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We present Harmonic Memory Networks (HMem), a neural architecture forknowledge base completion that models entities as weighted sums of pairwisebindings between an entity's neighbors and corresponding relations. Sinceentities are modeled as aggregated neighborhoods, representations of unseenentities can be generated on the fly. We demonstrate this with two newdatasets: WNGen and FBGen. Experiments show that the model is SOTA onbenchmarks, and flexible enough to evolve without retraining as the knowledgegraph grows.

Scalable knowledge base completion with superposition memories