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.
In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp
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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.