Abstract
There are three main problems when designing an information retrieval (IR) system, namely, uncertainty in the representation of documents and queries, computational complexity, and the diversity of users. An IR system may be designed to be adaptive by allowing the modification of document and query representation. As well, different retrieval methods can be used for different users. The combina-tion of multi-representation of documents and multi-strategy retrieval may provide a Solution for the diversity of users. A widely used Solution for reducing computational costs is cluster-based retrieval. However, the use of document clustering only reduces the dimensionality of documents. The same number of terms is used for the representation of the Clusters. One may reduce the dimensionality of terms by constructing a term hierarchy in parallel to the construction of a document hierarchy. The proposed framework of granular IR enables us to incorporate multi-representation of documents and multi-strategy retrieval. Hence, granular IR may provide a method for developing knowledge based intelligent IR Systems.
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Wong, S.K.M., Yao, Y.Y., Butz, C.J. (2000). Granular Information 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_13
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DOI: https://doi.org/10.1007/978-3-7908-1849-9_13
Publisher Name: Physica, Heidelberg
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