具体化作为可迁移词汇：使用普通GNN的零样本链接预测

中文摘要 AI 辅助

知识图谱基础模型（如ULTRA）通过专门设计、硬编码迁移机制的架构，在未见过的图上实现零样本链接预测。在这项工作中，我们将该机制从架构中移出并放入表示中，通过将输入图具体化：每个事实成为一个节点，通过六个元关系的固定词汇连接到其主语、宾语和关系类型，关系类型作为匿名共享节点而非模型参数。在这种表示上，五种教科书式GNN（GAT、使用求和及均值+最大聚合的GINE、GraphSAGE、R-GCN），每种在单个包含4,245个三元组的知识图谱上于一块NVIDIA A100上训练30分钟，即可零样本迁移到40个归纳式链接预测基准。其中最佳模型——一个现成的GAT，在ULTRA自身的评估套件上匹配了在三个图谱上预训练的专用基础模型ULTRA。相同的固定词汇扩展到关系数据库，行成为实体，外键列成为关系类型；在两个未见过的数据库上的初步探针（无单元格值、无模式文本、无上下文标签）显示，该系列模型在三个知识图谱上预训练后，对外键目标的排序远高于随机初始化和度控制。我们发布了所有40个基准的代码、检查点和评估流程。

英文摘要

Knowledge graph foundation models such as ULTRA achieve zero-shot link prediction on unseen graphs through dedicated architectures that hard-code a transfer mechanism. In this work we move that mechanism out of the architecture and into the representation, by \emph{reifying} the input graph: every fact becomes a node, connected to its subject, object, and relation type through a fixed vocabulary of six meta-relations, with relation types as anonymous shared nodes rather than model parameters. On this representation, five textbook GNNs (GAT, GINE with sum and with mean+max aggregation, GraphSAGE, R-GCN), each trained on a single knowledge graph of 4,245 triples for 30 minutes on one NVIDIA A100, transfer zero-shot to 40 inductive link-prediction benchmarks. The best of them, an off-the-shelf GAT, matches ULTRA, a dedicated foundation model pretrained on three graphs, across ULTRA's own evaluation suite. The same fixed vocabulary extends to relational databases, a row becoming an entity and a foreign-key column a relation type; a preliminary probe on two unseen databases, with no cell values, schema text or in-context labels, shows a model of this family pretrained on three knowledge graphs ranking foreign-key targets far above random-initialization and degree controls. We release the code, the checkpoints, and the evaluation pipeline for all 40 benchmarks.

发表机构

• Matr

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