  Efficient usage of the knowledge provided by the Linked Data community is
often hindered by the need for domain experts to formulate the right SPARQL
queries to answer questions. For new questions they have to decide which
datasets are suitable and in which terminology and modelling style to phrase
the SPARQL query.
  In this work we present an evolutionary algorithm to help with this
challenging task. Given a training list of source-target node-pair examples our
algorithm can learn patterns (SPARQL queries) from a SPARQL endpoint. The
learned patterns can be visualised to form the basis for further investigation,
or they can be used to predict target nodes for new source nodes.
  Amongst others, we apply our algorithm to a dataset of several hundred human
associations (such as "circle - square") to find patterns for them in DBpedia.
We show the scalability of the algorithm by running it against a SPARQL
endpoint loaded with > 7.9 billion triples. Further, we use the resulting
SPARQL queries to mimic human associations with a Mean Average Precision (MAP)
of 39.9 % and a Recall@10 of 63.9 %.
