Abstract
In most ontology-based scholar’s recommender systems, profiling approaches employ a reference ontology as a backbone hierarchy to learn the topics of scholar’s interests. It often works on the assumption that the reference ontology contains possible topics of scholars’ preferences. However, such single reference ontologies lack sufficient ontological concepts and poor ontological concepts, which unable to capture the entire scholars’ interests in terms of academic knowledge. In this paper, we extract, select, and merge heterogeneous subjects from different taxonomies on the Web and enrich by Wikipedia to constructs an OWL reference ontology for Computer Science domain. Compared to similar reference ontologies, our ontology purely supports the structure of scholars’ knowledge, contains richer topics of the domain, and best fits for profiling the scholars’ knowledge.
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Amini, B., Ibrahim, R., Othman, M.S., Ahmad, M.N. (2014). A Multi-reference Ontology for Profiling Scholars’ Background Knowledge. In: Herawan, T., Ghazali, R., Deris, M. (eds) Recent Advances on Soft Computing and Data Mining. Advances in Intelligent Systems and Computing, vol 287. Springer, Cham. https://doi.org/10.1007/978-3-319-07692-8_4
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DOI: https://doi.org/10.1007/978-3-319-07692-8_4
Publisher Name: Springer, Cham
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