Study on Ontology Ranking Models Based on the Ensemble Learning

Liu Jie, Yuan Kerou, Zhou Jianshe and Shi Jinsheng
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Liu Jie: College of Information Engineering, Capital Normal University, Beijing, China & Beijing Advanced Innovation Center for Imaging Technology, Capital Normal University, Beijing, China
Yuan Kerou: College of Information Engineering, Capital Normal University, Beijing, China
Zhou Jianshe: Beijing Advanced Innovation Center for Imaging Technology, Capital Normal University, Beijing, China
Shi Jinsheng: Beijing Advanced Innovation Center for Imaging Technology, Capital Normal University, Beijing, China

International Journal on Semantic Web and Information Systems (IJSWIS), 2018, vol. 14, issue 2, 138-161

Abstract: This article describes how more knowledge appears on the Internet than in an ontological form. Displaying results to users precisely when searching is the key issue of the research on ontology retrieval. The considered factors of ontology ranking are not only limited to internal character-matching, but analysis of metadata, including the entities, structures and the relations in ontologies. Currently, existing single feature ranking algorithms focus on the structures, elements and the contents of a certain aspect in ontology, thus, the results are not satisfactory. Combining multiple single-featured models seems to achieve better results, but the objectivity and versatility of models' weights are debatable. Machine learning effectively solves the problem and putting advantages of ranking learning algorithms together is the pressing issue. So we propose ensemble learning strategies to combine different algorithms in ontology ranking. And the ranking result is more satisfied compared to Swoogle and base algorithms.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jswis0:v:14:y:2018:i:2:p:138-161

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