(1), 1993.

Digital Library

[8]

Y. Gil and E. Melz. Explicit representations of problem-solving strategies to support knowledge acquisition. In Proc. Thirteenth National Conference on Artificial Intelligence. AAAI Press, 1996.

Digital Library

[9]

P. D. Karp, V. K. Chaudhri, and S. M. Paley. A collaborative environment for authoring large knowledge bases. Journal of Intelligent Information Systems, 13:155-194, 1999.

Digital Library

[10]

J. Kim and Y. Gil. Deriving expectations to guide knowledge-base creation. In Proc. Sixteenth National Conference on Artificial Intelligence, pages 235-241. AAAI Press, 1999.

Digital Library

[11]

J. Kim and Y. Gil. Acquiring problem-solving knowledge from end users: Putting interdependency models to the test. In Proc. Seventeenth National Conference on Artificial Intelligence. AAAI Press, 2000.

Digital Library

[12]

G. Linden, S. Hanks, and N. Lesh. Interactive assessment of user preference models: The automated travel assistant. Sixth International Conference on User Modelling, 1997.

[13]

D. L. McGuinness, R. Fikes, J. Rice, and S. Wilder. The chimaera ontology environment. In Proc. Seventeenth National Conference on Artificial Intelligence. AAAI Press, 2000.

Digital Library

[14]

K. Myers. Strategic advice for hierarchical planners. In Proceedings of the International Conference on Knowledge Representation, 1996.

[15]

A. R. Puerta, J. W. Egar, S. Tu, and M. A. Musen. A multiple-method knowledge acquisition shell for the automatic generation of knowledge acquisition tools. Knowledge Acquisition, 4(2):171-196, 1992.

Digital Library

[16]

B. Swartout, R. Patil, K. Knight, and T. Russ. Towards distributed use of large-scale ontologies. In Proc. Tenth Knowledge Acquisition for Knowledge-Based Systems Workshop, Banff, Alberta, 1996.

[17]

W. R. Swartout and Y. Gil. Expect: Explicit representations for flexible acquisition. In Proc. Ninth Knowledge Acquisition for Knowledge-Based Systems Workshop, Banff, Alberta, 1995.

[18]

M. Tallis and Y. Gil. Designing scripts to guide users in modifying knowledge-based systems. In Proc. Sixteenth National Conference on Artificial Intelligence. AAAI Press, 1999.

Digital Library

[19]

L. G. Terveen and D. A. Wroblewski. a collaborative interface for browsing and editing large knowledge bases. In Proc. Eighth National Conference on Artificial Intelligence. AAAI Press, 1990.

Cited By

Yanchinda JSinthamrongruk TDahal K(2017)STEM learning concept with fuzzy inference for organic rice farming knowledge acquisition2017 11th International Conference on Software, Knowledge, Information Management and Applications (SKIMA)10.1109/SKIMA.2017.8294113(1-8)Online publication date: Dec-2017
https://doi.org/10.1109/SKIMA.2017.8294113
Lorenzetti CMaguitman ALeake DMenczer FReichherzer T(2016)Mining for Topics to Suggest Knowledge Model ExtensionsACM Transactions on Knowledge Discovery from Data10.1145/299765711:2(1-30)Online publication date: 3-Dec-2016
https://dl.acm.org/doi/10.1145/2997657
Han SYoon HKang BPark S(2014)Using MCRDR based Agile approach for expert system developmentComputing10.1007/s00607-013-0336-y96:9(897-908)Online publication date: 1-Sep-2014
https://dl.acm.org/doi/10.1007/s00607-013-0336-y
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Index Terms

An integrated environment for knowledge acquisition
1. Computing methodologies
  1. Machine learning
    1. Learning settings
2. Human-centered computing
  1. Human computer interaction (HCI)

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Published In

cover image ACM Conferences

IUI '01: Proceedings of the 6th international conference on Intelligent user interfaces

January 2001

174 pages

ISBN:1581133251

DOI:10.1145/359784

Chairmen:
Candy Sidner
MERL
,
Johanna Moore
Univ. of Edinburgh, Edinburgh, Scotland

Copyright © 2001 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGAI: ACM Special Interest Group on Artificial Intelligence
SIGCHI: ACM Special Interest Group on Computer-Human Interaction
AAAI: Am Assoc for Artifical Intelligence

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 January 2001

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IUI01

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IUI01: International Conference on Intelligent User Interfaces 2001

January 14 - 17, 2001

New Mexico, Santa Fe, USA

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Overall Acceptance Rate 746 of 2,811 submissions, 27%

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March 24 - 27, 2025

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Cited By

Yanchinda JSinthamrongruk TDahal K(2017)STEM learning concept with fuzzy inference for organic rice farming knowledge acquisition2017 11th International Conference on Software, Knowledge, Information Management and Applications (SKIMA)10.1109/SKIMA.2017.8294113(1-8)Online publication date: Dec-2017
https://doi.org/10.1109/SKIMA.2017.8294113
Lorenzetti CMaguitman ALeake DMenczer FReichherzer T(2016)Mining for Topics to Suggest Knowledge Model ExtensionsACM Transactions on Knowledge Discovery from Data10.1145/299765711:2(1-30)Online publication date: 3-Dec-2016
https://dl.acm.org/doi/10.1145/2997657
Han SYoon HKang BPark S(2014)Using MCRDR based Agile approach for expert system developmentComputing10.1007/s00607-013-0336-y96:9(897-908)Online publication date: 1-Sep-2014
https://dl.acm.org/doi/10.1007/s00607-013-0336-y
Yoon HHan SKang BPark S(2012)V&V to Use Agile Approach in ES Development: Why RDR Works for Expert System Developments!Computer Applications for Software Engineering, Disaster Recovery, and Business Continuity10.1007/978-3-642-35267-6_15(113-120)Online publication date: 2012
https://doi.org/10.1007/978-3-642-35267-6_15
Zhuo HYang QPan RLi L(2011)Cross-domain action-model acquisition for planning via web searchProceedings of the Twenty-First International Conference on International Conference on Automated Planning and Scheduling10.5555/3038485.3038524(298-305)Online publication date: 11-Jun-2011
https://dl.acm.org/doi/10.5555/3038485.3038524
Gil Y(2011)Review: interactive knowledge capture in the new millenniumThe Knowledge Engineering Review10.1017/S026988891000040826:1(45-51)Online publication date: 1-Feb-2011
https://dl.acm.org/doi/10.1017/S0269888910000408
Panda SMishra D(2010)Development of a tribological failure knowledge modelInternational Journal of Knowledge Engineering and Data Mining10.1504/IJKEDM.2010.0348391:2(99-114)Online publication date: 1-Aug-2010
https://dl.acm.org/doi/10.1504/IJKEDM.2010.034839
Tallis MKim JGil Y(2010)User studies of knowledge acquisition tools: methodology and lessons learnedJournal of Experimental & Theoretical Artificial Intelligence10.1080/0952813011010026113:4(359-378)Online publication date: 9-Nov-2010
https://doi.org/10.1080/09528130110100261
Zhuo HYang QHu DLi L(2010)Learning complex action models with quantifiers and logical implicationsArtificial Intelligence10.1016/j.artint.2010.09.007174:18(1540-1569)Online publication date: 1-Dec-2010
https://dl.acm.org/doi/10.1016/j.artint.2010.09.007
(2010)ReferencesNo Code Required10.1016/B978-0-12-381541-5.00038-9(453-472)Online publication date: 2010
https://doi.org/10.1016/B978-0-12-381541-5.00038-9
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