Fast-and-Frugal Text-Graph Transformers are Effective Link Predictors

• URL: http://arxiv.org/abs/2408.06778v3
• Date: Sun, 15 Dec 2024 12:37:51 GMT
• Title: Fast-and-Frugal Text-Graph Transformers are Effective Link Predictors
• Authors: Andrei C. Coman, Christos Theodoropoulos, Marie-Francine Moens, James Henderson,
• Abstract summary: 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.
• Score: 28.403174369346715
• License:
• 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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