More Web Proxy on the site http://driver.im/

Article

Dynamic extraction topic descriptors and discriminators: towards automatic context-based topic search

Authors:

Thomas Reichherzer,

Filippo MenczerAuthors Info & Claims

CIKM '04: Proceedings of the thirteenth ACM international conference on Information and knowledge management

Pages 463 - 472

https://doi.org/10.1145/1031171.1031260

Published: 13 November 2004 Publication History

Abstract

Effective knowledge management may require going beyond initial knowledge capture, to support decisions about how to extend previously-captured knowledge. Electronic <i>concept maps,</i> interlinked with other concept maps and multimedia resources, can provide rich <i>knowledge models</i> for human knowledge capture and sharing. This paper presents research on methods for supporting experts as they extend these knowledge models, by searching the Web for new context-relevant topics as candidates for inclusion. This topic search problem presents two challenges: First, how to formulate queries to seek topics that reflect the context of the current knowledge model, and, second, how to identify candidate topics with the right balance of novelty and relevance. More generally, this problem raises the broad question of the interaction of topic information from the local analysis space (a collected set of documents) and the global search space (the Web). The paper develops a framework for understanding this interaction, and proposes and evaluates techniques for addressing the query formation and topic identification questions by dynamically extracting topic descriptors and discriminators from a knowledge model, to characterize information needs for retrieval and filtering of relevant material. Using these techniques, we have developed a support tool that starts from a knowledge model under construction and automatically produces a set of suggestions for topics to include, proactively supporting users as they extend knowledge models.

References

[1]

Armstrong, R., Freitag, D., Joachims, T., and Mitchell, T. (1995). Webwatcher: A learning apprentice for the World Wide Web. InAAAI Spring Symposium on Information Gathering, pages 6--12.

[2]

Belkin, N. J. (2000). Helping people find what they don't know. Commun. ACM, 43(8):58--61.

Digital Library

[3]

Berge, C. (1973). Graphs and Hypergraphs. North Holland.

Digital Library

[4]

Billerbeck, B., Scholer, F., Williams, H. E., and Zobel, J. (2003). Query expansion using associated queries. In CIKM, pages 2--9.

Digital Library

[5]

Budzik, J. and Hammond, K. (1999). Watson: Anticipating and contextualizing information needs. In 62nd Annual Meeting of the American Society for Information Science.

[6]

Cañas, A., Ford, K., Brennan, J., Reichherzer, T., and Hayes, P. (1995). Knowledge construction and sharing in Quorum. InAIED, pages 218--225.

[7]

Cañas, A., Leake, D., and Maguitman, A. (2001). Combining concept mapping with CBR: Experience-based support for knowledge modeling. InFLAIRS, pages 286--290.

Digital Library

[8]

Cañas, A. J., Coffey, J., Reichherzer, T., Hill, G., Suri, N., Carff, R., Mitrovich, T., and Eberle, D. (1998). El-tech: A performance support system with embedded training for electronics technicians. In FLAIRS, pages 79--83.

Digital Library

[9]

Carvalho, M., Hewett, R., and Cañas, A. (2001). Enhancing Web searches from concept map-based knowledge models. In Proceedings of the SCI Conference.

[10]

Chakrabarti, S., van den Berg, M., and Dom, B. (1999). Focused crawling: a new approach to topic-specific Web resource discovery. Computer Networks, 31(11--16):1623--1640.

Digital Library

[11]

Deerwester, S. C., Dumais, S. T., Landauer, T. K., Furnas, G. W., and Harshman, R. A. (1990). Indexing by latent semantic analysis. Journal of the American Society of Information Science, 41(6):391--407.

[12]

Ford, K. M., Coffey, J. W., Cañas, A. J., Andrews, E. J., and Turner, C. W. (1996). Diagnosis and explanation by a nuclear cardiology expert system. International Journal of Expert Systems, 9:499--506.

[13]

Hoffman, R. R., Coffey, J. W., Ford, K. M., and Carnot, M. J. (2001). Storm-LK: A human-centered knowledge model for weather forecasting. In Proceedings of the 45th Annual Meeting of the Human Factors and Ergonomics Society.

