将知识图谱结构融入序列模型，提升链接预测准确率。
Leveraging Graph Structure in Seq2Seq Models for Knowledge Graph Link Prediction
- 结合文本与多跳图结构，联合编码实体关系
- 在CoDEx数据集上相对基线提升19%准确率
- 适合需要挖掘复杂关系的图推理任务
我们提出GA-S2S框架，将T5-small编码器-解码器与关系图注意力网络（RGAT）结合，用于知识图谱链接预测。现有序列模型仅依赖实体和关系的表面文本描述，最多将查询实体的邻域展平为线性序列，忽略了固有的图结构信息。GA-S2S则联合编码查询实体周围完整k跳子图拓扑结构与文本特征。通过融合原始编码器输出与RGAT生成的关系感知嵌入，模型能捕捉更丰富的多跳关系模式与文本信息。在CoDEx数据集上的初步实验表明，该方法优于多种基准序列模型，链接预测准确率最高提升19%。
原文摘要 · Abstract (English)
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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