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A significant and recent development in neural-symbolic learning are deep neural networks that can reason over symbolic knowledge graphs (KGs). A particular task of interest is KG entailment, which is to infer the set of all facts that are a logical consequence of current and potential facts of a KG. Initial neural-symbolic systems that can deduce the entailment of a KG have been presented, but they are limited: current systems learn fact relations and entailment patterns specific to a particular KG and hence do not truly generalize, and must be retrained for each KG they are tasked with entailing. We propose a neural-symbolic system to address this limitation in this paper. It is designed as a differentiable end-to-end deep memory network that learns over abstract, generic symbols to discover entailment patterns common to any reasoning task. A key component of the system is a simple but highly effective normalization process for continuous representation learning of KG entities within memory networks. Our results show how the model, trained over a set of KGs, can effectively entail facts from KGs excluded from the training, even when the vocabulary or the domain of test KGs is completely different from the training KGs.


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