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The number of Knowledge Graphs (KGs) generated with automatic and manualapproaches is constantly growing. For an integrated view and usage, analignment between these KGs is necessary on the schema as well as instancelevel. While there are approaches that try to tackle this multi sourceknowledge graph matching problem, large gold standards are missing to evaluatetheir effectiveness and scalability. We close this gap by presenting Gollum --a gold standard for large-scale multi source knowledge graph matching with over275,000 correspondences between 4,149 different KGs. They originate fromknowledge graphs derived by applying the DBpedia extraction framework to alarge wiki farm. Three variations of the gold standard are made available: (1)a version with all correspondences for evaluating unsupervised matchingapproaches, and two versions for evaluating supervised matching: (2) one whereeach KG is contained both in the train and test set, and (3) one where each KGis exclusively contained in the train or the test set.

Gollum: A Gold Standard for Large Scale Multi Source Knowledge Graph Matching