Keyword search has received a lot of awareness in the database section as it is constructive approach for querying a database preferably no need of knowing its underlying schema. Though, keyword search queries frequently return multiple results. One merit outcome is to rank results such that the one best result will appear first. Still, this approach can suffer from redundancy and ambiguity problem. So there is need of automatic diversification of search results based on context or surroundings. There are two algorithms which are efficient, namely baseline and anchor pruning analysis results into top-k efficient search result collaborating feature selection & keyword diversification model. Efficiency measures are listed that evaluate effectiveness of search result.
Mrudul Arkadi : Department of Computer Engineering,
Smt Kashibai Navale College of Engineering, Maharashtra Pune, India
P. B. Mali : Department of Computer Engineering,
Smt Kashibai Navale College of Engineering, Maharashtra, Pune, India
XML
Mutual Information
Baseline Solution
Anchor
n-DCG
This Paper provides survey based on keyword search diversification, when ordinary user search for keyword queries it is difficult to find appropriate search solution, especially when search query is short and vague, it becomes ambivalent.
Model of feature selection extracts features i.e. candidate keywords or term-pair. Diversification model works on the basis of Bayes theorem collaborates correlation graph of correlation values of term-pair based on mutual information model and Baseline and anchor pruning solutions leads to top-k search result. Search query is nothing but to find user intension human involvement sometimes help but it is time-consuming so methods like baseline solution and anchor pruning solution automatically diversifies search while considering performance measures.
Context based classification makes user task easier as someone gets diversified result all related to someone’s search intensions. Baseline and anchor based pruning algorithms diversify XML data & effectively measured through relevance measure and relations between different query results
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