DOI: 10.5281/zenodo.15834519
title: Semantically Enriched Link Prediction Datasets DB100k+, Yago3-10+ and NELL-995+

description: 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+, NELL995+ and NELL995+_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)

instype_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+)

class2id2ent2id 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+)

r2id2dom2id linking predicates ids to their related domain class id

r2id2range2id linking predicates ids to their related range class id

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 . NELL995+ 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
149678


Test
16,437
3,426
24,909
5,228
50,000


Valid
16,517
3,527
24,827
5,129
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
109,800
0
0
39,878
149,678


Test
3,992
0
0
0
3,992


Valid
543
0
0
0
543


Total
114,335
0
0
39,878
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