研究人员推出FITTER，这是一种新颖的归纳式模型，专为时序知识图谱链接预测而设计。与仅限于单个图谱词汇的先前方法不同，FITTER能够跨不同领域以及未见的实体、关系名称和时间戳转移知识。该模型通过交互模式表示谓词并使用相对时间编码来实现词汇无关的嵌入。在六个基准上的评估表明，FITTER在无需重新训练的情况下持续优于归纳式基线，这表明它有可能在语义网的各种知识图谱中进行信息推理。 AI
影响 这项研究可能会促进信息在各种不断演变的知识图谱之间进行推理和转移的方式。
排序理由 该集群描述了一篇关于时序知识图谱链接预测新颖模型的新研究论文。
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arXiv:2608.10668v1 Announce Type: new Abstract: Temporal knowledge graphs are central to many uses of the Semantic Web, but existing completion methods assume the entities, relation names, and timestamps to be reasoned about are already known at training time, restricting each mo…
Temporal knowledge graphs are central to many uses of the Semantic Web, but existing completion methods assume the entities, relation names, and timestamps to be reasoned about are already known at training time, restricting each model to a single graph and vocabulary. We propose…