发表机构
University of Stuttgart; SAP SE; University of Southampton（斯图加特大学; 思爱普公司; 南安普顿大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
FITTER是首个支持跨域迁移的全归纳时序知识图谱链接预测模型，通过相对顺序编码与消息传递生成词汇无关嵌入，在6类基准上无需重训便优于归纳基线，为语义网异构知识图谱推理提供可行基础。
AI 中文摘要
时序知识图谱是语义网诸多应用的核心，但现有补全方法假设待推理的实体、关系名称和时间戳在训练时已为已知，将每个模型限制在单一图谱和词汇表内。我们提出FITTER，首个支持跨域迁移的全归纳时序知识图谱链接预测结构模型：推理图可包含来自不同域的全新实体、关系名称和时间戳。FITTER通过相对而非绝对顺序的编码，以与其他谓词及时间的交互模式表征每个谓词；消息传递融合局部与全局时序上下文，生成词汇无关的嵌入。我们证明其时序编码具有时移不变性，并在6个不同域、粒度和时长的时序知识图谱基准上评估FITTER的跨域、跨图迁移性能，结果显示FITTER无需重新训练便始终优于归纳基线，表明词汇无关的结构学习是语义网异构知识图谱推理的可行基础。
英文摘要
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 FITTER, the first fully-inductive structural model for temporal knowledge graph link prediction that supports cross-domain transfer: the inference graph may contain entirely unseen entities, relation names, and timestamps drawn from a different domain. FITTER represents each predicate by its interaction patterns with others and time through encodings of relative rather than absolute ordering; message-passing fuses local and global temporal context to produce vocabulary-agnostic embeddings. We prove the temporal encoding is time-shift invariant and evaluate FITTER on cross-domain, cross-graph transfer over six temporal knowledge graph benchmarks of diverse domains, granularities, and time spans. FITTER consistently outperforms inductive baselines without retraining, indicating that vocabulary-agnostic structural learning is a viable foundation for inference over the heterogeneous knowledge graphs of the Semantic Web.
CommentsAccepted at ISWC 2026
