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.

Data and Resources

Cite this as

Omar Arab Oghli (2022). Dataset: Templates Recommendation in the Open Research Knowledge Graph. https://doi.org/10.25835/qi8a8xpz

DOI retrieved: June 3, 2022

Additional Info

Field Value
Imported on January 12, 2023
Last update August 4, 2023
License CC-BY-3.0
Source https://data.uni-hannover.de/dataset/templates-recommendation-in-the-open-research-knowledge-graph
Author Omar Arab Oghli
Author Email Omar Arab Oghli
Maintainer Omar Arab Oghli
Maintainer Email Omar Arab Oghli
Source Creation 03 June, 2022, 06:48 AM (UTC+0000)
Source Modified 03 June, 2022, 06:58 AM (UTC+0000)
1st Supervisor Jennifer D'Souza
2nd Supervisor Sören Auer