Introduction

This repository contains:

• The datasets DB100k+, Yago3-10+ and NELL-995+ which are the enrichments of the standard datasets DB100k, Yago3-10 and NELL-995 with entity types infered from class hierarchies and property domains and ranges
• The notebooks that generated these datasets
• A masking algorithm allowing to create variants of these datasets with target proportion of the predicates domain/range information
• An example of such dataset with a version of NELL-995 with 10% of triples with fully signed predicates (domain and range), 30% of triples with predicates with only the domain declared (no range), 10% of triples with predicates with only the range declared (no domain) and 50% of triples with unsigned predicates (no domain and no range).

This repository has been published under the LGPL-2.1 license

This repository is affiliated to the WIMMICS research team, check the other WIMMICS projects.

How to use

Datasets

Each of the datasets DB100k+, YAGO3-10+, NELL-995+ and NELL-995+_10_30_10 contain the following files:

• A notebook for the creation of the enriched dataset, using the URL of the original dataset for downloading it
• A notebook for dataset analysis that provides key information about the dataset
• The dataset files themselves
  • The dataset splits train2id.txt, test2id.txt and valid2id.txt
  • The dataset splits variants including the explicit modelling of inverse relations train2id_inv.txt, test2id_inv.txt and valid2id_inv.txt
  • A Sankey diagram that decomposes the dataset triples given their semantic information
  • a pickle/ folder containing different pickle dictionnaries
    • ent2id translating each entity to its related id (int)
    • rel2id translating each relation to its related id (int)
    • class2id translating each class to its related id (int)
    • inst_type linking ids of entities to their types (no inferred types included)
    • inst_type_all linking ids of entities to their types (including those that were got from subsumption axiom closure in any dataset, and domain/range in YAGO3-10+ and NELL-995+)
    • classid2entid linking ids of classes to the ids of their instances (including those that were infered from subsumption axiom closure in any dataset, and domain/range in YAGO3-10+ and NELL-995+)
    • subclassof2id linking ids of classes to their direct superclasses ids (set of ids)
    • subclassof_all2id linking ids of classes to their direct and indirect superclasses ids (set of ids)
    • rid2domid linking predicates ids to their related domain classes ids (set of ids)
    • rid2rangeid linking predicates ids to their related range classes ids (set of ids)
    • observed_tails_original_kg contains a head/relation/tail index of the dataset in the form of nested dictionaries using ids of entities and relations
    • observed_heads_original_kg contains a tail/relation/head index of the dataset in the form of nested dictionaries using ids of entities and relations
    • observed_tails_inv is an equivalent of observed_tails_original_kg that also contains explicit modelling of inverse relations
    • observed_heads_inv is an equivalent of observed_heads_original_kg that also contains explicit modelling of inverse relations

Masking script

The script has the following usage:

python /path/to/dataset-mask.py /path/to/dataset-folder dataset_name full_signed_proportion domain_only_signed_proportion range_only_signed_proportion

where:

• /path/to/dataset-folder is the path to the folder containing the datasets. If the command is launched in the repository root it's simply .
• dataset_name is the name of the folder of the dataset to mask. In the example below, it is NELL995+
• full_signed_proportion is an int that is the desired percentage of triples with predicates having known domain and range (in train, test and valid splits)
• domain_only_signed_proportion is an int that is the desired percentage of triples with predicates having known domain but no range (in train, test and valid splits)
• range_only_signed_proportion is an int that is the desired percentage of triples with predicates having known range but no domain (in train, test and valid splits)

The resulting dataset is saved in the dataset folder, in a proper subfolder.

For example, launching the following command in the repository root:

python dataset-mask.py . NELL-995+ 10 30 10

generates the dataset that is in folder NELL995+_10_30_10

Key stats

DB100k+

Split	# Fully signed triples	# Domain-only triples	# Range-only triples	# Unsigned triples	Total
Train	196,877	41,267	297,209	62,219	597,572
Test	16,517	3,527	24,827	5,129	50,000
Valid	16,437	3,426	24,909	5,228	50,000
Total	229,831	48,220	346,945	72,576	697,572

NELL-995+

Split	# Fully signed triples	# Domain-only triples	# Range-only triples	# Unsigned triples	Total
Train	103,255	6,545	25,779	14,099	149,678
Test	3,992	0	0	0	3,992
Valid	543	0	0	0	543
Total	107,790	6,545	25,779	14,099	154,213

YAGO3-10+

Split	# Fully signed triples	# Domain-only triples	# Range-only triples	# Unsigned triples	Total
Train	1,057,339	21,701	0	0	1,079,040
Test	4,886	114	0	0	5,000
Valid	4,912	88	0	0	5,000
Total	1,067,137	21,903	0	0	1,089,040