MEI

2000년 도입 · 논문 14편에서 사용

MEI introduces the *multi-partition embedding interaction* technique with block term tensor format to systematically address the efficiency--expressiveness trade-off in knowledge graph embedding. It divides the embedding vector into multiple partitions and learns the local interaction patterns from data instead of using fixed special patterns as in ComplEx or SimplE models. This enables MEI to achieve optimal efficiency--expressiveness trade-off, not just being fully expressive. Previous methods such as TuckER, RESCAL, DistMult, ComplEx, and SimplE are suboptimal restricted special cases of MEI.

출처: Multi-Partition Embedding Interaction with Block Term Format for Knowledge Graph Completion

소개 논문: Multi-Partition Embedding Interaction with Block Term Format for Knowledge Graph Completion