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 ins...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.展开更多
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.
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.
