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Neural models of Knowledge Base data have typically employed compositionalrepresentations of graph objects: entity and relation embeddings aresystematically combined to evaluate the truth of a candidate Knowedge Baseentry. Using a model inspired by Harmonic Grammar, we propose to tokenizetriplet embeddings by subjecting them to a process of optimization with respectto learned well-formedness conditions on Knowledge Base triplets. The resultingmodel, known as Gradient Graphs, leads to sizable improvements when implementedas a companion to compositional models. Also, we show that the"supracompositional" triplet token embeddings it produces have interpretableproperties that prove helpful in performing inference on the resulting tripletrepresentations.

Augmenting Compositional Models for Knowledge Base Completion Using Gradient Representations