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Schema mapping is essential to manage schema heterogeneity among different sources. Schema mapping can be conducted by using machine learning algorithms or by knowledge engineering approaches. These two approaches have advantages and disadvantages. The machine learning approaches can learn their model using the data, but they are static, so they cannot be modified to reflect the domain data changes. Inversely, the knowledge engineering approaches need domain experts, but they can be modified by reflecting the domain data changes. In order to exploit the advantages of both approaches and reduce the limitations, we propose a hybrid approach, called Hybrid-RDR, which combines a machine learning algorithm with ripple-down rules (RDR), an incremental knowledge engineering approach. A model is constructed by a decision tree algorithm and then it is extended by adding rules incrementally. This approach achieves higher performance in terms of precision, recall and F-measure compared to the machine learning algorithm. This significantly reduces the effort for classifying the related schemas one by one by manually creating rules and it is possible to modify the knowledge base by adding rules without creating model again if decision tree gives wrong classifications whenever the schema data changes over time.

Item Type:	Refereed Conference Paper
Keywords:	machine learning algorithm, knowledge engineering approach, schema mapping, incremental learning
Research Division:	Information and Computing Sciences
Research Group:	Computer vision and multimedia computation
Research Field:	Pattern recognition
Objective Division:	Information and Communication Services
Objective Group:	Information services
Objective Field:	Information 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:	106697
Year Published:	2015
Deposited By:	Information and Communication Technology
Deposited On:	2016-02-17
Last Modified:	2018-03-27
Downloads:	67 View Download Statistics