T-TExTS builds a domain ontology into a knowledge graph and tests four embedding methods, finding Node2Vec yields the highest AUC (0.9642-0.9750) while a hybrid embedding balances ranking quality with interpretability across dataset sizes of 98-351 texts.
https://www.nysed.gov/ sites/default/files/programs/standards-instruction/appendix-a-common-core-s tandards-for-ela-literacy-supporting-research-glossary.pdf
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T-TExTS (Teaching Text Expansion for Teacher Scaffolding): Enhancing Text Selection in High School Literature through Knowledge Graph-Based Recommendation
T-TExTS builds a domain ontology into a knowledge graph and tests four embedding methods, finding Node2Vec yields the highest AUC (0.9642-0.9750) while a hybrid embedding balances ranking quality with interpretability across dataset sizes of 98-351 texts.