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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Dekun Tan Hui Sun Minjun Deng |
| Copyright Year | 2008 |
| Description | Author affiliation: Sch. of Civil Eng. & Archit., East China Jiaotong Univ., Nanchang (Minjun Deng) || Dept. of Comput. Sci.&Technol., Nanchang Inst. of Technol., Nanchang (Dekun Tan; Hui Sun) |
| Abstract | This paper proposes a new information retrieval method based on fuzzy rough sets theory. In the approach, firstly, the traditional Mutual Information function are used to compute the semantic association weight among document characteristic words, i.e., construct the thesaurus, the similar concept class of each characteristic word is mined based on it. Secondly, the fuzzy-rough set of document and query is built with thesaurus and similar concept class, its lower and upper approximation expand the expression of document and query, it implements conceptual expansion by association semantics. Finally, the semantic closeness between document and query is computed by the semantic distance on fuzzy-rough set, the ordinal results are returned to user according to the close degree value. The user can also adjust support threshold to get his satisfactory research objects through feedback information. This method can realize conceptual retrieval. At the end of the paper, an example is given for further illustration. |
| Starting Page | 576 |
| Ending Page | 581 |
| File Size | 227777 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781424425129 |
| DOI | 10.1109/GRC.2008.4664675 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2008-08-26 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | characteristic word Computational modeling information retrieval Set theory Information retrieval Thesauri mutual information Approximation methods Data mining Mutual information fuzzy rough set |
| Content Type | Text |
| Resource Type | Article |
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