sylloge 0.3.0
Small library to simplify collecting and loading of entity alignment benchmark datasets
License: MIT
Requires Python: <4.0,>=3.8
Keywords: entity resolution, knowledge graph, datasets, entity alignment

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<h2 align="center">sylloge</h2>

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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`](https://eche.readthedocs.io/en/latest/reference/eche/#eche.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](https://www.dask.org/) 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:

```bash
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:
```bash
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
============
```bash
pip install sylloge
```

Datasets
========
| Dataset family name | Year | # of Datasets | Sources | References |
|:--------------------|:----:|:-------------:|:-------:|:----------|
| [OpenEA](https://sylloge.readthedocs.io/en/latest/source/datasets.html#sylloge.OpenEA) | 2020 | 16 | DBpedia, Yago, Wikidata |  [Paper](http://www.vldb.org/pvldb/vol13/p2326-sun.pdf), [Repo](https://github.com/nju-websoft/OpenEA#dataset-overview) |
| [MED-BBK](https://sylloge.readthedocs.io/en/latest/source/datasets.html#sylloge.MED_BBK) | 2020 | 1 | Baidu Baike |  [Paper](https://aclanthology.org/2020.coling-industry.17.pdf), [Repo](https://github.com/ZihengZZH/industry-eval-EA/tree/main#benchmark) |
| [MovieGraphBenchmark](https://sylloge.readthedocs.io/en/latest/source/datasets.html#sylloge.MovieGraphBenchmark) | 2022 | 3 | IMDB, TMDB, TheTVDB | [Paper](http://ceur-ws.org/Vol-2873/paper8.pdf), [Repo](https://github.com/ScaDS/MovieGraphBenchmark) |
| [OAEI](https://sylloge.readthedocs.io/en/latest/source/datasets.html#sylloge.OAEI) | 2022 | 5 | Fandom wikis | [Paper](https://ceur-ws.org/Vol-3324/oaei22_paper0.pdf), [Website](http://oaei.ontologymatching.org/2022/knowledgegraph/index.html) |

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]
```