Corporate open-source intelligence toolkit for data-driven investigations.

A common use case in investigative reporting is to research a given set of companies or people by searching for their ownership, control and other relationships in online databases. corpint augments that process by automating look-ups in web services and building a network graph out of the resulting set of links. It also provides an explicit way to accept and reject results from online research, thus making sure the entire resulting graph is fact-checked.

To run corpint you will want to have Python and PostgreSQL installed. You may also want to install Neo4J if you intend to use the graph exporter feature.

It's recommended to run corpint inside a Python virtual environment. When you have a virtualenv set up, clone the git repository and install the package:

$ git clone https://github.com/alephdata/corpint.git
$ cd corpint
$ pip install -e .

Most of the usage of corpint is handled via a command-line utility, which allows users to enrich data from external sources, find duplicates proposed for merging and generate output formats such as a Neo4J graph.

Some configuration is required to make corpint connect to the correct database and to the right subset of the data in there.

• CORPINT_PROJECT is the title of the current investigation, in a slug form, e.g. foo or panama_papers.
• DATABASE_URI is an environment variable containing a database connection URI of the form postgresql://user:password@host/database.
• NEO4J_URI is the URL for Neo4J, usually http://neo4j:neo4j@localhost:7474/.

Unfortunately, loading data still requires some manual mapping of the data into the structure expected by corpint. This can be done via a Python script:

from corpint import project, csv
from corpint import PERSON, COMPANY

# name the source, used to distinguish from enrichment results:
origin = project.origin('mysource')
# delete previous load:
origin.clear()

# lets assume a data file with the names of companies and their directors
with open('data.csv', 'r') as fh:
  for row in csv(fh):
    # get a row:
    director_name = row.get('director_name')
    # important: you need to generate unique IDs (uids) for each entity you
    # load.
    director_id = origin.uid(director_name)

    # check for empty names:
    if director_id is not None:
      origin.emit_entity({
        'uid': director_id,
        'name': director_name,
        'schema': PERSON,
        # this will enable company look-ups for the entity:
        'tasked': True
      })

    # same for the companies
    company_name = row.get('company_name')
    company_id = origin.uid(company_name)
    if company_id is not None:
      origin.emit_entity({
        'uid': company_id,
        'name': company_name,
        'schema': Company,
        # maybe add an extra property:
        'country': row.get('company_country'),
        'tasked': True
      })

    # now, create a link:
    if company_id is not None and director_id is not None:
      origin.emit_link({
        'source_uid': director_id,
        'target_uid': company_id,
        'schema': 'DIRECTOR',
        'summary': row.get('director_role')
      })

Once the data is loaded, you might want to start by checking if there are duplicates within the source list:

$ corpint mappings generate -o mysource -t 0.8

This will generate all duplicate candidates with a ranking better than 80%.

You can then go and use the web interface to manually cross-check duplicates:

$ corpint webui

This will expose the web interface on port 5000 of the local machine.

To run all the loaded entities against an online source, such as OpenCorporates, and store the resulting matches, run the following command:

$ corpint enrich -o mysource opencorporates

Valid enrichers currently include opencorporates, aleph, alephdocuments, gmaps, and bvdorbis. Some of these enrichers may work better if API keys are provided:

• OPENCORPORATES_APIKEY a valid API key from OpenCorporates.
• GMAPS_APIKEY a Google Maps API key
• ALEPH_APIKEY, ALEPH_HOST to specify an Aleph instance other than data.occrp.org.

The MIT License (MIT)

Copyright (c) 2017 Journalism Development Network, Inc.

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

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