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Volume 31 Issue 1
January  2016
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Chao Shao, Lin-Mei Hu, Juan-Zi Li, Zhi-Chun Wang, Tonglee Chung, Jun-Bo Xia. RiMOM-IM:A Novel Iterative Framework for Instance Matching[J]. Journal of Computer Science and Technology, 2016, 31(1): 185-197. DOI: 10.1007/s11390-016-1620-z
Citation: Chao Shao, Lin-Mei Hu, Juan-Zi Li, Zhi-Chun Wang, Tonglee Chung, Jun-Bo Xia. RiMOM-IM:A Novel Iterative Framework for Instance Matching[J]. Journal of Computer Science and Technology, 2016, 31(1): 185-197. DOI: 10.1007/s11390-016-1620-z
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RiMOM-IM:A Novel Iterative Framework for Instance Matching

  • 1 Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China;
    2 College of Information Science and Technology, Beijing Normal University, Beijing 100875, China

Funds: The work is supported by the National Basic Research 973 Program of China under Grant No. 2014CB340504, the National Natural Science Foundation of China and the French National Research Agency under Grant No. 61261130588, the Tsinghua University Initiative Scientific Research Program under Grant No. 20131089256, the Science and Technology Support Program of China under Grant No. 2014BAK04B00 and the Tsinghua University and National University of Singapore Extreme Search Joint Centre.
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  • Author Bio:

    Chao Shao is a master student in the Department of Computer Science and Technology, Tsinghua University, Beijing. His research interests include instance matching, semantic web, text mining, and big data.t

  • Corresponding author:

    Lin-Mei Hu E-mail: hulinmei1991@gmail.com

  • Received Date: October 29, 2014
  • Revised Date: July 16, 2015
  • Published Date: January 04, 2016
    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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    • References (26)
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