MQuadE: a Unified Model for Knowledge Fact Embedding
Jingxin Yu, Yunfeng Cai, Mingming Sun, Ping Li · 2021
The task of knowledge graph embedding (KGE) tries to find appropriate representations for entities and relations and appropriate mathematical computations between the representations to approximate the symbolic and logical relationships between entities. One major challenge for KGE is that the relations in real-world knowledge bases exhibit complex behaviors: they can be injective (1-1) or non-injective (1-N, N-1, or N-N), symmetry or skew-symmetry; one relation may be the inversion of another relation; one relation may be the composition of other two relations (where the composition can be either Abelian or non-Abelian). To our knowledge, there has not been any theoretical guarantee that these complex behaviors can be modeled by existing KGE methods.