Wikidata Thematic Subgraph Selection These datasets have been designed to train and evaluate algorithms to select thematic subgraphs of interest in a large knowledge graph from seed entities of interest. Specifically, we consider Wikidata. Given a set of seed QIDs of interest, a graph expansion is performed following P31, P279, and (-)P279 edges. Traversed classes that thematically deviates from seed QIDs of interest should be pruned. Datasets thus consist of classes reached from seed QIDs that are labeled as "to prune" or "to keep". Available datasets Dataset # Seed QIDs # Labeled decisions # Prune decisions Min prune depth Max prune depth # Keep decisions Min keep depth Max keep depth # Reached nodes up # Reached nodes down dataset1 455 5233 3464 1 4 1769 1 4 1507 2593609 dataset2 105 982 388 1 2 594 1 3 1159 1247385 Each dataset folder contains datasetX.csv: a CSV file containing one seed QID per line (not the complete URL, just the QID). This CSV file has no header. datasetX_labels.csv: a CSV file containing one seed QID per line and its label (not the complete URL, just the QID) datasetX_gold_decisions.csv: a CSV file with seed QIDs, reached QIDs, and the labeled decision (1: keep, 0: prune) datasetX_Y_folds.pkl: folds to train and test models based on the labeled decisions dataset1-2 consists of using dataset1 for training and dataset2 for testing. License Datasets are available under the CC BY-NC license.