CRPITV159Anam2.pdf (735.34 kB)
Schema mapping using hybrid ripple-down rules
conference contribution
posted on 2023-05-23, 10:52 authored by Anam, S, Kim, YS, Byeong KangByeong Kang, Liu, QSchema 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.
History
Publication title
Proceedings of the 38th Australasian Computer Science Conference (ACSC 2015)Volume
159Editors
D ParryPagination
17-26ISBN
9781921770418Department/School
School of Information and Communication TechnologyPublisher
Australian Computer Society Inc.Place of publication
Sydney, AustraliaEvent title
38th Australasian Computer Science Conference (ACSC 2015)Event Venue
Sydney, AustraliaDate of Event (Start Date)
2015-01-27Date of Event (End Date)
2015-01-30Rights statement
Copyright 2015 Australian Computer Society, Inc.Repository Status
- Open