通过混合多目标优化算法优化本体映射结果

Optimizing Ontology Alignment Through Hybrid Multi-object Evolutionary Algorithm

提出采用混合多目标优化算法以解决优化三类不同的相似度度量技术(基于语法学的相似度度量,基于语言学的相似度度量和基于分类学的相似度度量)的映射结果集成的问题.比起传统的基于遗传算法的方法,本文提出的方法能够同时实现三个目标,即最大化映射的查全率recall、查准率precision和f-度量f-measure值,获取的本体映射结果能够避免对于查全率或是查准率的偏好.实验结果表明本文提出的方法是有效的.

In this paper, we propose to use the hybrid multi-objective optimization algorithm to address the problem of optimizing the aggregation of three different basic similarity measures （ Syntactic Measure,Linguistic Measure and Taxonomy-based Measure ）. Com- paring with conventional genetic algorithm,the proposed method is able to realize three goals simultaneously, i. e. maximizing the a- lignment recall ,the alignment precision and the f-measure ,and the resulted ontology alignment could avoid bias to recall or precision value. Experimental results show that the proposed approach is effective.