<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The problem of thematic indexing of Open Educational Resources (OERs) is often a time-consuming and costly manual task, relying on expert knowledge. In addition, a lot of online resources may be poorly annotated with arbitrary, ad-hoc keywords instead of standard, controlled vocabularies, a fact that stretches up the search space and hampers interoperability. In this paper, we propose an approach that facilitates curators and instructors to annotate thematically educational content. To achieve this, we combine explicit knowledge graph representations with vector-based learning of formal thesaurus terms. We apply this technique in the domain of biomedical literature and show that it is possible to produce a reasonable set of thematic suggestions which exceed a certain similarity threshold. Our method yields acceptable levels for precision and recall against corpora already indexed by human experts. Ordering of recommendations is significant and this approach can also have satisfactory results for the ranking problem. However, traditional IR metrics may not be adequate due to semantic relations amongst recommended terms being underutilized.</p>
</abstract>

The problem of thematic indexing of Open Educational Resources (OERs) is often a time-consuming and costly manual task, relying on expert knowledge. In addition, a lot of online resources may be poorly annotated with arbitrary, ad-hoc keywords instead of standard, controlled vocabularies, a fact that stretches up the search space and hampers interoperability. In this paper, we propose an approach that facilitates curators and instructors to annotate thematically educational content. To achieve this, we combine explicit knowledge graph representations with vector-based learning of formal thesaurus terms. We apply this technique in the domain of biomedical literature and show that it is possible to produce a reasonable set of thematic suggestions which exceed a certain similarity threshold. Our method yields acceptable levels for precision and recall against corpora already indexed by human experts. Ordering of recommendations is significant and this approach can also have satisfactory results for the ranking problem. However, traditional IR metrics may not be adequate due to semantic relations amongst recommended terms being underutilized.

Semantic Classification and Indexing of Open Educational Resources with Word Embeddings and Ontologies

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

The problem of thematic indexing of Open Educational Resources (OERs) is often a time-consuming and costly manual task, relying on expert knowledge. In...

Semantic Classification and Indexing of Open Educational Resources with Word Embeddings and Ontologies

Pubblicato online: 13 set 2020

Pagine: 95 - 116

Ricevuto: 09 mar 2020

Accettato: 26 giu 2020

DOI: https://doi.org/10.2478/cait-2020-0043

Parole chiave
Learning objects, Open Educational Resources (OERs), classification, word embeddings, thesauri, ontologies, doc2vec, federated search, MeSH

© 2020 Dimitrios A. Koutsomitropoulos et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Semantic Classification and Indexing of Open Educational Resources with Word Embeddings and Ontologies

Pubblicato online: 13 set 2020

Pagine: 95 - 116

Ricevuto: 09 mar 2020

Accettato: 26 giu 2020

DOI: https://doi.org/10.2478/cait-2020-0043

Parole chiaveLearning objects, Open Educational Resources (OERs), classification, word embeddings, thesauri, ontologies, doc2vec, federated search, MeSH

© 2020 Dimitrios A. Koutsomitropoulos et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Parole chiave
Learning objects, Open Educational Resources (OERs), classification, word embeddings, thesauri, ontologies, doc2vec, federated search, MeSH