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A Hierarchical N-Gram Framework for Zero-Shot Link Prediction. (arXiv:2204.10293v3 [cs.CL] UPDATED)
Oct. 10, 2022, 1:16 a.m. | Mingchen Li, Junfan Chen, Samuel Mensah, Nikolaos Aletras, Xiulong Yang, Yang Ye
cs.CL updates on arXiv.org arxiv.org
Due to the incompleteness of knowledge graphs (KGs), zero-shot link
prediction (ZSLP) which aims to predict unobserved relations in KGs has
attracted recent interest from researchers. A common solution is to use textual
features of relations (e.g., surface name or textual descriptions) as auxiliary
information to bridge the gap between seen and unseen relations. Current
approaches learn an embedding for each word token in the text. These methods
lack robustness as they suffer from the out-of-vocabulary (OOV) problem.
Meanwhile, models …
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