Using ontology for personalised course recommendation applications

Mohammed Essmat Ibrahim, Yanyan Yang, David Ndzi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

9 Citations (Scopus)
191 Downloads (Pure)


The primary data source for universities and courses for students is increasingly becoming the web, and with a vast amount of information about thousands of courses on different websites, it is quite a task to find one that matches a student’s needs. That is why we are proposing the “Course Recom-mendation System”, a system that suggests the course best suited for prospective students. As there has been a huge increase in course content on the Internet, finding the course you really need has become time-consuming, so we are pro-posing to use an ontology-based approach to semantic content recommendation. The aim is to enhance the efficiency and effectiveness of providing students with suitable recommendations. The recommender takes into consideration knowledge about the user (the student’s profile) and course content, as well as knowledge about the domain that is being learned. Ontology is used to both models and represent such forms of knowledge. There are four steps to this: ex-tracting information from multiple sources, applying ontologies by using Protégé tools, semantic relevance calculation and refining the recommendation. A per-sonalised, complete and augmented course is then suggested for the student, based on these steps.
Original languageEnglish
Title of host publicationComputational Science and Its Applications – ICCSA 2017
Subtitle of host publication17th International Conference, Trieste, Italy, July 3-6, 2017, Proceedings, Part I
PublisherSpringer International Publishing AG
ISBN (Electronic)978-3-319-62392-4
ISBN (Print)978-3-319-62391-7
Publication statusE-pub ahead of print - 6 Jul 2017

Publication series

NameLecture Notes in Computer Science
PublisherSpringer International Publishing AG
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


  • Recommendation systems
  • Semantic Similarity
  • Semantic Web
  • Course Selec-tion,
  • Ontology


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