SpaceE: Knowledge Graph Embedding by Relational Linear Transformation in the Entity Space
Translation distance based knowledge graph embedding (KGE) methods, such as _TransE_ and _RotatE_, model the relation in knowledge graphs as translation or rotation in the vector space. Both translation and rotation are injective; that is, the translation or rotation of different vectors results in different results. In knowledge graphs, different entities may have a relation with the same entity; for example, many actors starred in one movie. Such a non-injective relation pattern cannot be well modeled by the translation or rotation operations in existing translation distance based KGE methods. To tackle the challenge, we propose a translation distance-based KGE method called **SpaceE** to model relations as linear transformations. The proposed SpaceE embeds both entities and relations in knowledge graphs as matrices and SpaceE naturally models non-injective relations with singular linePowered by Seed HypermediaOpen App