  Predicting missing links between entities in a knowledge graph is a
fundamental task to deal with the incompleteness of data on the Web. Knowledge
graph embeddings map nodes into a vector space to predict new links, scoring
them according to geometric criteria. Relations in the graph may follow
patterns that can be learned, e.g., some relations might be symmetric and
others might be hierarchical. However, the learning capability of different
embedding models varies for each pattern and, so far, no single model can learn
all patterns equally well. In this paper, we combine the query representations
from several models in a unified one to incorporate patterns that are
independently captured by each model. Our combination uses attention to select
the most suitable model to answer each query. The models are also mapped onto a
non-Euclidean manifold, the Poincar\'e ball, to capture structural patterns,
such as hierarchies, besides relational patterns, such as symmetry. We prove
that our combination provides a higher expressiveness and inference power than
each model on its own. As a result, the combined model can learn relational and
structural patterns. We conduct extensive experimental analysis with various
link prediction benchmarks showing that the combined model outperforms
individual models, including state-of-the-art approaches.
