Templates Recommendation in the Open Research Knowledge Graph

This dataset has been created for implementing a content-based recommender system in the context of the Open Research Knowledge Graph (ORKG). The recommender system accepts research paper's title and abstracts as input and recommends existing templates in the ORKG semantically relevant to the given paper.

Two approaches have been trained on this dataset in the context of this https://doi.org/10.15488/11834 master's thesis, namely a Natural Language Inference (NLI) approach based on SciBERT embeddings and an unsupervised approach based on ElasticSearch.

This publication consists therefore of one general dataset, two training sets for each approach, validation set for the supervised approach and a test set for both approaches.

Daten und Ressourcen

Cite this as

Omar Arab Oghli (2022). Templates Recommendation in the Open Research Knowledge Graph [Data set]. LUIS. https://doi.org/10.25835/qi8a8xpz
Retrieved: 07:46 24 Oct 2026 (UTC)

Zusätzliche Informationen

Feld Wert
Quelle https://zenodo.org/record/6607165#.YpmuhjlBxFE
Autor Omar Arab Oghli
Verantwortlicher Omar Arab Oghli
Zuletzt aktualisiert Juni 3, 2022, 06:58 (UTC)
Erstellt Juni 3, 2022, 06:48 (UTC)
Lizenz Creative Commons Attribution 3.0
Dataset Size 9.0 MByte
1st Supervisor Jennifer D'Souza
2nd Supervisor Sören Auer