  Representation Learning on Knowledge Graphs (KGs) is essential for downstream
tasks. The dominant approach, KG Embedding (KGE), represents entities with
independent vectors and faces the scalability challenge. Recent studies propose
an alternative way for parameter efficiency, which represents entities by
composing entity-corresponding codewords matched from predefined small-scale
codebooks. We refer to the process of obtaining corresponding codewords of each
entity as entity quantization, for which previous works have designed
complicated strategies. Surprisingly, this paper shows that simple random
entity quantization can achieve similar results to current strategies. We
analyze this phenomenon and reveal that entity codes, the quantization outcomes
for expressing entities, have higher entropy at the code level and Jaccard
distance at the codeword level under random entity quantization. Therefore,
different entities become more easily distinguished, facilitating effective KG
representation. The above results show that current quantization strategies are
not critical for KG representation, and there is still room for improvement in
entity distinguishability beyond current strategies. The code to reproduce our
results is available at https://github.com/JiaangL/RandomQuantization.
