  Knowledge graphs are freely aggregated, published, and edited in the Web of
data, and thus may overlap. Hence, a key task resides in aligning (or matching)
their content. This task encompasses the identification, within an aggregated
knowledge graph, of nodes that are equivalent, more specific, or weakly
related. In this article, we propose to match nodes within a knowledge graph by
(i) learning node embeddings with Graph Convolutional Networks such that
similar nodes have low distances in the embedding space, and (ii) clustering
nodes based on their embeddings, in order to suggest alignment relations
between nodes of a same cluster. We conducted experiments with this approach on
the real world application of aligning knowledge in the field of
pharmacogenomics, which motivated our study. We particularly investigated the
interplay between domain knowledge and GCN models with the two following
focuses. First, we applied inference rules associated with domain knowledge,
independently or combined, before learning node embeddings, and we measured the
improvements in matching results. Second, while our GCN model is agnostic to
the exact alignment relations (e.g., equivalence, weak similarity), we observed
that distances in the embedding space are coherent with the ``strength'' of
these different relations (e.g., smaller distances for equivalences), letting
us considering clustering and distances in the embedding space as a means to
suggest alignment relations in our case study.
