Nomenklatura de-duplicates and integrates different Follow the Money entities. It serves to clean up messy data and to find links between different datasets.
You can install
nomenklatura via PyPI:
$ pip install nomenklatura
Much of the functionality of
nomenklatura can be used as a command-line tool. In the following example, we'll assume that you have a file containing Follow the Money entities in your local directory, named
entities.ijson. If you just want try it out, you can use the file
tests/fixtures/donations.ijson in this repository for testing (it contains German campaign finance data).
With the file in place, you will cross-reference the entities to generate de-duplication candidates, then run the interactive de-duplication UI in your console, and eventually apply the judgements to generate a new file with merged entities:
# generate merge candidates using an in-memory index: $ nomenklatura xref entities.ijson # note there is now a new file, `entities.rslv.ijson` that contains de-duplication info. $ nomenklatura dedupe entiites.ijson # will pop up a user interface. $ nomenklatura apply entities.ijson -o merged.ijson # de-duplicated data goes into `merged.ijson`: $ cat entities.ijson | wc -l 474 entities.ijson $ cat entities.ijson | wc -l 468 merged.ijson
The command-line use of
nomenklatura is targeted at small datasets which need to be de-duplicated. For more involved scenarios, the package also offers a Python API which can be used to control the semantics of de-duplication.
nomenklatura.Dataset- implements a basic dataset for describing a set of entities.
nomenklatura.Loader- a general purpose access mechanism for entities. By default, a
nomenklatura.FileLoaderis used to access entity data stored in files, but the loader can be subclassed to work with entities from a database system.
nomenklatura.Index- a full-text in-memory search index for FtM entities. In the application, this is used to block de-duplication candidates, but the index can also be used to drive an API etc.
nomenklatura.Resolver- the core of the de-duplication process, the resolver is essentially a graph with edges made out of entity judgements. The resolver can be used to store judgements or get the canonical ID for a given entity.
All of the API classes have extensive type annotations, which should make their integration in any modern Python API simpler.
This package offers an implementation of an in-memory data deduplication framework centered around the FtM data model. The idea is the following workflow:
Later on, the following might be added:
The key implementation detail of nomenklatura is the
Resolver, a graph structure that
manages user decisions regarding entity identity. Edges are
Judgements of whether
two entity IDs are the same, not the same, or undecided. The resolver implements an
algorithm for computing connected components, which can the be used to find the best
available ID for a cluster of entities. It can also be used to evaluate transitive
judgements, e.g. if A <> B, and B = C, then we don't need to ask if A = C.
This codebase is licensed under the terms of an MIT license (see LICENSE).
We're keen for any contributions, bug fixes and feature suggestions, please use the GitHub issue tracker for this repository.
Nomenklatura is currently developed thanks to a Prototypefund grant for OpenSanctions. Previous iterations of the package were developed with support from Knight-Mozilla OpenNews and the Open Knowledge Foundation Labs.