sylloge

This simple library aims to collect entity-alignment benchmark datasets and make them easily available.

Usage

Load benchmark datasets:

>>> from sylloge import OpenEA
>>> ds = OpenEA()
>>> ds
OpenEA(backend=pandas, 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)
>>> 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
>>> 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:

>>> ds.ent_links.clusters[0]
{'http://www.wikidata.org/entity/Q21197', 'http://dbpedia.org/resource/E123186'}
>>> ('http://www.wikidata.org/entity/Q21197', 'http://dbpedia.org/resource/E123186') in ds.ent_links
True
>>> ('http://dbpedia.org/resource/E123186', 'http://www.wikidata.org/entity/Q21197') in ds.ent_links
True
>>> ds.ent_links.links('http://www.wikidata.org/entity/Q21197')
'http://dbpedia.org/resource/E123186'
>>> 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")
>>> 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)
>>> ds.dataset_names
('imdb', 'tmdb', 'tvdb')

Here the PrefixedClusterHelper various convenience functions:

Get pairs between specific dataset pairs

>>> list(ds.ent_links.pairs_in_ds_tuple(("imdb","tmdb")))[0]
('https://www.scads.de/movieBenchmark/resource/IMDB/nm0641721', 'https://www.scads.de/movieBenchmark/resource/TMDB/person1236714')

Get number of intra-dataset pairs
>>> ds.ent_links.number_of_intra_links
(1, 64, 22663)

For all datasets you can get a canonical name for a dataset instance to use e.g. to create folders to store experiment results:

>>> ds.canonical_name
'openea_d_w_15k_v1'

You can use dask as backend for larger datasets:

>>> ds = OpenEA(backend="dask")
>>> 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

Installation

pip install sylloge

Datasets

More broad statistics are provided in dataset_statistics.csv. You can also get a pandas DataFrame with statistics for specific datasets for example to create tables for publications:

>>> ds = MovieGraphBenchmark(graph_pair="multi")
>>> from sylloge.base import create_statistics_df
>>> stats_df = create_statistics_df([ds])
>>> stats_df.loc[("MovieGraphBenchmark","moviegraphbenchmark_multi","imdb")]
                                                            Entities  Relation Triples  Attribute Triples  ...  Clusters  Intra-dataset Matches  All Matches
Dataset family      Task Name                 Dataset Name                                                 ...
MovieGraphBenchmark moviegraphbenchmark_multi imdb              5129             17507              20800  ...      3598                      1        31230

[1 rows x 9 columns]