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Efficient usage of the knowledge provided by the Linked Data community isoften hindered by the need for domain experts to formulate the right SPARQLqueries to answer questions. For new questions they have to decide whichdatasets are suitable and in which terminology and modelling style to phrasethe SPARQL query.  In this work we present an evolutionary algorithm to help with thischallenging task. Given a training list of source-target node-pair examples ouralgorithm can learn patterns (SPARQL queries) from a SPARQL endpoint. Thelearned 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 humanassociations (such as "circle - square") to find patterns for them in DBpedia.We show the scalability of the algorithm by running it against a SPARQLendpoint loaded with > 7.9 billion triples. Further, we use the resultingSPARQL queries to mimic human associations with a Mean Average Precision (MAP)of 39.9 % and a Recall@10 of 63.9 %.

An Evolutionary Algorithm to Learn SPARQL Queries for Source-Target-Pairs: Finding Patterns for Human Associations in DBpedia