skip to main content
10.1145/1516241.1516302acmconferencesArticle/Chapter ViewAbstractPublication PagesicuimcConference Proceedingsconference-collections
research-article

Merging element fuzzy cognitive maps

Published: 15 February 2009 Publication History

Abstract

Importance degree and difference degree of keywords in different topics have been computed by the weights in Element Fuzzy Cognitive Maps (E-FCMs). Logic "and" operation is introduced to roughly evaluate the similarities between mass E-FCMs in order to form similar communities of E-FCMs. Based on the weights computing and the logic "and" operation, an E-FCMs-based knowledge merging algorithm is proposed to inspect the noisy and the redundancy information hidden in the original E-FCMs belonging to one similar community. Shannon entropy is employed as an indicator to measure the loss of textual information during the merging process of E-FCMs. The merging algorithm and the indicator provide a concise representation of text knowledge that can be used in understanding-based text automatic classification and clustering, as well as relevant knowledge aggregation and integration. The proposed algorithm has very good application prospects in the fields of e-Science knowledge gird and e-Learning.

References

[1]
G. Salton, M. J. McGill, Introduction to Modern Information Retrieval. McGraw-Hill Book Company, New York. 1983. 120--123.
[2]
T. Hofmann (1999). Probabilistic latent semantic indexing. in Proceedings of the 22nd International Conference on Research and Development in Information Retrieval (SIGIR"99), 50--57.
[3]
D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent Dirichlet Allocation. Journal of Machine Learning Research, 2003, 3: 993--1022.
[4]
R. Z. Michal, G. Thomas. The Author-Topic Model for Authors and Documents. http://www.datalab.uci.edu/author-topic/398.pdf
[5]
A. McCallum, A. C. Emmanuel, et.al. The author-recipient-topic model for topic and role discovery in social networks: experiments with Enron and Academic email. http://www.cs.umass. edu/~mccallum/papers/art04tr.pdf.
[6]
D. M. Blei, J. D. Lafferty. Correlated Topic Models. http://www.cs.princeton.edu/~blei/ papers/ BleiLafferty 2006.pdf.
[7]
D. Cohn, T. Hofmann. The missing link: A probabilistic model of document content and hypertext connectivity. Neural Information Processing Systems, 2001, 13, 430--436.
[8]
R. Z. Michal, G. Thomas. The Author-Topic Model for Authors and Documents. http://www.datalab.uci.edu/author-topic/398.pdf
[9]
H. Zhuge, X. F. Luo. Automatic generation of document semantics for the e-Science knowledge gird Knowledge Grid. Journal of Systems and Software, 2006, 79, 969--983.
[10]
X. F. Luo, N. Fang, et. al. Semantic Representation of Scientific Documents for the e-Science knowledge gird Knowledge Grid. International Journal of Concurrency and Computation: Practice and Experience, 2008, 20(7): 839--862.
[11]
H. Anthony, S. Rupert. A knowledge-based approach to merging information Knowledge-Based Systems, Volume 19, Issue 8, December 2006, 647--674.
[12]
H. Anthony, Merging structured text using temporal knowledge, Data & Knowledge Engineering, Volume 41, Issue 1, April 2002, 29--66.
[13]
A. Leila and K. Souhila. An argumentation framework for merging conflicting knowledge bases. International Journal of Approximate Reasoning, Volume 45, Issue 2, July 2007, 321--340.
[14]
W. Z. Christopher, P. R. Loren and R. R. Terry. Automated merging of conflicting knowledge bases, using a consistent, majority-rule approach with knowledge-form maintenance. Computers & Operations Research, Volume 32, Issue 7, July 2005, 1809--1829.
[15]
S. Benferhat, D. Dubois, S. Kaci, H. Prade, Possibilistic merging and distance-based fusion of propositional information, Annals of Mathematics and Artificial Intelligence 34 (1--3) (2002) 217--252.
[16]
S. Benferhat, D. Dubois, H. Prade, M. Williams, A practical approach to fusing and revising prioritized belief bases, in: Proceedings of the 9th Portuguese Conference on Artificial Intelligence (EPIA'99), 1999, 222--236.
[17]
J. P. Delgrande and S. Torsten. A consistency-based framework for merging knowledge bases. Journal of Applied Logic, 5(3), September 2007, 459--477
[18]
B. Loreto, E. Leopoldo. Bertossi, Logic programs for consistently querying data integration systems, in: Proceedings of the 18th International Joint Conference on Artificial Intelligence (IJCAI'03), 2003, 10--15.
[19]
L. Cholvy, Reasoning about merging information, Handbook of Defeasible Reasoning and Uncertainty Management Systems, vol. 3, 1998, 233--263.
[20]
A. Levy, Logic-based techniques in data integration, in: Jack Minker (Ed.), Logic Based Artificial Intelligence, Kluwer, 2000.
[21]
J. Lin, Integration of weighted knowledge bases, Artificial Intelligence 83 (1996) 363--378.
[22]
P. Z. Revesz, On the semantics of theory change: arbitration between old and new information, in: 12th ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Databases, 1993, pp. 71--92.
[23]
A. Poulovassilis, P. McBrien, A general formal framework for schema transformation, Data & Knowledge Engineering 28 (1998) 47--71.
[24]
D. Calvanese, G. De Giacomo, M. Lenzerini, D. Nardi, R. Rosati, Description logic framework for information integration, in: Proceedings of the 6th Conference on the Principles of Knowledge Representation and Reasoning (KR'98), Morgan Kaufmann, Los Altos, CA, 1998. 2--13.
[25]
N. Rescher, R. Manor, On inference from inconsistent premises, Theory and Decision 1 (1970) 179--219.
[26]
B. Chaib-Draa. Causal Maps: Theory, Implementation and Practical Applications in Multi-agent Environments. IEEE Trans on Knowledge and Data Engineering, 2002, 14(6): 1--17.
[27]
P. C. Silva. New Forms of Combinated Matrices in Fuzzy Cognitive Maps. In: Proc of the IEEE International Conference on Neural Network, New York, 1995, 771--776.
[28]
K. Perusich; M. D. Mcneese, Using Fuzzy Cognitive Maps for Knowledge Management in a Conflict Environment. IEEE Transactions on Systems, Man and Cybernetics, Part C: Applications and Reviews, 36(6), Nov. 2006, 810--821.
[29]
D. Kardaras, Karakostas, B., E-service adaptation using fuzzy cognitive maps, The 3rd International IEEE Conference on Intelligent Systems, Sept. 2006, 227--230.
[30]
A. J, Jetter, Fuzzy Cognitive Maps for Engineering and Technology Management: What Works in Practice?, Technology Management for the Global Future. PICMET 2006, 2, July 2006, 498--512.
[31]
S. K. Golmohammadi, A. Azadeh, et. al, Action Selection in Robots Based on Learning Fuzzy Cognitive Map, IEEE International Conference on Industrial Informatics, Aug. 2006, 731--736.

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
ICUIMC '09: Proceedings of the 3rd International Conference on Ubiquitous Information Management and Communication
February 2009
704 pages
ISBN:9781605584058
DOI:10.1145/1516241
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: 15 February 2009

Permissions

Request permissions for this article.

Check for updates

Qualifiers

  • Research-article

Funding Sources

Conference

ICUIMC '09
Sponsor:

Acceptance Rates

Overall Acceptance Rate 251 of 941 submissions, 27%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 180
    Total Downloads
  • Downloads (Last 12 months)3
  • Downloads (Last 6 weeks)0
Reflects downloads up to 13 Feb 2025

Other Metrics

Citations

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media