[14]

Jones, S. K. (1972). A statistical interpretation of term specificity and its application in retrieval. Journal of Documentation, 28:11--21.

[15]

Kobayashi, M. and Takeda, K. (2000). Information retrieval on the Web. ACM Comput. Surv., 32(2):144--173.

Digital Library

[16]

Leake, D., Maguitman, A., and Reichherzer, T. (2003a). Topic extraction and extension to support concept mapping. In FLAIRS, pages 325--329.

[17]

Leake, D., Maguitman, A., and Reichherzer, T. (2004). Understanding knowledge models: Modeling assessment of concept importance in concept maps. In CogSci2004, pages 785--800.

[18]

Leake, D., Maguitman, A., Reichherzer, T., Cañas, A., Carvalho, M., Arguedas, M., Brenes, S., and Eskridge, T. (2003b). Aiding knowledge capture by searching for extensions of knowledge models. In KCAP, pages 44--53.

Digital Library

[19]

Lieberman, H. (1995). Letizia: An agent that assists Web browsing. In IJCAI, pages 924--929.

Digital Library

[20]

Menczer, F., Pant, G., and Srinivasan, P. (2004). Topic-driven crawlers: Machine learning issues. ACM TOIT (To appear).

[21]

Novak, J. and Gowin, D. B. (1984). Learning How to Learn. Cambridge University Press.

[22]

Rhodes, B. and Starner, T. (1996). The remembrance agent: A continuously running automated information retrieval system. In PAAM, pages 487--495.

[23]

Rocchio, J. J. (1971). Relevance feedback in information retrieval. In Salton, G., editor,The SMART retrieval system - experiments in automatic document processing, pages 313--323. Prentice-Hall.

[24]

Salton, G. and Buckley, C. (1988). Term weighting approaches in automatic text retrieval. Information Processing and Management, 24(5):513--523.

Digital Library

[25]

Salton, G. and Yang, C. (1973). On the specification of term values in automatic indexing. Journal of Documentation, 29:351--372.

[26]

Scholer, F. and Williams, H. E. (2002). Query association for effective retrieval. In CIKM, pages 324--331.

Digital Library

Cited By

Chesnevar CMaguitman AGonzález MEstevez E(2016)Opinion Aggregation and Conflict Resolution in E-Government PlatformsInterdisciplinary Perspectives on Contemporary Conflict Resolution10.4018/978-1-5225-0245-6.ch011(183-203)Online publication date: 2016
https://doi.org/10.4018/978-1-5225-0245-6.ch011
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
Maguitman ALorenzetti CCecchini RBrena RGuzman-Arenas A(2012)Evaluating and Enhancing Contextual Search with Semantic Similarity DataQuantitative Semantics and Soft Computing Methods for the Web10.4018/978-1-60960-881-1.ch008(163-182)Online publication date: 2012
https://doi.org/10.4018/978-1-60960-881-1.ch008
Show More Cited By

Index Terms

Dynamic extraction topic descriptors and discriminators: towards automatic context-based topic search
1. Information systems
  1. Information retrieval
    1. Information retrieval query processing

Recommendations

Suggesting novel but related topics: towards context-based support for knowledge model extension
IUI '05: Proceedings of the 10th international conference on Intelligent user interfaces

Much intelligent user interfaces research addresses the problem of providing information relevant to a current user topic. However, little work addresses the complementary question of helping the user identify potential topics to explore next. In ...
Cross-lingual latent topic extraction
ACL '10: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics

Probabilistic latent topic models have recently enjoyed much success in extracting and analyzing latent topics in text in an unsupervised way. One common deficiency of existing topic models, though, is that they would not work well for extracting cross-...
A Tutorial on Probabilistic Topic Models for Text Data Retrieval and Analysis
SIGIR '18: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval

As text data continues to grow quickly, it is increasingly important to develop intelligent systems to help people manage and make use of vast amounts of text data ("big text data''). As a new family of effective general approaches to text data ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

CIKM '04: Proceedings of the thirteenth ACM international conference on Information and knowledge management

