  The number of Knowledge Graphs (KGs) generated with automatic and manual
approaches is constantly growing. For an integrated view and usage, an
alignment between these KGs is necessary on the schema as well as instance
level. While there are approaches that try to tackle this multi source
knowledge graph matching problem, large gold standards are missing to evaluate
their effectiveness and scalability. We close this gap by presenting Gollum --
a gold standard for large-scale multi source knowledge graph matching with over
275,000 correspondences between 4,149 different KGs. They originate from
knowledge graphs derived by applying the DBpedia extraction framework to a
large wiki farm. Three variations of the gold standard are made available: (1)
a version with all correspondences for evaluating unsupervised matching
approaches, and two versions for evaluating supervised matching: (2) one where
each KG is contained both in the train and test set, and (3) one where each KG
is exclusively contained in the train or the test set.
