April 3, 2024, 4:41 a.m. | Zhongni Hou, Xiaolong Jin, Zixuan Li, Long Bai, Jiafeng Guo, Xueqi Cheng

cs.LG updates on arXiv.org arxiv.org

arXiv:2404.01695v1 Announce Type: new
Abstract: Temporal Knowledge Graph (TKG), which characterizes temporally evolving facts in the form of (subject, relation, object, timestamp), has attracted much attention recently. TKG reasoning aims to predict future facts based on given historical ones. However, existing TKG reasoning models are unable to abstain from predictions they are uncertain, which will inevitably bring risks in real-world applications. Thus, in this paper, we propose an abstention mechanism for TKG reasoning, which helps the existing models make selective, …

abstract arxiv attention cs.lg facts form future graph however knowledge knowledge graph object predictions reasoning temporal type uncertain will

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