@inproceedings{coman-etal-2025-fast,
title = "Fast-and-Frugal Text-Graph Transformers are Effective Link Predictors",
author = "Coman, Andrei Catalin and
Theodoropoulos, Christos and
Moens, Marie-Francine and
Henderson, James",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.615/",
doi = "10.18653/v1/2025.findings-acl.615",
pages = "11828--11841",
ISBN = "979-8-89176-256-5",
abstract = "We propose Fast-and-Frugal Text-Graph (FnF-TG) Transformers, a Transformer-based framework that unifies textual and structural information for inductive link prediction in text-attributed knowledge graphs. We demonstrate that, by effectively encoding ego-graphs (1-hop neighbourhoods), we can reduce the reliance on resource-intensive textual encoders. This makes the model both fast at training and inference time, as well as frugal in terms of cost. We perform a comprehensive evaluation on three popular datasets and show that FnF-TG can achieve superior performance compared to previous state-of-the-art methods. We also extend inductive learning to a fully inductive setting, where relations don{'}t rely on transductive (fixed) representations, as in previous work, but are a function of their textual description. Additionally, we introduce new variants of existing datasets, specifically designed to test the performance of models on unseen relations at inference time, thus offering a new test-bench for fully inductive link prediction."
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<abstract>We propose Fast-and-Frugal Text-Graph (FnF-TG) Transformers, a Transformer-based framework that unifies textual and structural information for inductive link prediction in text-attributed knowledge graphs. We demonstrate that, by effectively encoding ego-graphs (1-hop neighbourhoods), we can reduce the reliance on resource-intensive textual encoders. This makes the model both fast at training and inference time, as well as frugal in terms of cost. We perform a comprehensive evaluation on three popular datasets and show that FnF-TG can achieve superior performance compared to previous state-of-the-art methods. We also extend inductive learning to a fully inductive setting, where relations don’t rely on transductive (fixed) representations, as in previous work, but are a function of their textual description. Additionally, we introduce new variants of existing datasets, specifically designed to test the performance of models on unseen relations at inference time, thus offering a new test-bench for fully inductive link prediction.</abstract>
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%0 Conference Proceedings
%T Fast-and-Frugal Text-Graph Transformers are Effective Link Predictors
%A Coman, Andrei Catalin
%A Theodoropoulos, Christos
%A Moens, Marie-Francine
%A Henderson, James
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F coman-etal-2025-fast
%X We propose Fast-and-Frugal Text-Graph (FnF-TG) Transformers, a Transformer-based framework that unifies textual and structural information for inductive link prediction in text-attributed knowledge graphs. We demonstrate that, by effectively encoding ego-graphs (1-hop neighbourhoods), we can reduce the reliance on resource-intensive textual encoders. This makes the model both fast at training and inference time, as well as frugal in terms of cost. We perform a comprehensive evaluation on three popular datasets and show that FnF-TG can achieve superior performance compared to previous state-of-the-art methods. We also extend inductive learning to a fully inductive setting, where relations don’t rely on transductive (fixed) representations, as in previous work, but are a function of their textual description. Additionally, we introduce new variants of existing datasets, specifically designed to test the performance of models on unseen relations at inference time, thus offering a new test-bench for fully inductive link prediction.
%R 10.18653/v1/2025.findings-acl.615
%U https://aclanthology.org/2025.findings-acl.615/
%U https://doi.org/10.18653/v1/2025.findings-acl.615
%P 11828-11841
Markdown (Informal)
[Fast-and-Frugal Text-Graph Transformers are Effective Link Predictors](https://aclanthology.org/2025.findings-acl.615/) (Coman et al., Findings 2025)
ACL