Before revision:  We evaluate our method by answering queries that rely on edges not seen during training, obtaining competitive performance . In contrast with previous work, we show that our method can generalize from training for the single-hop, link prediction task, to answering queries with more complex structures. A qualitative analysis reveals that the learned embeddings successfully capture the notion of different entity types .
After revision:  Our method shows competitive performance against previous models for complex queries, and in contrast with these models, it can answer complex queries when trained for link prediction only. We show that the model learns entity embeddings that capture the notion of different entity types .