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The Natural Language Processing (NLP) community has focused on machine learning and data-driven approaches to linguistic problems for several decades now, leading to the recent proposal of implicit, latent models and representations obtained from large amounts of training data [9, 17]. In contrast, the Semantic Technology community has primarily taken a symbolic approach resulting in the production of explicit, human-readable knowledge representations [7, 19]. However, both communities share core goals of Artificial Intelligence such as making applications more intelligent and interactive, and, in the longer term, enabling machine understanding. Knowledge Graphs provide a key contribution to bridging the gap between the two areas and join forces to achieve their common mission.