RiMOM-IM： A Novel Iterative Framework for Instance Matching
RiMOM-IM： A Novel Iterative Framework for Instance Matching作者机构：Department of Computer Science and Technology Tsinghua University Beijing 100084 China College of Information Science and Technology Beijing Normal University Beijing 100875 China
出 版 物：《Journal of Computer Science & Technology》 (计算机科学技术学报（英文版）)
年 卷 期：2016年第31卷第1期
页 面：185-197页
核心收录：
学科分类：081203[工学-计算机应用技术] 08[工学] 081402[工学-结构工程] 081304[工学-建筑技术科学] 0835[工学-软件工程] 0813[工学-建筑学] 0814[工学-土木工程] 0812[工学-计算机科学与技术（可授工学、理学学位）]
基 金：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
主 题：instance matching large-scale knowledge base blocking similarity aggregation
摘 要：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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