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  • 标题:A Semantic Distances-Based Approach for a Deeply Indexing of Learning Objects
  • 本地全文:下载
  • 作者:Kamal El Guemmat ; Sara Ouahabi
  • 期刊名称:International Journal of Emerging Technologies in Learning (iJET)
  • 印刷版ISSN:1863-0383
  • 出版年度:2019
  • 卷号:14
  • 期号:06
  • 页码:27-40
  • DOI:10.3991/ijet.v14i06.9738
  • 出版社:Kassel University Press
  • 摘要:Educational search engines are important for users to find learning objects (LO). However, these engines have not reached maturity in terms of searching, they suffer from several worries like the deep extraction of notions which diminishes their performance. The purpose of this paper is to propose a new approach that allows depth extraction of LO’s notions to increase the relevance level of educational search engines. The proposed approach focuses on semi-automatic indexing of textual LO and more precisely the deeper relations of sentences that flesh out explanations. It based on linguistic structures and semantic distances between specific and generic notions according to OntOAlgO ontology. The notions obtained will be improved by learning object metadata (LOM) and will be represented semantically in final index. The tests performed on algorithmic LO, proving the usefulness of our approach to educational search engines. It increases the degree of precision and recall of notions extracted from LO.
  • 关键词:Educational search engine; Learning object; Linguistic structure; Semantic distance; Ontology; Learning object metadata.
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