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16th Workshop on Graph-Based Methods for Natural Language Processing, TextGraphs 2022, in conjunction with the 29th International Conference on Computational Linguistics, COLING 2022

作者： Feng, Xincan Qu, Zhi Cheng, Yuchang Watanabe, Taro Yugami, Nobuhiro Natural Language Processing Laboratory Nara Institute of Science and Technology Japan Multilingual Knowledge Computing Laboratory Fujitsu Ltd Japan

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*E (Nayyeri et al., 2021) and ComplEx (Trouillon et al., 2016) on five benchmark datasets. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Directed graphs

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Sharing Parameter by Conjugation for knowledge Graph Embeddings in Complex Space

arXiv

引用

arXiv 2024年

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★E (Nayyeri et al., 2021) and ComplEx (Trouillon et al., 2016) on five benchmark datasets. © 2024, CC BY.

关键词： knowledge graph

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