基于改进协同训练的本体映射方法

针对基于机器学习的本体映射方法存在的人工标记代价高和样本不平衡问题,将本体映射建模为二视图上的协同训练问题,分别从本体模式层和数据层提取特征集。通过对本体概念对进行预匹配,缩小样本不平衡度。分析了传统协同训练方法的局限性,结合主动学习思想,设计了一种考虑样本价值的改进协同训练算法,在每轮迭代中选择更有价值的未标记样本更新训练集。实验结果表明,该方法学习效率更高,同时能取得较好的本体映射结果。

Improved co-training based ontology matching method

Aiming at the problem of high labeling cost and imbalanced data exists in the machine learning based ontology matching, the model ontology matching is taken as an co-training problem on two views, which respectively extracts feature sets from the ontology schema level and the data level. Through pre-matching ontologies’ concept pairs, the imbalanced degree of samples is reduced. After analyzing the limitations of the traditional co-training method, in combination with the active learning theory, an improved sample value considered co-training method is proposed, it can choose more valuable unlabeled samples to update training sets in each iteration. Experimental results show that the proposed method is more efficient and achieves better competitive ontology matching results.