DOI: 10.5281/zenodo.7217654
title: InductiveQE Datasets

description: <strong>InductiveQE datasets</strong> UPD 2.0: Regenerated datasets free of potential test set leakages UPD 1.1: Added train_answers_val.pkl files to all freebase-derived datasets - answers of training queries on larger validation graphs This repository contains 10 inductive complex query answering datasets published in "Inductive Logical Query Answering in Knowledge Graphs" (NeurIPS 2022). 9 datasets (106-550) were created from FB15k-237, the wikikg dataset was created from OGB WikiKG 2 graph. In the datasets, all inference graphs extend training graphs and include new nodes and edges. Dataset numbers indicate a relative size of the inference graph compared to the training graph, e.g., in 175, the number of nodes in the inference graph is 175% compared to the number of nodes in the training graph. The higher the ratio, the more new unseen nodes appear at inference time, the more complex the task is. The Wikikg split has a fixed 133% ratio. Each dataset is a zip archive containing 17 files: train_graph.txt (pt for wikikg) - original training graph val_inference.txt (pt) - inference graph (validation split), new nodes in validation are disjoint with the test inference graph val_predict.txt (pt) - missing edges in the validation inference graph to be predicted. test_intference.txt (pt) - inference graph (test splits), new nodes in test are disjoint with the validation inference graph test_predict.txt (pt) - missing edges in the test inference graph to be predicted. train/valid/test_queries.pkl - queries of the respective split, 14 query types for fb-derived datasets, 9 types for Wikikg (EPFO-only) *_answers_easy.pkl - easy answers to respective queries that do not require predicting missing links but only edge traversal *_answers_hard.pkl - hard answers to respective queries that DO require predicting missing links and against which the final metrics will be computed train_answers_val.pkl - the extended set of answers for training queries on the bigger validation graph, most of training queries have at least 1 more new answers. This is supposed to be an inference-only dataset to measure faithfulness of trained models train_answers_test.pkl - the extended set of answers for training queries on the bigger test graph, most of training queries have at least 1 more new answers. This is supposed to be an inference-only dataset to measure faithfulness of trained models og_mappings.pkl - contains entity2id / relation2id dictionaries mapping local node/relation IDs from a respective dataset to the original fb15k237 / wikikg2 stats.txt - a small file with dataset stats Overall unzipped size of all datasets combined is about 10 GB. Please refer to the paper for the sizes of graphs and the number of queries per graph. The Wikikg dataset is supposed to be evaluated in the inference-only regime being pre-trained solely on simple link prediction, the number of training complex queries is not enough for such a large dataset. Paper pre-print: https://arxiv.org/abs/2210.08008 The full source code of training/inference models is available at https://github.com/DeepGraphLearning/InductiveQE
description: {"references": ["Galkin, Mikhail et al. Inductive Logical Query Answering in Knowledge Graphs. NeurIPS 2022"]}