  We present an end-to-end approach that takes unstructured textual input and
generates structured output compliant with a given vocabulary. Inspired by
recent successes in neural machine translation, we treat the triples within a
given knowledge graph as an independent graph language and propose an
encoder-decoder framework with an attention mechanism that leverages knowledge
graph embeddings. Our model learns the mapping from natural language text to
triple representation in the form of subject-predicate-object using the
selected knowledge graph vocabulary. Experiments on three different data sets
show that we achieve competitive F1-Measures over the baselines using our
simple yet effective approach. A demo video is included.
