中文摘要：

针对已有的本体映射方法在处理大规模本体映射任务时效率和有效性较低的问题,文中提出了一个基于数据场的本体映射算法.该算法首先使用高效的相似度算法,建立本体中元素对另一本体的初始相关度;然后,利用数据场势函数引入周围本体元素对当前元素的影响,修正初始相关度,并最终确定本体间的相关子本体;最后,利用针对性的方法对上述相关子本体进行更有效的映射.实验结果表明,该算法可以在提高映射结果质量的同时保证较高的映射效率.

英文摘要：

As the cornerstone of ontology based data integration,data exchange and metadata management,ontology mapping,aiming to obtain semantic correspondences between two ontologies,has attracted wide attentions of researchers in community of the Semantic Web.However when getting the alignments between two large-scale ontologies,the existed mapping methods are not very effective and efficient due to neglecting of relevant sub-ontologies in those two ontologies.For addressing this important issue,in this paper,a data field based ontology mapping approach is proposed to improve the effectiveness and the efficiency of large scale ontology mapping tasks.At first,this approach employs a light weight similarity computing method to collect the initial relevance values between one ontology＇s elements to another ontology.Then,the potential functions of a data field are taken into account to revise the relevance of an ontology element according to its surroundings.Finally,the relevant sub-ontologies are found and extracted,and a fine-grained alignment approach is used to mapping between the extracted ontologies for better results.The experiments show that the proposed approach is able to effectively deal with the large scale ontology mapping issue with the satisfactory efficiency.