Leveraging Graph Structure in Seq2Seq Models for Knowledge Graph Link Prediction
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| Format: | Preprint |
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2026
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| author | Phuc, Luu Huu Thapa, Ratan Bahadur Nayyeri, Mojtaba Wu, Jingcheng Kharlamov, Evgeny Staab, Steffen |
| author_facet | Phuc, Luu Huu Thapa, Ratan Bahadur Nayyeri, Mojtaba Wu, Jingcheng Kharlamov, Evgeny Staab, Steffen |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_18211 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Leveraging Graph Structure in Seq2Seq Models for Knowledge Graph Link Prediction Phuc, Luu Huu Thapa, Ratan Bahadur Nayyeri, Mojtaba Wu, Jingcheng Kharlamov, Evgeny Staab, Steffen Computation and Language Artificial Intelligence 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. |
| title | Leveraging Graph Structure in Seq2Seq Models for Knowledge Graph Link Prediction |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2605.18211 |