  We present Harmonic Memory Networks (HMem), a neural architecture for
knowledge base completion that models entities as weighted sums of pairwise
bindings between an entity's neighbors and corresponding relations. Since
entities are modeled as aggregated neighborhoods, representations of unseen
entities can be generated on the fly. We demonstrate this with two new
datasets: WNGen and FBGen. Experiments show that the model is SOTA on
benchmarks, and flexible enough to evolve without retraining as the knowledge
graph grows.
