Combination of Qualitative Information with 2-Tuple Linguistic Representation in DSmT

Modern systems for information retrieval, fusion and management need to deal more and more with information coming from human experts usually expressed qualitatively in natural language with linguistic labels. In this paper, we propose and use two new 2-Tuple linguistic representation models (i.e.,...

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Bibliographic Details
Published in:Journal of computer science and technology 2009-07, Vol.24 (4), p.786-797
Main Authors: Li, Xin-De, Smarandache, Florentin, Dezert, Jean, Dai, Xian-Zhong
Format: Article
Language:English
Subjects:
Artificial Intelligence
Computer Science
Data Structures and Information Theory
Distribution functions
Human
Information retrieval
Information Systems Applications (incl.Internet)
Labels
Linguistics
Mathematical analysis
Mathematical models
Natural language (computers)
Number theory
Preserves
Regular Paper
Representations
Software Engineering
Studies
Theory of Computation
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Summary:Modern systems for information retrieval, fusion and management need to deal more and more with information coming from human experts usually expressed qualitatively in natural language with linguistic labels. In this paper, we propose and use two new 2-Tuple linguistic representation models (i.e., a distribution function model (DFM) and an improved Herrera-Martínez’s model) jointly with the fusion rules developed in Dezert-Smarandache Theory (DSmT), in order to combine efficiently qualitative information expressed in term of qualitative belief functions. The two models both preserve the precision and improve the efficiency of the fusion of linguistic information expressing the global expert’s opinion. However, DFM is more general and efficient than the latter, especially for unbalanced linguistic labels. Some simple examples are also provided to show how the 2-Tuple qualitative fusion rules are performed and their advantages.
ISSN:1000-9000
1860-4749
DOI:10.1007/s11390-009-9258-8