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.
History
Publication title
Lecture Notes in Artificial Intelligence 8862: Proceedings of the 13th Pacific Rim International Conference on Artificial Intelligence (PRICAI-2014)
Volume
8862
Editors
D-N Pham, S-B Park
Pagination
561-572
ISSN
0302-9743
Department/School
School of Information and Communication Technology
Publisher
Springer International Publishing
Place of publication
Switzerland
Event title
13th Pacific Rim International Conference on Artificial Intelligence (PRICAI-2014)
Event Venue
Gold Coast, Australia
Date of Event (Start Date)
2014-12-01
Date of Event (End Date)
2014-12-05
Rights statement
Copyright 2014 Springer International Publishing Switzerland
Repository Status
Restricted
Socio-economic Objectives
Information systems, technologies and services not elsewhere classified