Random Entity Quantization for Parameter-Efficient Compositional Knowledge Graph Representation | VIDEO
Published in Underline Science Inc. • Nov 19, 2023
Authors:,,
Association for Computational Linguistics 2023
Li, Jiaang
Liu, Yi
Abstract
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 entit...
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