INTENT-BASED DIVERSIFICATION FOR FUZZY KEYWORD SEARCH OVER XML DATA
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Abstract
The keyword queries overvarious data forms have wide attention nowadays. The query intention can easily be obtainedby comparing the keyword with some query suggestions. Anannotation process can be recommended to generate the structured meta-data for a document. In the proposed system the conceptualization and measure of co-occurrence count of a typed term has considered on the basis of semantic relatedness and similarity between terms. This shouldbe useful for the retrieval of informationby search query with short and vague keywords. Apart from this, using a fuzzy uncertaintyfunction the fuzzy semantic of a query can be easily obtained. This will reveal the uncertainties among the co-occurring terms. The closest matching terms can be easily constructedby using the keyword similarity semantics. The edit distance and gram based pattern matching methods are used to check thecloseness of keyword similarityof terms. Theconcept based clustering isused to hold the data in a multidimensional space. The proposed dimensionreduction method reduces the cardinality of the result set in the direction of Eigen vectors calculated for the selected features. The outliers of the concept vector can set to the required level on the basis of concept density. The attributes and related features arestored as metadata information in XMLfiles for more precise representation.
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