A Semi-supervised Learning Approach for Ontology Matching

  • Zhichun WangEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 480)


Ontology matching is the task of finding correspondences between semantically related entities in different ontologies, which is a key solution to the semantic heterogeneity problem. Recently, several supervised learning approaches for ontology matching have been proposed, which outperform traditional unsupervised approaches. The existing learning based approaches treat the similarity values of matchers as normal numerical features, and need a lot of training examples. In this paper, we propose a semi-supervised learning approach for ontology matching. Our approach needs a small set of training examples, and exploit the dominant relation of similarity metrics to enrich the training examples. A label propagation algorithm is used to determine the matching results. Experimental results show that our approach can achieve good matching results with a few training examples.


Ontology matching Semi-supervised learning Heterogeneity 



The work is supported by NSFC (No. 61202246), NSFC-ANR (No. 61261130588), and the Fundamental Research Funds for the Central Universities (2013NT56).


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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.College of Information Science and TechnologyBeijing Normal UniversityBeijingChina

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