We propose neural encoders and decoders to replace initial layers of any embeddingbased relational model; we apply them to Dist-Mult  and ConvE (Dettmers et al., 2018) here.
instead of learning a distinct vector for each entity and using enumeration to predict links, MKBE includes the following extensions: (1) introduce additional neural encoders to embed multimodal evidence types that the relational model uses to predict links, and (2) introduce neural decoders that use an entity's embedding to generate its multimodal attributes (like image and text).