Meta-Learning Based Knowledge Extrapolation for Knowledge Graphs in the Federated Setting. (arXiv:2205.04692v1 [cs.CL])

We study the knowledge extrapolation problem to embed new components (i.e.,
entities and relations) that come with emerging knowledge graphs (KGs) in the
federated setting. In this problem, a model trained on an existing KG needs to
embed an emerging KG with unseen entities and relations. To solve this problem,
we introduce the meta-learning setting, where a set of tasks are sampled on the
existing KG to mimic the link prediction task on the emerging KG. Based on
sampled tasks, …

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