Random Entity Quantization for Parameter-Efficient Compositional Knowledge Graph Representation
This work addresses parameter efficiency for knowledge graph representation, but it is incremental as it simplifies existing methods without introducing a new paradigm.
The paper tackles the scalability challenge in Knowledge Graph Embedding (KGE) by showing that random entity quantization achieves similar parameter efficiency to complex strategies, with analysis revealing higher entropy and Jaccard distance that improve entity distinguishability.
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.