Wikidata Thematic Subgraph Selection

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.