Abstract The mapping efficiency is the key to some dynamic mapping applications. A modularization based large-scale ontology mapping approach is proposed. Firstly, it uses a weighted semantic distance and information content based method is employed to calculate the similarity of ontology concepts. Then, by an improved efficient agglomerative hierarchical clustering algorithm, the concepts are clustered and the sub-ontologies are extracted. Finally, an elaborate information retrieval based method is designed to find related sub-ontologies from heterogeneous ontologies. The proposed approach reduces time complexity by pruning candidate search space effectively. The experimental results show that the proposed approach improves the mapping efficiency significantly with high-quality mapping results.
About author:: 孙煜飞(通讯作者),男,1988年生,博士研究生,主要研究方向为语义Web、本体映射.E-mail:sharesorrows@163.com. (SUN Yufei(Corresponding author), born in 1988, Ph.D. candidate. His research interests include semantic web and ontology mapping.) 马良荔,女,1968年生,博士,教授,主要研究方向为信息系统、决策支持.E-mail:maliangl@163.com. (MA Liangli, born in 1968, Ph.D., professor. Her research interests include information systems and decision support.) 郭晓明,女,1984年生,博士,助理工程师,主要研究方向为数据集成.E-mail:402107676@qq.com. (GUO Xiaoming, born in 1984, Ph.D., assistant engineer. Her research interests include data integration.) 覃基伟,男,1990年生,博士研究生,主要研究方向为云存储.E-mail:851293113@qq.com. (QIN Jiwei, born in 1990, Ph.D. candidate. His research interests include cloud storage.)
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