Original scientific paper

https://doi.org/10.17559/TV-20150314113111

Interpreting XML keyword query using hidden Markov model

Xiping Liu ; School of Information Technology, Jiangxi University of Finance and Economics, No. 169, East Shuanggang Road, Nanchang 330013, Jiangxi, P. R. China
Changxuan Wan ; School of Information Technology, Jiangxi University of Finance and Economics, No. 169, East Shuanggang Road, Nanchang 330013, Jiangxi, P. R. China
Dexi Liu ; School of Information Technology, Jiangxi University of Finance and Economics, No. 169, East Shuanggang Road, Nanchang 330013, Jiangxi, P. R. China

Abstract

Keyword search on XML database has attracted a lot of research interests. As XML documents are very different from flat documents, effective search of XML documents needs special considerations. Traditional bag-of-words model does not take the roles of keywords and the relationship between keywords into consideration, and thus is not suited for XML keyword search. In this paper, we present a novel model, called semi-structured keyword query (SSQ), which understands a keyword query in a different way: a keyword query is composed of several query units, where each unit represents query condition. To interpret a keyword query under this model, we take two steps. First, we propose a probabilistic approach based on a Hidden Markov Model for computing the best mapping of the query keywords into the database terms, i.e., elements, attributes and values. Second, we generate SSQs based on the mapping. Experimental results verify the effectiveness of our methods.

Keywords

hidden Markov model (HMM); semi-structured keyword query (SSQ); XML keyword query

Hrčak ID:

169356

URI

https://hrcak.srce.hr/169356

Publication date:

29.11.2016.

Article data in other languages: croatian

Visits: 1.602 *