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datasets-knowledge-embedding

Project license

πŸ“ A collection of common datasets used in knowledge embedding

Synopsis

This project collects different datasets used in various knowledge embedding related papers. It also standardizes the format of these datasets, making it easier to use them in the evaluation of new works.

The datasets can be downloaded from the release page.
For licensing information, please refer to the original dataset license file.

If you are using this collection of datasets please consider to start ⭐️ the project to support it.

Datasets format

Every subfolder in this repo is a single dataset.
Every folder contains the following 18 files.

File name Description
edges_as_text_{train,valid,test}.tsv These three files contain the three splits of the dataset where entities and relations are in a textual form (i.e. italy locatedin europe).
edges_as_text_all.tsv The concatenation of edges_as_text_train.tsv, edges_as_text_valid.tsv, and edges_as_text_test.tsv.
edges_as_id_{train,valid,test}.tsv These three files contain the three splits of the dataset where entities and relations are mapped to a numerical ID (i.e. 38 1 2). Entities and relations that are more frequent are mapped to lower integers (e.g. the entity/relation with ID 0 is the most frequent entity/relation in the dataset).
edges_as_id_all.tsv The concatenation of edges_as_id_train.tsv, edges_as_id_valid.tsv, and edges_as_id_test.tsv.
map_entity_id_to_text.tsv This file contains the mapping from numerical IDs used for entities in edges_as_id_*.tsv to the textual representation used in edges_as_text_*.tsv (i.e. 38 italy, 2 europe).
map_relation_id_to_text.tsv This file contains the mapping from numerical IDs used for relations in edges_as_id_*.tsv to the textual representation used in edges_as_text_*.tsv (i.e 1 locatedin).
frequency_entities_{all,train,valid,test}.tsv These files contain the frequency of each entity in the various splits of the dataset.
frequency_relations_{all,train,valid,test}.tsv These files contain the frequency of each relation in the various splits of the dataset.

Add a new dataset

If you want to add a new dataset to this collection, first you need to create three files called train.tsv, valid.tsv, and test.tsv containing respectively the edges for the three splits train, validation and test.
The files must contain tab-separated triples of the form (head entity, relation, tail entity).

Once you did this, you can simply process the three files with the following bash script.

bash build.sh train.tsv valid.tsv test.tsv .

The script uses the edgelist-mapper tool under the hood.

Datasets

The datasets are distributed in two formats, namely text-based and id-based (see the dataset format section for the difference).

COUNTRIES-S1

This dataset was introduced in On Approximate Reasoning Capabilities of Low-Rank Vector Spaces.
The link to the original dataset as released by the authors is unknown but a copy has been taken from here.

Entities Relation Types Edges Train Edges Validation Edges Test Edges
271 2 1159 1111 24 24

Download COUNTRIES-S1.tgz Download COUNTRIES-S1-ID.tgz

COUNTRIES-S2

This dataset was introduced in On Approximate Reasoning Capabilities of Low-Rank Vector Spaces.
The link to the original dataset as released by the authors is unknown but a copy has been taken from here.

Entities Relation Types Edges Train Edges Validation Edges Test Edges
271 2 1111 1063 24 24

Download COUNTRIES-S2.tgz Download COUNTRIES-S2-ID.tgz

COUNTRIES-S3

This dataset was introduced in On Approximate Reasoning Capabilities of Low-Rank Vector Spaces.
The link to the original dataset as released by the authors is unknown but a copy has been taken from here.

Entities Relation Types Edges Train Edges Validation Edges Test Edges
271 2 1033 985 24 24

Download COUNTRIES-S3.tgz Download COUNTRIES-S3-ID.tgz

FB15K

This dataset was introduced in Translating Embeddings for Modeling Multi-relational Data.
The original dataset as released by the authors is available here.

Entities in this dataset are represented trough the Freebase ids (i.e. /m/07l450, /film/film/genre, /m/082gq). Since they are hard to read we are considering to map them to Wikipedia pages (i.e. The_Last_King_of_scotland_(film), /film/film/genre, War_film).

Entities Relation Types Edges Train Edges Validation Edges Test Edges
14951 1345 592213 483142 50000 59071

Download FB15K.tgz Download FB15K-ID.tgz

FB15K-237

This dataset was introduced in Observed versus latent features for knowledge base and text inference.
The original dataset as released by the authors is available here.

Entities in this dataset are represented trough the Freebase ids (i.e. /m/07l450, /film/film/genre, /m/082gq). Since they are hard to read we are considering to map them to Wikipedia pages (i.e. The_Last_King_of_scotland_(film), /film/film/genre, War_film).

Entities Relation Types Edges Train Edges Validation Edges Test Edges
14541 237 310116 272115 17535 20466

Download FB15K-237.tgz Download FB15K-237-ID.tgz

KINSHIP

This dataset was introduced in Learning systems of concepts with an infinite relational model.
The original dataset as released by the authors is available here.

Entities Relation Types Edges Train Edges Validation Edges Test Edges
104 25 10686 8544 1068 1074

Download KINSHIP.tgz Download KINSHIP-ID.tgz

NATIONS

This dataset was introduced in Learning systems of concepts with an infinite relational model.
The original dataset as released by the authors is available here.

Entities Relation Types Edges Train Edges Validation Edges Test Edges
14 55 1992 1592 199 201

Download NATIONS.tgz Download NATIONS-ID.tgz

UMLS

This dataset was introduced in Learning systems of concepts with an infinite relational model.
The original dataset as released by the authors is available here.

Entities Relation Types Edges Train Edges Validation Edges Test Edges
135 46 6529 5216 652 661

Download UMLS.tgz Download UMLS-ID.tgz

WN18

This dataset was introduced in Translating Embeddings for Modeling Multi-relational Data.
The original dataset as released by the authors is available here.

In the original dataset, the entities are represented trough the WordNet offset id (i.e. 01257145 derivationally_related_form 07488875), but the version distributed here has the offsets mapped to WordNet synsets that can be read by the nltk library (i.e. sensual.s.02 derivationally_related_form sensuality.n.01).

Entities Relation Types Edges Train Edges Validation Edges Test Edges
41105 18 151442 141442 5000 5000

Download WN18.tgz Download WN18-ID.tgz

WN18RR

This dataset was introduced in Convolutional 2D Knowledge Graph Embeddings.
The original dataset as released by the authors is available here.

In the original dataset, the entities are represented trough the WordNet offset id (i.e. 01257145 derivationally_related_form 07488875), but the version distributed here has the offsets mapped to WordNet synsets that can be read by the nltk library (i.e. sensual.s.02 derivationally_related_form sensuality.n.01).

Entities Relation Types Edges Train Edges Validation Edges Test Edges
41105 11 93003 86835 3034 3134

Download WN18RR.tgz Download WN18RR-ID.tgz

YAGO3-10

This dataset was introduced in Convolutional 2D Knowledge Graph Embeddings.
The original dataset as released by the authors is available here.

Entities Relation Types Edges Train Edges Validation Edges Test Edges
123182 37 1089040 1079040 5000 5000

Download YAGO3-10.tgz Download YAGO3-10-ID.tgz

Authors

See also the list of contributors who participated in this project.

License

This project is licensed under the MIT License - see the license file for details.