Leveraging Graph Structure in Seq2Seq Models for Knowledge Graph Link Prediction

2026-05-18 · Source: Computation and Language · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Natural Language Processing · Depth: Expert, quick

Summary

Graph-Augmented Sequence-to-Sequence (GA-S2S) is a new framework that enhances knowledge graph link prediction by integrating a T5-small encoder-decoder with a Relational Graph Attention Network (RGAT). Unlike previous Seq2Seq models that only process textual descriptions or flattened entity neighborhoods, GA-S2S jointly encodes both textual features and the complete k-hop subgraph topology around a query entity. This integration allows the model to capture richer multi-hop relational patterns and textual information by combining raw encoder outputs with RGAT's relation-aware embeddings. Preliminary experiments on the CoDEx dataset show that GA-S2S achieves up to a 19% relative gain in link prediction accuracy compared to competitive Seq2Seq-based baselines.

Key takeaway

For research scientists developing knowledge graph applications, GA-S2S offers a robust approach to improve link prediction accuracy. By explicitly incorporating graph topology alongside textual data, your models can achieve up to a 19% relative gain, suggesting a critical shift from purely text-based or flattened graph representations. Consider adopting this hybrid architecture to enhance the precision of your knowledge graph inferences.

Key insights

Integrating graph structure with Seq2Seq models significantly improves knowledge graph link prediction.

Principles

• Jointly encode textual and graph topology.
• Leverage multi-hop relational patterns.

Method

GA-S2S integrates a T5-small encoder-decoder with an RGAT to jointly encode textual features and k-hop subgraph topology, combining raw encoder outputs with RGAT embeddings.

In practice

• Apply GA-S2S for enhanced link prediction.
• Utilize RGAT for relation-aware embeddings.

Topics

• Graph-Augmented Sequence-to-Sequence
• Knowledge Graph Link Prediction
• Relational Graph Attention Network
• Seq2Seq Models
• T5-small

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computation and Language.