@inproceedings{feng-etal-2022-sharing,
    title = "Sharing Parameter by Conjugation for Knowledge Graph Embeddings in Complex Space",
    author = "Feng, Xincan  and
      Qu, Zhi  and
      Cheng, Yuchang  and
      Watanabe, Taro  and
      Yugami, Nobuhiro",
    booktitle = "Proceedings of TextGraphs-16: Graph-based Methods for Natural Language Processing",
    month = oct,
    year = "2022",
    address = "Gyeongju, Republic of Korea",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.textgraphs-1.3",
    pages = "25--34",
    abstract = "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 $5^{\bigstar}\mathrm{E}$ (CITATION) and $\mathrm{ComplEx}$ (CITATION) on five benchmark datasets.",
}