A machine learning approach for instance matching based on similarity metrics

Authors:
Shu Rong;Xing Niu;Evan Wei Xiang;Haofen Wang;Qiang Yang;Yong Yu
Affiliations:
APEX Data & Knowledge Management Lab, Shanghai Jiao Tong University, China;APEX Data & Knowledge Management Lab, Shanghai Jiao Tong University, China;Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong;APEX Data & Knowledge Management Lab, Shanghai Jiao Tong University, China;Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong;APEX Data & Knowledge Management Lab, Shanghai Jiao Tong University, China
Venue:
ISWC'12 Proceedings of the 11th international conference on The Semantic Web - Volume Part I
Year:
2012

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Abstract

The Linking Open Data (LOD) project is an ongoing effort to construct a global data space, i.e. the Web of Data. One important part of this project is to establish owl:sameAs links among structured data sources. Such links indicate equivalent instances that refer to the same real-world object. The problem of discovering owl:sameAs links between pairwise data sources is called instance matching. Most of the existing approaches addressing this problem rely on the quality of prior schema matching, which is not always good enough in the LOD scenario. In this paper, we propose a schema-independent instance-pair similarity metric based on several general descriptive features. We transform the instance matching problem to the binary classification problem and solve it by machine learning algorithms. Furthermore, we employ some transfer learning methods to utilize the existing owl:sameAs links in LOD to reduce the demand for labeled data. We carry out experiments on some datasets of OAEI2010. The results show that our method performs well on real-world LOD data and outperforms the participants of OAEI2010.