Recently large amounts of schema data, which describe data structure of various domains such as purchase order, health, publication, geography, agriculture, environment and music, are available over the Web. Schema mapping aims to solve schema heterogeneity problem in schema data. This research thoroughly examines how string similarity metrics and text processing techniques impact on the performance of terminological schema mapping and high-lights their limitations. Our experimental study demonstrates that the performance of terminological schema matching is significantly improved by using text processing techniques. However, the performance improvement is slightly different between datasets because of the characteristics of the datasets, and in spite of applying all text processing techniques, some datasets still exhibit low performance. Our research supports the claim that a system which can manage the context dependent characteristics of terminological schema matching is es-sential for better schema mapping algorithms.

Item Type:	Refereed Conference Paper
Keywords:	schema mapping, terminological schema matching, string metrics
Research Division:	Information and Computing Sciences
Research Group:	Software engineering
Research Field:	Software engineering 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:	Anam, S (Mrs Sarawat Anam)
UTAS Author:	Kim, YS (Dr Yang Kim)
UTAS Author:	Kang, BH (Professor Byeong Kang)
ID Code:	98399
Year Published:	2014
Deposited By:	Information and Communication Technology
Deposited On:	2015-02-13
Last Modified:	2018-03-18
Downloads:	0