MEI
Multi-partition Embedding Interaction
**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.
Multi-Partition Embedding Interaction with Block Term Format for Knowledge Graph Completion
https://paperswithcode.com/paper/multi-partition-embedding-interaction-with
Multi-Partition Embedding Interaction with Block Term Format for Knowledge Graph Completion