  Knowledge graph completion (KGC) aims to reason over known facts and infer
the missing links. Text-based methods such as KGBERT (Yao et al., 2019) learn
entity representations from natural language descriptions, and have the
potential for inductive KGC. However, the performance of text-based methods
still largely lag behind graph embedding-based methods like TransE (Bordes et
al., 2013) and RotatE (Sun et al., 2019b). In this paper, we identify that the
key issue is efficient contrastive learning. To improve the learning
efficiency, we introduce three types of negatives: in-batch negatives,
pre-batch negatives, and self-negatives which act as a simple form of hard
negatives. Combined with InfoNCE loss, our proposed model SimKGC can
substantially outperform embedding-based methods on several benchmark datasets.
In terms of mean reciprocal rank (MRR), we advance the state-of-the-art by +19%
on WN18RR, +6.8% on the Wikidata5M transductive setting, and +22% on the
Wikidata5M inductive setting. Thorough analyses are conducted to gain insights
into each component. Our code is available at
https://github.com/intfloat/SimKGC .
