UPDATE 2025-05: the next generation of the fingerprints codebase is now included in rigour. See the documentation here: https://opensanctions.github.io/rigour/names/. This library is now UNMAINTAINED.

This library helps with the generation of fingerprints for entity data. A fingerprint in this context is understood as a simplified entity identifier, derived from it's name or address and used for cross-referencing of entity across different datasets.

import fingerprints fp = fingerprints.generate('Mr. Sherlock Holmes') assert fp == 'holmes sherlock' fp = fingerprints.generate('Siemens Aktiengesellschaft') assert fp == 'ag siemens' fp = fingerprints.generate('New York, New York') assert fp == 'new york'

A significant part of what fingerprints does it to recognize company legal form names. For example, fingerprints will be able to simplify Общество с ограниченной ответственностью to ООО, or Aktiengesellschaft to AG. The required database is based on two different sources:

• A Google Spreadsheet created by OCCRP.
• The ISO 20275: Entity Legal Forms Code List

Wikipedia also maintains an index of types of business entity.

• Clustering in Depth, part of the OpenRefine documentation discussing how to create collisions in data clustering.
• probablepeople, parser for western names made by the brilliant folks at datamade.us.
• The study Developing a Legal Form Classification and Extraction Approach For Company Entity Matching by Kruse et al. (2021) investigates four approaches for identifying and classifying legal forms in company names.
• List of Legal Forms from AnaCredit dataset by ECB (one of the Annexes).
• Transformer-based Entity Legal Form Classification by Arimond et al. (2023).