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Expert Opinion Extraction from a Biomedical Database

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Date
2017
Link to item file
https://hal.inria.fr/hal-01584984
Dewey
Organisation des données
Sujet
Uncertain database; Data mining; Opinion
DOI
http://dx.doi.org/10.1007/978-3-319-61581-3_13
Conference name
Symbolic and Quantitative Approaches to Reasoning with Uncertainty 14th European Conference, ECSQARU 2017, Lugano, Switzerland, July 10–14, 2017, Proceedings
Conference date
2017
Conference city
Berlin Heidelberg
Conference country
SWITZERLAND
Book title
Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU)
Author
Antonucci, Alessandro; Cholvy, Laurence; Papini, Odile
Publisher
Springer
Publisher city
Berlin Heidelberg
ISBN
978-3-319-61580-6
Book URL
10.1007/978-3-319-61581-3
URI
https://basepub.dauphine.fr/handle/123456789/21015
Collections
  • LAMSADE : Publications
Metadata
Show full item record
Author
Samet, Ahmed
Guyet, Thomas
Negrevergne, Benjamin
Dao, Tien-Tuan
Nha Hoang, Tuan
Ho Ba Tho, Marie-Christine
Type
Communication / Conférence
Item number of pages
135-145
Abstract (EN)
In this paper, we tackle the problem of extracting frequent opinions from uncertain databases. We introduce the foundation of an opinion mining approach with the definition of pattern and support measure. The support measure is derived from the commitment definition. A new algorithm called OpMiner that extracts the set of frequent opinions modelled as a mass functions is detailed. Finally, we apply our approach on a real-world biomedical database that stores opinions of experts to evaluate the reliability level of biomedical data. Performance analysis showed a better quality patterns for our proposed model in comparison with literature-based methods.

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