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.readthedocs.yml (457, 2023-06-23)
CHANGELOG.md (2341, 2023-06-23)
LICENSE (1072, 2023-06-23)
codecov.yml (90, 2023-06-23)
docs (0, 2023-06-23)
docs\Makefile (634, 2023-06-23)
docs\conf.py (2824, 2023-06-23)
docs\forayerlogo.png (17044, 2023-06-23)
docs\index.rst (2846, 2023-06-23)
docs\make.bat (760, 2023-06-23)
docs\source (0, 2023-06-23)
docs\source\apidoc.rst (1340, 2023-06-23)
docs\source\user_guide.rst (12465, 2023-06-23)
forayer (0, 2023-06-23)
forayer\__init__.py (849, 2023-06-23)
forayer\datasets (0, 2023-06-23)
forayer\datasets\__init__.py (179, 2023-06-23)
forayer\datasets\base_dataset.py (1100, 2023-06-23)
forayer\datasets\oaei_kg.py (7433, 2023-06-23)
forayer\datasets\open_ea.py (2425, 2023-06-23)
forayer\evaluation (0, 2023-06-23)
forayer\evaluation\__init__.py (147, 2023-06-23)
forayer\evaluation\er_metrics.py (1721, 2023-06-23)
forayer\exploration (0, 2023-06-23)
forayer\exploration\attributes.py (9066, 2023-06-23)
forayer\input_output (0, 2023-06-23)
forayer\input_output\__init__.py (40, 2023-06-23)
forayer\input_output\from_to_gradoop.py (19335, 2023-06-23)
forayer\input_output\from_to_open_ea.py (6816, 2023-06-23)
forayer\input_output\from_to_rdf.py (4508, 2023-06-23)
forayer\knowledge_graph (0, 2023-06-23)
forayer\knowledge_graph\__init__.py (303, 2023-06-23)
forayer\knowledge_graph\clusters.py (15512, 2023-06-23)
... ...

forayer

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://dbpedia.org/ontology/activeYearsEndYear': '"2008"^^', 'http://dbpedia.org/ontology/birthName': 'Dorothea Carothers Allen', 'http://dbpedia.org/ontology/alias': 'Allen, Dorothea Carothers', 'http://dbpedia.org/ontology/birthYear': '"1923"^^', 'http://purl.org/dc/elements/1.1/description': 'Film editor', 'http://dbpedia.org/ontology/birthDate': '"1923-12-03"^^', 'http://dbpedia.org/ontology/deathDate': '"2010-04-17"^^', 'http://dbpedia.org/ontology/deathYear': '"2010"^^'}}, 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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