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Simple and Effective Relation-based Embedding Propagation for Knowledge Representation Learning. (arXiv:2205.06456v1 [cs.CL])
May 16, 2022, 1:10 a.m. | Huijuan Wang, Siming Dai, Weiyue Su, Hui Zhong, Zeyang Fang, Zhengjie Huang, Shikun Feng, Zeyu Chen, Yu Sun, Dianhai Yu
cs.CL updates on arXiv.org arxiv.org
Relational graph neural networks have garnered particular attention to encode
graph context in knowledge graphs (KGs). Although they achieved competitive
performance on small KGs, how to efficiently and effectively utilize graph
context for large KGs remains an open problem. To this end, we propose the
Relation-based Embedding Propagation (REP) method. It is a post-processing
technique to adapt pre-trained KG embeddings with graph context. As relations
in KGs are directional, we model the incoming head context and the outgoing
tail context …
arxiv embedding knowledge learning representation representation learning
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