TY - GEN
T1 - The interaction between schema matching and record matching in data integration (extended abstract)
AU - Gu, Binbin
AU - Li, Zhixu
AU - Zhang, Xiangliang
AU - Liu, An
AU - Liu, Guanfeng
AU - Zheng, Kai
AU - Zhao, Lei
AU - Zhou, Xiaofang
N1 - KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: This research is partially supported by Natural Science Foundation of China (Grant No. 61402313, 61303019, 61472263, 61572336), the King Abdullah University of Science and Technology, the Australian Research Council (Grants No. DP120102829), the Postdoctoral scientific research funding of Jiangsu Province (No. 1501090B) and the National Postdoctoral Funding (No. 2015M581859, 2016T90493).
PY - 2017/5/18
Y1 - 2017/5/18
N2 - Schema Matching (SM) and Record Matching (RM) are two necessary steps in integrating multiple relational tables of different schemas, where SM unifies the schemas and RM detects records referring to the same real-world entity. The two processes have been thoroughly studied separately, but few attention has been paid to the interaction of SM and RM. In this work we find that, even alternating them in a simple manner, SM and RM can benefit from each other to reach a better integration performance (i.e., in terms of precision and recall). Therefore, combining SM and RM is a promising solution for improving data integration.
AB - Schema Matching (SM) and Record Matching (RM) are two necessary steps in integrating multiple relational tables of different schemas, where SM unifies the schemas and RM detects records referring to the same real-world entity. The two processes have been thoroughly studied separately, but few attention has been paid to the interaction of SM and RM. In this work we find that, even alternating them in a simple manner, SM and RM can benefit from each other to reach a better integration performance (i.e., in terms of precision and recall). Therefore, combining SM and RM is a promising solution for improving data integration.
UR - http://hdl.handle.net/10754/624029
UR - http://ieeexplore.ieee.org/document/7929919/
UR - http://www.scopus.com/inward/record.url?scp=85021253226&partnerID=8YFLogxK
U2 - 10.1109/ICDE.2017.23
DO - 10.1109/ICDE.2017.23
M3 - Conference contribution
SN - 9781509065431
SP - 33
EP - 34
BT - 2017 IEEE 33rd International Conference on Data Engineering (ICDE)
PB - Institute of Electrical and Electronics Engineers (IEEE)
ER -