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

Hongzhu Li, Xiangrui Gao, Linhui Feng, Yafeng Deng, Yuhui Yin

Keywords: Knowledge Representation Learning, Knowledge Graph Embedding, Knowledge Graph, Self-Attention Network

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.

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TL;DR: knowledge representation learning based on incomplete local subgraph