Core Concepts
TransERR introduces a translation-based knowledge graph embedding method using efficient relation rotation in the hypercomplex-valued space, enhancing translation freedom for graph embeddings.
Abstract
Knowledge graphs represent real-world entities and their associations.
TransERR encodes knowledge graphs in hypercomplex-valued space for better translation freedom.
Mathematical proofs demonstrate TransERR's ability to model various relation patterns effectively.
Experimental results validate TransERR's effectiveness and generalization on benchmark datasets.
TransERR outperforms existing models with fewer parameters.
TransERR can model complex relations and key patterns simultaneously.
Quaternions in TransERR enable smooth rotation and spatial translation.
Stats
TransERR는 지식 그래프 임베딩을 위해 하이퍼복소값 공간에서 효율적인 관계 회전을 사용합니다.
TransERR는 대규모 데이터셋을 더 적은 매개변수로 더 잘 인코딩할 수 있음을 나타냅니다.
Quotes
"TransERR encodes knowledge graphs in the hypercomplex-valued space, enabling higher translation freedom."
"Experimental results show that TransERR outperforms existing models with fewer parameters."