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StarGraph: Knowledge Representation Learning based on Incomplete Two-hop Subgraph

2023-01-03 16:51:29
Hongzhu Li, Xiangrui Gao, Linhui Feng, Yafeng Deng, Yuhui Yin

Abstract

Conventional representation learning algorithms for knowledge graphs (KG) map each entity to a unique embedding vector, ignoring the rich information contained in the neighborhood. We propose a method named StarGraph, which gives a novel way to utilize the neighborhood information for large-scale knowledge graphs to obtain entity representations. An incomplete two-hop neighborhood subgraph for each target node is at first generated, then processed by a modified self-attention network to obtain the entity representation, which is used to replace the entity embedding in conventional methods. We achieved SOTA performance on ogbl-wikikg2 and got competitive results on fb15k-237. The experimental results proves that StarGraph is efficient in parameters, and the improvement made on ogbl-wikikg2 demonstrates its great effectiveness of representation learning on large-scale knowledge graphs. The code is now available at \url{this https URL}.

Abstract (translated)

URL

https://arxiv.org/abs/2205.14209

PDF

https://arxiv.org/pdf/2205.14209.pdf


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