<p align="center">
<img src="https://github.com/dobraczka/forayer/raw/main/docs/forayerlogo.png" alt="forayer logo", width=200/>
</p>
<h2 align="center"> forayer</h2>
<p align="center">
<a href="https://github.com/dobraczka/forayer/actions/workflows/main.yml"><img alt="Tests" src="https://github.com/dobraczka/forayer/actions/workflows/tests.yml/badge.svg?branch=main"></a>
<a href="https://github.com/dobraczka/forayer/actions/workflows/quality.yml"><img alt="Linting" src="https://github.com/dobraczka/forayer/actions/workflows/quality.yml/badge.svg?branch=main"></a>
<a><img alt="Test coverage" src="https://img.shields.io/endpoint?url=https://gist.githubusercontent.com/dobraczka/6d07d95e43929bcbf9d031c2c8f2015f/raw/forayer_test_gist.json"></a>
<a href="https://pypi.org/project/forayer"/><img alt="Stable python versions" src="https://img.shields.io/pypi/pyversions/forayer"></a>
<a href="https://github.com/dobraczka/forayer/blob/main/LICENSE"><img alt="MIT License" src="https://img.shields.io/badge/license-MIT-blue"></a>
<a href="https://github.com/psf/black"><img alt="Code style: black" src="https://img.shields.io/badge/code%20style-black-000000.svg"></a>
</p>
About
=====
Forayer is a library of **f**irst aid utilities for kn**o**wledge g**r**aph explor**a**tion with an entit**y** c**e**ntric app**r**oach.
It is intended to make data integration of knowledge graphs easier. With entities as first class citizens forayer is a toolset to aid in knowledge graph exploration for data integration and specifically entity resolution.
You can easily load pre-existing entity resolution tasks:
```python
>>> from forayer.datasets import OpenEADataset
>>> ds = OpenEADataset(ds_pair="D_W",size="15K",version=1)
>>> ds.er_task
ERTask({DBpedia: (# entities: 15000, # entities_with_rel: 15000, # rel: 13359,
# entities_with_attributes: 13782, # attributes: 13782, # attr_values: 24995),
Wikidata: (# entities: 15000, # entities_with_rel: 15000, # rel: 13554,
# entities_with_attributes: 14376, # attributes: 14376, # attr_values: 114107)},
ClusterHelper(# elements:30000, # clusters:15000))
```
This entity resolution task holds 2 knowledge graphs and a cluster of known matches. You can search in knowledge graphs:
```python
>>> ds.er_task["DBpedia"].search("Dorothea")
KG(entities={'http://dbpedia.org/resource/E801200':
{'http://dbpedia.org/ontology/activeYearsStartYear': '"1948"^^<http://www.w3.org/2001/XMLSchema#gYear>',
'http://dbpedia.org/ontology/activeYearsEndYear': '"2008"^^<http://www.w3.org/2001/XMLSchema#gYear>',
'http://dbpedia.org/ontology/birthName': 'Dorothea Carothers Allen',
'http://dbpedia.org/ontology/alias': 'Allen, Dorothea Carothers',
'http://dbpedia.org/ontology/birthYear': '"1923"^^<http://www.w3.org/2001/XMLSchema#gYear>',
'http://purl.org/dc/elements/1.1/description': 'Film editor',
'http://dbpedia.org/ontology/birthDate': '"1923-12-03"^^<http://www.w3.org/2001/XMLSchema#date>',
'http://dbpedia.org/ontology/deathDate': '"2010-04-17"^^<http://www.w3.org/2001/XMLSchema#date>',
'http://dbpedia.org/ontology/deathYear': '"2010"^^<http://www.w3.org/2001/XMLSchema#gYear>'}}, rel={}, name=DBpedia)
```
Decide to work with a smaller snippet of the resolution task:
```python
>>> ert_sample = ds.er_task.sample(100)
>>> ert_sample
ERTask({DBpedia: (# entities: 100, # entities_with_rel: 6, # rel: 4,
# entities_with_attributes: 99, # attributes: 99, # attr_values: 274),
Wikidata: (# entities: 100, # entities_with_rel: 4, # rel: 4,
# entities_with_attributes: 100, # attributes: 100, # attr_values: 797)},
ClusterHelper(# elements:200, # clusters:100))
```
And much more can be found in the [user guide](https://forayer.readthedocs.io/en/latest/source/user_guide.html).
