forayer 0.3.0

First aid utilies for knowledge graph exploration with an entity centric approach

forayer

About

Forayer is a library of first aid utilities for knowledge graph exploration with an entity centric approach. 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:

  >>> 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:

  >>> 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:

  >>> 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.

Installation

You can install forayer via pip:

  pip install forayer