@inproceedings{qayyum-etal-2025-teg,
title = "{T}e{G}-{DR}ec: Inductive Text-Graph Learning for Unseen Node Scientific Dataset Recommendation",
author = "Qayyum, Ammar and
Irnawan, Bassamtiano and
Fukumoto, Fumiyo and
Kamarudin, Latifah and
Go, Kentaro and
Suzuki, Yoshimi",
editor = "Accomazzi, Alberto and
Ghosal, Tirthankar and
Grezes, Felix and
Lockhart, Kelly",
booktitle = "Proceedings of the Third Workshop for Artificial Intelligence for Scientific Publications",
month = dec,
year = "2025",
address = "Mumbai, India and virtual",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.wasp-main.12/",
pages = "103--114",
ISBN = "979-8-89176-310-4",
abstract = "Scientific datasets are crucial for evaluating scientific research, and their number is increasing rapidly. Most scientific dataset recommendation systems use Information Retrieval (IR) methods that model semantics while overlooking interactions. Graph Neural Networks (GNNs) excel at handling interactions between entities but often overlook textual content, limiting their ability to generalise to unseen nodes. We propose TeG-DRec, a framework for scientific dataset recommendation that integrates GNNs and textual content via a subgraph generation module to ensure correct propagation throughout the model, enabling handling of unseen data. Experimental results on the dataset recommendation{'}s dataset show that our method outperformed the baselines for text-based IR and graph-based recommendation systems. Our source code is available at https://github.com/Maqif14/TeG-DRec.git"
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<abstract>Scientific datasets are crucial for evaluating scientific research, and their number is increasing rapidly. Most scientific dataset recommendation systems use Information Retrieval (IR) methods that model semantics while overlooking interactions. Graph Neural Networks (GNNs) excel at handling interactions between entities but often overlook textual content, limiting their ability to generalise to unseen nodes. We propose TeG-DRec, a framework for scientific dataset recommendation that integrates GNNs and textual content via a subgraph generation module to ensure correct propagation throughout the model, enabling handling of unseen data. Experimental results on the dataset recommendation’s dataset show that our method outperformed the baselines for text-based IR and graph-based recommendation systems. Our source code is available at https://github.com/Maqif14/TeG-DRec.git</abstract>
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%0 Conference Proceedings
%T TeG-DRec: Inductive Text-Graph Learning for Unseen Node Scientific Dataset Recommendation
%A Qayyum, Ammar
%A Irnawan, Bassamtiano
%A Fukumoto, Fumiyo
%A Kamarudin, Latifah
%A Go, Kentaro
%A Suzuki, Yoshimi
%Y Accomazzi, Alberto
%Y Ghosal, Tirthankar
%Y Grezes, Felix
%Y Lockhart, Kelly
%S Proceedings of the Third Workshop for Artificial Intelligence for Scientific Publications
%D 2025
%8 December
%I Association for Computational Linguistics
%C Mumbai, India and virtual
%@ 979-8-89176-310-4
%F qayyum-etal-2025-teg
%X Scientific datasets are crucial for evaluating scientific research, and their number is increasing rapidly. Most scientific dataset recommendation systems use Information Retrieval (IR) methods that model semantics while overlooking interactions. Graph Neural Networks (GNNs) excel at handling interactions between entities but often overlook textual content, limiting their ability to generalise to unseen nodes. We propose TeG-DRec, a framework for scientific dataset recommendation that integrates GNNs and textual content via a subgraph generation module to ensure correct propagation throughout the model, enabling handling of unseen data. Experimental results on the dataset recommendation’s dataset show that our method outperformed the baselines for text-based IR and graph-based recommendation systems. Our source code is available at https://github.com/Maqif14/TeG-DRec.git
%U https://aclanthology.org/2025.wasp-main.12/
%P 103-114
Markdown (Informal)
[TeG-DRec: Inductive Text-Graph Learning for Unseen Node Scientific Dataset Recommendation](https://aclanthology.org/2025.wasp-main.12/) (Qayyum et al., WASP 2025)
ACL