RiMOM-IM: A Novel Iterative Framework for Instance Matching

Chao Shao; Lin Mei Hu; Juan Zi Li; Zhi Chun Wang; Tonglee Chung; Jun Bo Xia

doi:10.1007/s11390-016-1620-z

RiMOM-IM: A Novel Iterative Framework for Instance Matching

Chao Shao, Lin Mei Hu^*, Juan Zi Li, Zhi Chun Wang, Tonglee Chung, Jun Bo Xia

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

56 Citations (Scopus)

Abstract

Instance matching, which aims at discovering the correspondences of instances between knowledge bases, is a fundamental issue for the ontological data sharing and integration in Semantic Web. Although considerable instance matching approaches have already been proposed, how to ensure both high accuracy and efficiency is still a big challenge when dealing with large-scale knowledge bases. This paper proposes an iterative framework, RiMOM-IM (RiMOM-Instance Matching). The key idea behind this framework is to fully utilize the distinctive and available matching information to improve the efficiency and control the error propagation. We participated in the 2013 and 2014 competition of Ontology Alignment Evaluation Initiative (OAEI), and our system was ranked the first. Furthermore, the experiments on previous OAEI datasets also show that our system performs the best.

Original language	English
Pages (from-to)	185-197
Number of pages	13
Journal	Journal of Computer Science and Technology
Volume	31
Issue number	1
DOIs	https://doi.org/10.1007/s11390-016-1620-z
Publication status	Published - 1 Jan 2016
Externally published	Yes

Keywords

blocking
instance matching
large-scale knowledge base
similarity aggregation

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10.1007/s11390-016-1620-z

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Shao, C., Hu, L. M., Li, J. Z., Wang, Z. C., Chung, T., & Xia, J. B. (2016). RiMOM-IM: A Novel Iterative Framework for Instance Matching. Journal of Computer Science and Technology, 31(1), 185-197. https://doi.org/10.1007/s11390-016-1620-z

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abstract = "Instance matching, which aims at discovering the correspondences of instances between knowledge bases, is a fundamental issue for the ontological data sharing and integration in Semantic Web. Although considerable instance matching approaches have already been proposed, how to ensure both high accuracy and efficiency is still a big challenge when dealing with large-scale knowledge bases. This paper proposes an iterative framework, RiMOM-IM (RiMOM-Instance Matching). The key idea behind this framework is to fully utilize the distinctive and available matching information to improve the efficiency and control the error propagation. We participated in the 2013 and 2014 competition of Ontology Alignment Evaluation Initiative (OAEI), and our system was ranked the first. Furthermore, the experiments on previous OAEI datasets also show that our system performs the best.",

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AU - Xia, Jun Bo

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RiMOM-IM: A Novel Iterative Framework for Instance Matching

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Keywords

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