复杂本体匹配方法主要分为基于匹配模式的方法和基于机器学习的方法,前者需要人工制定启发式规则而后者易陷入局部最优解.针对上述问题,提出一种融合匹配模式和机器学习的复杂匹配方法.引入路径特征刻画本体中实例具有的性质,路径特征是匹配模式的具体化.在此基础上,使用一阶归纳学习器组合路径特征得到复杂的匹配结果.实验结果表明,该方法可以自动学习到本体间的复杂匹配,且与基于一阶归纳学习器的复杂匹配方法相比,能够有效缓解局部最优问题.%The complex ontology matching methods are mainly classified into correspondence pattern based methods and machine learning based ones.The former are heuristic ones that are designed with some knowledge about the ontologies to be aligned and the latter are easy to fall into local optimums.This paper proposes a novel complex ontology matching method which benefits from both correspondence pattern and machine learning.The key of the method is the introduction of path feature to characterize the information of instances and path feature is an instantiation of correspondence pattern.Path features are combined by First Order Inductive Learner (FOIL) to acquire complex mappings.Experimental results show that this method automatically learns complex correspondences between ontologies,and effectively relieves the problem of local optimum compared with FOIL-based complex matching method.
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