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Context-based diversification for keyword queries over XML data

journal contribution
posted on 2015-03-01, 00:00 authored by Jianxin Li, Chengfei Liu, Jeffrey Xu Yu
While keyword query empowers ordinary users to search vast amount of data, the ambiguity of keyword query makes it difficult to effectively answer keyword queries, especially for short and vague keyword queries. To address this challenging problem, in this paper we propose an approach that automatically diversifies XML keyword search based on its different contexts in the XML data. Given a short and vague keyword query and XML data to be searched, we first derive keyword search candidates of the query by a simple feature selection model. And then, we design an effective XML keyword search diversification model to measure the quality of each candidate. After that, two efficient algorithms are proposed to incrementally compute top-k qualified query candidates as the diversified search intentions. Two 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. At last, a comprehensive evaluation on real and synthetic data sets demonstrates the effectiveness of our proposed diversification model and the efficiency of our algorithms.

History

Journal

IEEE transactions on knowledge and data engineering

Volume

27

Pagination

660-672

Location

Piscataway, N.J.

ISSN

1041-4347

Language

eng

Publication classification

C1.1 Refereed article in a scholarly journal

Copyright notice

2014, IEEE

Issue

3

Publisher

Institute of Electrical and Electronics Engineers

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