Analysis of Context Based XML Data and Diversification for Keyword Search Queries

Abstract – Keyword query is an ordinary users to search vast amount of data, the ambiguity of keyword query makes it difficult to effectively answer keyword queries, especially for huge and vast keyword queries. To meet this challenging problem, our analysis propose an approach that automatically diversifies XML keyword search based on its different contexts in the XML data. Consider huge amount of keyword query and XML data to be searched, we first derive keyword search candidates of the query by a simple feature selection model. Second identify design an effective XML keyword search diversification model to measure the quality of each candidate. Next, baseline efficient algorithms are proposed to incrementally compute top-k qualified query candidates as the diversified search intentions. Compare selection criteria are targeted: the k selected query candidates are most relevant to the given query while they have to cover maximal number of distinct results on real and synthetic data sets demonstrates the effectiveness diversification model and the efficiency of algorithms.