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Relational knowledge bases (KBs) are commonly used to represent worldknowledge in machines. However, while advantageous for their high degree ofprecision and interpretability, KBs are usually organized according tomanually-defined schemas, which limit their expressiveness and requiresignificant human efforts to engineer and maintain. In this review, we take anatural language processing perspective to these limitations, examining howthey may be addressed in part by training deep contextual language models (LMs)to internalize and express relational knowledge in more flexible forms. Wepropose to organize knowledge representation strategies in LMs by the level ofKB supervision provided, from no KB supervision at all to entity- andrelation-level supervision. Our contributions are threefold: (1) We provide ahigh-level, extensible taxonomy for knowledge representation in LMs; (2) Withinour taxonomy, we highlight notable models, evaluation tasks, and findings, inorder to provide an up-to-date review of current knowledge representationcapabilities in LMs; and (3) We suggest future research directions that buildupon the complementary aspects of LMs and KBs as knowledge representations.

Relational World Knowledge Representation in Contextual Language Models: A Review