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Incremental schema mapping


Anam, S and Kim, YS and Liu, Q, Incremental schema mapping, Lecture Notes in Artificial Intelligence 8862: 13th Pacific Rim International Conference on Artificial Intelligence (PRICAI-2014) Proceedings, 1-2 December 2014, Gold Coast, Australia, pp. 69-83. ISBN 978-3-319-13331-7 (2014) [Refereed Conference Paper]

Copyright Statement

Copyright 2014 Springer International Publishing Switzerland

DOI: doi:10.1007/978-3-319-13332-4_7


Schema mapping that provides a unified view to the users is essential to manage schema heterogeneity among different sources. Schema mapping can be conducted by machine learning or by knowledge engineering approach. Machine learning approach needs training data set for building models, but usually it is very difficult to obtain training datasets for large datasets. In addition, it is very difficult to change the model by human knowledge. Knowledge engineering approach encodes human knowledge directly, such that the knowledge base can be constructed with limited data, but it needs time consuming knowledge acquisition. This research proposes an incremental schema mapping method that employs Ripple-Down Rules (RDR) with the censored production rules (CPR). Our experimental results show that RDR approach shows comparable performance with the machine learning approaches and RDR knowledge base can be expanded incrementally as the cases classified increase.

Item Details

Item Type:Refereed Conference Paper
Keywords:schema mapping, incremental knowledge acquisition techniques and machine learning techniques
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)
ID Code:98400
Year Published:2014
Deposited By:Information and Communication Technology
Deposited On:2015-02-13
Last Modified:2018-03-18

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