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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.
Highlights
The search of information dates back to 1948 [1] it is a process of storage, organization, representation, and access to information by the user.These engines are used in several fields
Educational search engines bring a lot of benefits to e-learning systems; as the identification, adaptation, reuse and sharing of learning objects (LO)
The purpose of this article is to improve this technique by taking into consideration deeper relations in sentences that flesh out explanations and ensure that they convey meaning between them. It based on linguistic structures and semantic distances between notions according to OntOAlgO ontology
Summary
The search of information dates back to 1948 [1] it is a process of storage, organization, representation, and access to information by the user. These engines are used in several fields. Educational search engines are based on the exploratory learning approach allowing the learner to direct his own study according to his search and allowing the teacher to deposit and index his course. These search engines encounter a primary concern to the exact identification of LO. The degree of satisfaction is still far from the expectations of users
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