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arXiv · 2605.18211

Leveraging Graph Structure in Seq2Seq Models for Knowledge Graph Link Prediction

Luu Huu Phuc·Ratan Bahadur Thapa·Mojtaba Nayyeri·Jingcheng Wu·Evgeny Kharlamov·Steffen Staab
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Abstract

We introduce Graph-Augmented Sequence-to-Sequence (GA-S2S), a novel framework that integrates a T5-small encoder-decoder with a Relational Graph Attention Network (RGAT) to improve link prediction in knowledge graphs. While existing Seq2Seq models rely solely on surface-level textual descriptions of entities and relations and at best, flatten the neighborhoods of a query entity into a single linear sequence, thereby discarding the inherent graph structure, GA-S2S jointly encodes both textual features and the full $k$-hop subgraph topology surrounding the query entity. By integrating raw encoder outputs with RGAT's relation-aware embeddings, our model captures and leverages richer multi-hop relational patterns and textual information. Our preliminary experiments on the CoDEx dataset demonstrate that GA-S2S outperforms competitive Seq2Seq-based baseline models, achieving up to a 19\% relative gain in link prediction accuracy.

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Luu Huu Phuc, Ratan Bahadur Thapa, Mojtaba Nayyeri, Jingcheng Wu, Evgeny Kharlamov, Steffen Staab. 2026-05-18. Leveraging Graph Structure in Seq2Seq Models for Knowledge Graph Link Prediction. https://arxiv.org/abs/2605.18211

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