Journal of Computer Science and Technology

, Volume 31, Issue 1, pp 185–197 | Cite as

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
Regular Paper

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.

Keywords

instance matching large-scale knowledge base blocking similarity aggregation 

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Chao Shao
    • 1
  • Lin-Mei Hu
    • 1
  • Juan-Zi Li
    • 1
  • Zhi-Chun Wang
    • 2
  • Tonglee Chung
    • 1
  • Jun-Bo Xia
    • 1
  1. 1.Department of Computer Science and TechnologyTsinghua UniversityBeijingChina
  2. 2.College of Information Science and TechnologyBeijing Normal UniversityBeijingChina

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