  A Knowledge Graph (KG) is the directed graphical representation of entities
and relations in the real world. KG can be applied in diverse Natural Language
Processing (NLP) tasks where knowledge is required. The need to scale up and
complete KG automatically yields Knowledge Graph Embedding (KGE), a shallow
machine learning model that is suffering from memory and training time
consumption issues. To mitigate the computational load, we propose a
parameter-sharing method, i.e., using conjugate parameters for complex numbers
employed in KGE models. Our method improves memory efficiency by 2x in relation
embedding while achieving comparable performance to the state-of-the-art
non-conjugate models, with faster, or at least comparable, training time. We
demonstrated the generalizability of our method on two best-performing KGE
models $5^{\bigstar}\mathrm{E}$ and $\mathrm{ComplEx}$ on five benchmark
datasets.
