Sharing Parameter by Conjugation for Knowledge Graph Embeddings in Complex Space

arXiv:2404.11809v1 Announce Type: cross
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 …

abstract arxiv cs.cl cs.lg diverse embeddings graph knowledge knowledge graph language language processing natural natural language natural language processing nlp processing relations representation scale space tasks type world