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Knowledge Graph Embedding (KGE) aims to represent entities and relations ofknowledge graph in a low-dimensional continuous vector space. Recent worksfocus on incorporating structural knowledge with additional information, suchas entity descriptions, relation paths and so on. However, common usedadditional information usually contains plenty of noise, which makes it hard tolearn valuable representation. In this paper, we propose a new kind ofadditional information, called entity neighbors, which contain both semanticand topological features about given entity. We then develop a deep memorynetwork model to encode information from neighbors. Employing a gatingmechanism, representations of structure and neighbors are integrated into ajoint representation. The experimental results show that our model outperformsexisting KGE methods utilizing entity descriptions and achievesstate-of-the-art metrics on 4 datasets.

Knowledge Graph Embedding with Entity Neighbors and Deep Memory Network