Abstract
We present an approach to text retrieval, incorporating data mining of a controlled i.e., vocabulary mining, in order to improve retrieval Performance. In gener al, formal queries presented to a retrieval System axe not optimized for retrieval efficiency or effectiveness. Vocabulary mining allows us to transform the query via Operations such as generalization or specialization. We offer a new framework for vocabulary mining, combining rough sets and fuzzy sets, allowing us to use rough set approximations when the documents and queries are described us-ing weighted, i.e., fuzzy, representations. We also explore generalized rough sets, variable precision models, and coordinating multiple vocabulary views. Finally, we present a preliminary analysis of the application of our proposed framework to a modern controlled vocabulary, the Unified Medical Language System. The proposed framework supports the systematic study and application of different vocabulary views within the textual Information retrieval model.
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Srinivasan, P., Kraft, D., Chen, J. (2000). Rough and Fuzzy Sets for Data Mining of a Controlled Vocabulary for Textual Retrieval. In: Crestani, F., Pasi, G. (eds) Soft Computing in Information Retrieval. Studies in Fuzziness and Soft Computing, vol 50. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1849-9_15
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DOI: https://doi.org/10.1007/978-3-7908-1849-9_15
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