Installation
============
You can install forayer via pip:
```bash
pip install forayer
```
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"description": "<p align=\"center\">\n<img src=\"https://github.com/dobraczka/forayer/raw/main/docs/forayerlogo.png\" alt=\"forayer logo\", width=200/>\n</p>\n\n<h2 align=\"center\"> forayer</h2>\n\n<p align=\"center\">\n<a href=\"https://github.com/dobraczka/forayer/actions/workflows/main.yml\"><img alt=\"Tests\" src=\"https://github.com/dobraczka/forayer/actions/workflows/tests.yml/badge.svg?branch=main\"></a>\n<a href=\"https://github.com/dobraczka/forayer/actions/workflows/quality.yml\"><img alt=\"Linting\" src=\"https://github.com/dobraczka/forayer/actions/workflows/quality.yml/badge.svg?branch=main\"></a>\n<a><img alt=\"Test coverage\" src=\"https://img.shields.io/endpoint?url=https://gist.githubusercontent.com/dobraczka/6d07d95e43929bcbf9d031c2c8f2015f/raw/forayer_test_gist.json\"></a>\n<a href=\"https://pypi.org/project/forayer\"/><img alt=\"Stable python versions\" src=\"https://img.shields.io/pypi/pyversions/forayer\"></a>\n<a href=\"https://github.com/dobraczka/forayer/blob/main/LICENSE\"><img alt=\"MIT License\" src=\"https://img.shields.io/badge/license-MIT-blue\"></a>\n<a href=\"https://github.com/psf/black\"><img alt=\"Code style: black\" src=\"https://img.shields.io/badge/code%20style-black-000000.svg\"></a>\n</p>\n\nAbout\n=====\nForayer is a library of **f**irst aid utilities for kn**o**wledge g**r**aph explor**a**tion with an entit**y** c**e**ntric app**r**oach.\nIt is intended to make data integration of knowledge graphs easier. With entities as first class citizens forayer is a toolset to aid in knowledge graph exploration for data integration and specifically entity resolution.\n\nYou can easily load pre-existing entity resolution tasks:\n\n```python\n >>> from forayer.datasets import OpenEADataset\n >>> ds = OpenEADataset(ds_pair=\"D_W\",size=\"15K\",version=1)\n >>> ds.er_task\n ERTask({DBpedia: (# entities: 15000, # entities_with_rel: 15000, # rel: 13359,\n # entities_with_attributes: 13782, # attributes: 13782, # attr_values: 24995),\n Wikidata: (# entities: 15000, # entities_with_rel: 15000, # rel: 13554,\n # entities_with_attributes: 14376, # attributes: 14376, # attr_values: 114107)},\n ClusterHelper(# elements:30000, # clusters:15000))\n```\n\nThis entity resolution task holds 2 knowledge graphs and a cluster of known matches. You can search in knowledge graphs:\n\n```python\n >>> ds.er_task[\"DBpedia\"].search(\"Dorothea\")\n KG(entities={'http://dbpedia.org/resource/E801200': \n {'http://dbpedia.org/ontology/activeYearsStartYear': '\"1948\"^^<http://www.w3.org/2001/XMLSchema#gYear>',\n 'http://dbpedia.org/ontology/activeYearsEndYear': '\"2008\"^^<http://www.w3.org/2001/XMLSchema#gYear>',\n 'http://dbpedia.org/ontology/birthName': 'Dorothea Carothers Allen',\n 'http://dbpedia.org/ontology/alias': 'Allen, Dorothea Carothers',\n 'http://dbpedia.org/ontology/birthYear': '\"1923\"^^<http://www.w3.org/2001/XMLSchema#gYear>',\n 'http://purl.org/dc/elements/1.1/description': 'Film editor',\n 'http://dbpedia.org/ontology/birthDate': '\"1923-12-03\"^^<http://www.w3.org/2001/XMLSchema#date>',\n 'http://dbpedia.org/ontology/deathDate': '\"2010-04-17\"^^<http://www.w3.org/2001/XMLSchema#date>', \n 'http://dbpedia.org/ontology/deathYear': '\"2010\"^^<http://www.w3.org/2001/XMLSchema#gYear>'}}, rel={}, name=DBpedia)\n```\n\nDecide to work with a smaller snippet of the resolution task:\n\n```python\n >>> ert_sample = ds.er_task.sample(100)\n >>> ert_sample\n ERTask({DBpedia: (# entities: 100, # entities_with_rel: 6, # rel: 4,\n # entities_with_attributes: 99, # attributes: 99, # attr_values: 274),\n Wikidata: (# entities: 100, # entities_with_rel: 4, # rel: 4,\n # entities_with_attributes: 100, # attributes: 100, # attr_values: 797)},\n ClusterHelper(# elements:200, # clusters:100))\n```\n\nAnd much more can be found in the [user guide](https://forayer.readthedocs.io/en/latest/source/user_guide.html).\n\nInstallation\n============\n\nYou can install forayer via pip:\n\n```bash\n pip install forayer\n```\n",
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