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

The set of Knowledge Graphs (KGs) generated with automatic and manual approaches is constantly growing.<br> For an integrated view and usage, an alignment between these KGs is necessary on the schema as well as instance level.<br> There are already approaches which try to tackle this multi source knowledge graph matching problem,<br> but large gold standards are missing to evaluate their effectiveness and scalability.<br> In particular, most existing gold standards are fairly small and can be solved by matchers which match exactly two KGs (1:1), which are the majority of existing matching systems.<br> <br> 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.<br> They originate from knowledge graphs derived by applying the DBpedia extraction framework to a large wiki farm.<br> <br> Three variations of the gold standard are made available:<br> (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. <br> We plan to extend our KG track at the Ontology Alignment Evaluation Initiative (OAEI) to allow for matching systems <br> which are specifically designed to solve the multi KG matching problem.<br> As a first step towards this direction, we evaluate multi source matching approaches which reuse two-KG (1:1) matchers from the past OAEI. Due to the size of the KG files, they are hosted at the institute: http://data.dws.informatik.uni-mannheim.de/dbkwik/gollum/40K.tar (50,3 GB)<br> http://data.dws.informatik.uni-mannheim.de/dbkwik/gollum/all.tar (74,7 GB)<br> http://data.dws.informatik.uni-mannheim.de/dbkwik/gollum/gold.tar (25,3 GB)