RiMOM-IM: A Novel Iterative Framework for Instance Matching
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- 作者：Chao Shao ; Lin-Mei Hu ; Juan-Zi Li…
- 关键词：instance matching ; large ; scale knowledge base ; blocking ; similarity aggregation
- 刊名：Journal of Computer Science and Technology
- 出版年：2016
- 出版时间：January 2016
- 年：2016
- 卷：31
- 期：1
- 页码：185-197
- 全文大小：796 KB
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Lin-Mei Hu (1)
Juan-Zi Li (1)
Zhi-Chun Wang (2)
Tonglee Chung (1)
Jun-Bo Xia (1)
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 - 刊物类别：Computer Science
- 刊物主题：Computer Science, general
Software Engineering
Theory of Computation
Data Structures, Cryptology and Information Theory
Artificial Intelligence and Robotics
Information Systems Applications and The Internet
Chinese Library of Science - 出版者：Springer Boston
- ISSN：1860-4749
文摘
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