Study on Ontology Ranking Models Based on the Ensemble Learning

Study on Ontology Ranking Models Based on the Ensemble Learning

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) and Shi Jinsheng (Beijing Advanced Innovation Center for Imaging Technology, Capital Normal University, Beijing, China)
Copyright: © 2018 |Pages: 24
DOI: 10.4018/IJSWIS.2018040107

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.
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1. Introduction

The Semantic Web is an intelligent network. It extends the existing network, increasing the semantic support. Similar to web pages of traditional network, ontology is the main organization form of the information in Semantic Web, generally being described by ontology language, such as RDF (Resource Description Framework) and OWL (Web Ontology Language). With the gradual increase of ontology files in the network, how to quickly get the required ontology for users to improve the reusability of ontology has a practical significance.

At present, according to the ranking content of the ontology, the research of rank can be divided into: 1) the ontology ranking, which means ranking in ontology file; 2) the entity ranking, which means ranking the entity element of the ontology; 3) the relationship ranking, which means ranking the correlation among entities. All the above ranking ways have their own advantages. This paper merely conducts a thorough research on the sorting method of the ontology files.

The main idea of the rank is to feed the ontology files in line with users’ queries back to users according to the relevant degree. At present, the research on the ranking in ontology files is generally summarized in the following categories:

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