Overview¶
This simple library aims to collect entity-alignment benchmark datasets and make them easily available.
from sylloge import OpenEA
ds = OpenEA()
print(ds)
# OpenEA(graph_pair=D_W, size=15K, version=V1, rel_triples_left=38265, rel_triples_right=42746, attr_triples_left=52134, attr_triples_right=138246, ent_links=15000, folds=5)
print(ds.rel_triples_right.head())
# head relation tail
# 0 http://www.wikidata.org/entity/Q6176218 http://www.wikidata.org/entity/P27 http://www.wikidata.org/entity/Q145
# 1 http://www.wikidata.org/entity/Q212675 http://www.wikidata.org/entity/P161 http://www.wikidata.org/entity/Q446064
# 2 http://www.wikidata.org/entity/Q13512243 http://www.wikidata.org/entity/P840 http://www.wikidata.org/entity/Q84
# 3 http://www.wikidata.org/entity/Q2268591 http://www.wikidata.org/entity/P31 http://www.wikidata.org/entity/Q11424
# 4 http://www.wikidata.org/entity/Q11300470 http://www.wikidata.org/entity/P178 http://www.wikidata.org/entity/Q170420
print(ds.attr_triples_left.head())
# head relation tail
# 0 http://dbpedia.org/resource/E534644 http://dbpedia.org/ontology/imdbId 0044475
# 1 http://dbpedia.org/resource/E340590 http://dbpedia.org/ontology/runtime 6480.0^^<http://www.w3.org/2001/XMLSchema#double>
# 2 http://dbpedia.org/resource/E840454 http://dbpedia.org/ontology/activeYearsStartYear 1948^^<http://www.w3.org/2001/XMLSchema#gYear>
# 3 http://dbpedia.org/resource/E971710 http://purl.org/dc/elements/1.1/description English singer-songwriter
# 4 http://dbpedia.org/resource/E022831 http://dbpedia.org/ontology/militaryCommand Commandant of the Marine Corps
The gold standard entity links are stored as [eche](https://github.com/dobraczka/eche) ClusterHelper, which provides convenient functionalities:
print(ds.ent_links.clusters[0])
# {'http://www.wikidata.org/entity/Q21197', 'http://dbpedia.org/resource/E123186'}
print(('http://www.wikidata.org/entity/Q21197', 'http://dbpedia.org/resource/E123186') in ds.ent_links)
# True
print(('http://dbpedia.org/resource/E123186', 'http://www.wikidata.org/entity/Q21197') in ds.ent_links)
# True
print(ds.ent_links.links('http://www.wikidata.org/entity/Q21197'))
# 'http://dbpedia.org/resource/E123186'
print(ds.ent_links.all_pairs())
# <itertools.chain object at 0x7f92c6287c10>
Most datasets are binary matching tasks, but for example the MovieGraphBenchmark provides a multi-source setting:
ds = MovieGraphBenchmark(graph_pair="multi")
print(ds)
MovieGraphBenchmark(backend=pandas,graph_pair=multi, rel_triples_0=17507, attr_triples_0=20800 rel_triples_1=27903, attr_triples_1=23761 rel_triples_2=15455, attr_triples_2=20902, ent_links=3598, folds=5)
print(ds.dataset_names)
('imdb', 'tmdb', 'tvdb')
Here the [PrefixedClusterHelper](https://eche.readthedocs.io/en/latest/reference/eche/#eche.PrefixedClusterHelper) various convenience functions:
You can get a canonical name for a dataset instance to use e.g. to create folders to store experiment results:
print(ds.canonical_name)
# 'openea_d_w_15k_v1'
You can use dask as backend for larger datasets:
ds = OpenEA(backend="dask")
print(ds)
# OpenEA(backend=dask, graph_pair=D_W, size=15K, version=V1, rel_triples_left=38265, rel_triples_right=42746, attr_triples_left=52134, attr_triples_right=138246, ent_links=15000, folds=5)
Which replaces pandas DataFrames with dask DataFrames.
Datasets can be written/read as parquet via to_parquet or read_parquet. After the initial read datasets are cached using this format. The cache_path can be explicitly set and caching behaviour can be disable via use_cache=False, when initalizing a dataset.
Some datasets come with pre-determined splits:
tree ~/.data/sylloge/open_ea/cached/D_W_15K_V1
├── attr_triples_left_parquet
├── attr_triples_right_parquet
├── dataset_names.txt
├── ent_links_parquet
├── folds
│ ├── 1
│ │ ├── test_parquet
│ │ ├── train_parquet
│ │ └── val_parquet
│ ├── 2
│ │ ├── test_parquet
│ │ ├── train_parquet
│ │ └── val_parquet
│ ├── 3
│ │ ├── test_parquet
│ │ ├── train_parquet
│ │ └── val_parquet
│ ├── 4
│ │ ├── test_parquet
│ │ ├── train_parquet
│ │ └── val_parquet
│ └── 5
│ ├── test_parquet
│ ├── train_parquet
│ └── val_parquet
├── rel_triples_left_parquet
└── rel_triples_right_parquet
some don’t:
tree ~/.data/sylloge/oaei/cached/starwars_swg
├── attr_triples_left_parquet
│ └── part.0.parquet
├── attr_triples_right_parquet
│ └── part.0.parquet
├── dataset_names.txt
├── ent_links_parquet
│ └── part.0.parquet
├── rel_triples_left_parquet
│ └── part.0.parquet
└── rel_triples_right_parquet
└── part.0.parquet
You can install sylloge via pip:
pip install sylloge
Documentation