Research in relational keyword search has been focused on the efficient computation of results from multiple tuples as well as strategies to rank and output the most relevant ones. However, the challenge to retrieve the intended meaningful results remains. Existing relational keyword search techniques suffer from the problem of returning many incomplete results. In this work, we adopt a semantic approach to relational keyword search via an Object-Relationship-Mixed graph (ORMgraph). This graph is constructed based on database schema constraints to capture the semantics of objects and relationships in the data. Each node in the ORM-graph represents either an object, or a relationship, or both. We design an algorithm that utilizes the ORM-graph to process keyword queries. Experiment results show our approach returns more complete and meaningful results compared to existing methods, and is efficient.

Item Type:	Refereed Conference Paper
Keywords:	social network data management
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:	90495
Year Published:	2013
Deposited By:	Information and Communication Technology
Deposited On:	2014-04-08
Last Modified:	2014-08-29
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