A Semantic Distances-Based Approach for a Deeply Indexing of Learning Objects

Authors

  • Kamal El Guemmat Laboratory of information processing, Faculty of Sciences Ben M'Sik, Hassan II University, Cdt Driss El Harti, Sidi Othman, 7955, Casablanca, Morocco
  • Sara Ouahabi Laboratory of Modeling and Information Technology, Faculty of Sciences Ben M'Sik, Hassan II University, Cdt Driss El Harti, Sidi Othman, 7955, Casablanca, Morocco

DOI:

https://doi.org/10.3991/ijet.v14i06.9738

Keywords:

Educational search engine, Learning object, Linguistic structure, Semantic distance, Ontology, Learning object metadata.

Abstract


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.

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Published

2019-03-29

How to Cite

El Guemmat, K., & Ouahabi, S. (2019). A Semantic Distances-Based Approach for a Deeply Indexing of Learning Objects. International Journal of Emerging Technologies in Learning (iJET), 14(06), pp. 27–40. https://doi.org/10.3991/ijet.v14i06.9738

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Papers