A Semi-supervised Learning Approach for Ontology Matching
A Semi-supervised Learning Approach for Ontology Matching
复制标题
本体匹配的半监督学习方法
DOI：
10.1007/978-3-662-45495-4_2
复制
发表时间：
2014
期刊：
影响因子：
--
通讯作者：
Zhichun Wang
中科院分区：
文献类型：
--
作者：
Zhichun Wang
本体匹配是在不同本体中寻找语义相关实体之间的对应关系，是解决语义异构性问题的关键。最近，已有几种监督学习方法被提出用于本体匹配，它们的性能优于传统的非监督方法。现有的基于学习的方法将匹配者的相似度视为正常的数值特征，需要大量的训练样本。本文提出了一种用于本体匹配的半监督学习方法。该方法只需要少量的训练样本，并利用相似度量之间的优势关系来丰富训练样本。使用一种标签传播算法来确定匹配结果。实验结果表明，该方法在训练样本较少的情况下，能够达到较好的匹配效果。
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.