A Combined Approach for Eliciting Relationships for Educational Ontologies Using General-Purpose Knowledge Bases

Educational Ontologies describe learning domains by specifying the topics to be learned together with the relationships among the topics. Any technology supported learning system could take advantage of an educational ontology in the process of guiding students during their learning processes. This...

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Bibliographic Details
Published in:IEEE access 2019, Vol.7, p.48339-48355
Main Authors: Conde, Angel, Larranaga, Mikel, Arruarte, Ana, Elorriaga, Jon A.
Format: Article
Language:English
Subjects:
domain module
Domains
Education
educational ontologies
Electronic publishing
Encyclopedias
Extractors
Internet
knowledge bases
Knowledge bases (artificial intelligence)
Learning
Modular design
Ontologies
Ontology
relationship extraction
Taxonomy
Technology supported learning systems
Textbooks
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Summary:Educational Ontologies describe learning domains by specifying the topics to be learned together with the relationships among the topics. Any technology supported learning system could take advantage of an educational ontology in the process of guiding students during their learning processes. This paper presents LiReWi, a system for the elicitation of relationships for educational ontologies from electronic textbooks. LiReWi combines grammar-based, co-occurrence-based and taxonomy-based methods together with several knowledge bases, such as Wikipedia, WordNet, WikiTaxonomy, WibiTaxonomy, and WikiRelations to elicit isA, partOf, prerequisite and pedagogicallyClose relationships between learning domain topics. LiReWi performs a three-step procedure to fulfill its task: first, all the topics are mapped to the diverse knowledge bases that will be used to identify the relationships; then, several relationship extractors, each using a different approach, are concurrently run to elicit candidate relationships; and, finally, the results are combined and filtered to obtain the final set of pedagogical relationships. LiReWi is designed following a modular approach that enables the inclusion of new relationship extractors. The evaluation conducted to validate the proposal is also reported in this paper.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2910079