November 2004

678 pages

ISBN:1581138741

DOI:10.1145/1031171

General Chair:
David Grossman
Illinois Institute of Technology
,
Program Chairs:
Luis Gravano
Columbia University
,
ChengXiang Zhai
University of Illinois at Urbana-Champaign
,
Otthein Herzog
University of Bremen, Germany
,
David A. Evans
Clairvoyance Corporation

Copyright © 2004 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

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 November 2004

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Article

Conference

CIKM04

Sponsor:

CIKM04: Conference on Information and Knowledge Management

November 8 - 13, 2004

D.C., Washington, USA

Acceptance Rates

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

Upcoming Conference

CIKM '25

Sponsor:
sigir
sigir

The 34th ACM International Conference on Information and Knowledge Management

November 10 - 14, 2025

Seoul , Republic of Korea

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

15
Total Citations
View Citations
955
Total Downloads

Downloads (Last 12 months)1
Downloads (Last 6 weeks)0

Reflects downloads up to 10 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Chesnevar CMaguitman AGonzález MEstevez E(2016)Opinion Aggregation and Conflict Resolution in E-Government PlatformsInterdisciplinary Perspectives on Contemporary Conflict Resolution10.4018/978-1-5225-0245-6.ch011(183-203)Online publication date: 2016
https://doi.org/10.4018/978-1-5225-0245-6.ch011
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
Maguitman ALorenzetti CCecchini RBrena RGuzman-Arenas A(2012)Evaluating and Enhancing Contextual Search with Semantic Similarity DataQuantitative Semantics and Soft Computing Methods for the Web10.4018/978-1-60960-881-1.ch008(163-182)Online publication date: 2012
https://doi.org/10.4018/978-1-60960-881-1.ch008
Chesñevar CMaguitman AEstevez EBrena RFerriero DPardo TQian H(2012)Integrating argumentation technologies and context-based search for intelligent processing of citizens' opinion in social mediaProceedings of the 6th International Conference on Theory and Practice of Electronic Governance10.1145/2463728.2463762(166-170)Online publication date: 22-Oct-2012
https://dl.acm.org/doi/10.1145/2463728.2463762
Cecchini RLorenzetti CMaguitman ABrignole N(2010)Multiobjective evolutionary algorithms for context‐based searchJournal of the American Society for Information Science and Technology10.1002/asi.2132061:6(1258-1274)Online publication date: 11-May-2010
https://doi.org/10.1002/asi.21320
Brena RMaguitman A(2009)Mining the Internet for ConceptsEncyclopedia of Data Warehousing and Mining, Second Edition10.4018/978-1-60566-010-3.ch203(1310-1315)Online publication date: 2009
https://doi.org/10.4018/978-1-60566-010-3.ch203
Lorenzetti CMaguitman A(2009)A semi-supervised incremental algorithm to automatically formulate topical queriesInformation Sciences: an International Journal10.1016/j.ins.2009.01.029179:12(1881-1892)Online publication date: 1-May-2009
https://dl.acm.org/doi/10.1016/j.ins.2009.01.029
Herrmann APaech B(2009)Practical challenges of requirements prioritization based on risk estimationEmpirical Software Engineering10.1007/s10664-009-9105-014:6(644-684)Online publication date: 1-Dec-2009
https://dl.acm.org/doi/10.1007/s10664-009-9105-0
Zoric GSmid KPandzic I(2009)Towards Facial Gestures Generation by Speech Signal Analysis Using HUGE ArchitectureMultimodal Signals: Cognitive and Algorithmic Issues10.1007/978-3-642-00525-1_11(112-120)Online publication date: 17-Feb-2009
https://dl.acm.org/doi/10.1007/978-3-642-00525-1_11
Ngonga Ngomo ASchumacher F(2009)BorderFlowProceedings of the 10th International Conference on Computational Linguistics and Intelligent Text Processing10.1007/978-3-642-00382-0_44(547-558)Online publication date: 17-Feb-2009
https://dl.acm.org/doi/10.1007/978-3-642-00382-0_44
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents