eCite Digital Repository

ExpressQ: identifying keyword context and search target in relational keyword queries


Zeng, Z and Bao, Z and Le, TN and Lee, ML and Ling, TW, ExpressQ: identifying keyword context and search target in relational keyword queries, Proceedings of the ACM International Conference on Information and Knowledge Management 2014, 3-7 November 2014, Shanghai, China, pp. 31-40. ISBN 978-1-4503-2598-1 (2014) [Refereed Conference Paper]

Copyright Statement

Copyright 2014 ACM New York

DOI: doi:10.1145/2661829.2661870


Keyword search in relational databases has gained popularity due to its ease of use. However, the challenge to return query answers that satisfy usersí information need remains. Traditional keyword queries have limited expressive capability and are ambiguous. In this work, we extend keyword queries to enhance their expressive power and describe an semantic approach to process these queries. Our approach considers keywords that match meta-data such as the names of relations and attributes, and utilizes them to provide the context of subsequent keywords in the query. Based on the ORM schema graph which captures the semantics of objects and relationships in the database, we determine the objects and relationships referred to by the keywords in order to infer the search target of the query. Then, we construct a set of minimal connected graphs called query patterns, to represent userís possible search intentions. Finally, we translate the top-k ranked query patterns into SQL statements in order to retrieve information that the user is interested in. We develop a system prototype called ExpressQ to process the extended keyword queries. Experimental results show that our system is able to generate SQL statements that retrieve user intended information effectively.

Item Details

Item Type:Refereed Conference Paper
Keywords:keyword query, relational database, semantic approach, relational data usability, keyword search
Research Division:Information and Computing Sciences
Research Group:Data management and data science
Research Field:Data management and data science not elsewhere classified
Objective Division:Information and Communication Services
Objective Group:Information systems, technologies and services
Objective Field:Information systems, technologies and services not elsewhere classified
UTAS Author:Bao, Z (Dr Zhifeng Bao)
ID Code:93781
Year Published:2014
Deposited By:Information and Communication Technology
Deposited On:2014-08-16
Last Modified:2015-04-27

Repository Staff Only: item control page