Entity Matching model#

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Entity Matching Model (EMM) solves the problem of matching company names between two possibly very large datasets. EMM can match millions against millions of names with a distributed approach. It uses the well-established candidate selection techniques in string matching, namely: tfidf vectorization combined with cosine similarity (with significant optimization), both word-based and character-based, and sorted neighbourhood indexing. These so-called indexers act complementary for selecting realistic name-pair candidates. On top of the indexers, EMM has a classifier with optimized string-based, rank-based, and legal-entity based features to estimate how confident a company name match is.

The classifier can be trained to give a string similarity score or a probability of match. Both types of score are useful, in particular when there are many good-looking matches to choose between. Optionally, the EMM package can also be used to match a group of company names that belong together, to a common company name in the ground truth. For example, all different names used to address an external bank account. This step aggregates the name-matching scores from the supervised layer into a single match.

The package is modular in design and and works both using both Pandas and Spark. A classifier trained with the former can be used with the latter and vice versa.

For release history see GitHub Releases.

Notebooks#

For detailed examples of the code please see the notebooks under notebooks/.

  • 01-entity-matching-pandas-version.ipynb: Using the Pandas version of EMM for name-matching.

  • 02-entity-matching-spark-version.ipynb: Using the Spark version of EMM for name-matching.

  • 03-entity-matching-training-pandas-version.ipynb: Fitting the supervised model and setting a discrimination threshold (Pandas).

  • 04-entity-matching-aggregation-pandas-version.ipynb: Using the aggregation layer and setting a discrimination threshold (Pandas).

Documentation#

For documentation, design, and API see the documentation.

Check it out#

The Entity matching model library requires Python >= 3.7 and is pip friendly. To get started, simply do:

pip install emm

or check out the code from our repository:

git clone https://github.com/ing-bank/EntityMatchingModel.git
pip install -e EntityMatchingModel/

where in this example the code is installed in edit mode (option -e).

Additional dependencies can be installed with, e.g.:

pip install "emm[spark,dev,test]"

You can now use the package in Python with:

import emm

Congratulations, you are now ready to use the Entity Matching model!

Quick run#

As a quick example, you can do:

from emm import PandasEntityMatching
from emm.data.create_data import create_example_noised_names

# generate example ground-truth names and matching noised names, with typos and missing words.
ground_truth, noised_names = create_example_noised_names(random_seed=42)
train_names, test_names = noised_names[:5000], noised_names[5000:]

# two example name-pair candidate generators: character-based cosine similarity and sorted neighbouring indexing
indexers = [
  {
      'type': 'cosine_similarity',
      'tokenizer': 'characters',   # character-based cosine similarity. alternative: 'words'
      'ngram': 2,                  # 2-character tokens only
      'num_candidates': 5,         # max 5 candidates per name-to-match
      'cos_sim_lower_bound': 0.2,  # lower bound on cosine similarity
  },
  {'type': 'sni', 'window_length': 3}  # sorted neighbouring indexing window of size 3.
]
em_params = {
  'name_only': True,         # only consider name information for matching
  'entity_id_col': 'Index',  # important to set both index and name columns to pick up
  'name_col': 'Name',
  'indexers': indexers,
  'supervised_on': False,    # no supervided model (yet) to select best candidates
  'with_legal_entity_forms_match': True,   # add feature that indicates match of legal entity forms (e.g. ltd != co)
}
# 1. initialize the entity matcher
p = PandasEntityMatching(em_params)

# 2. fitting: prepare the indexers based on the ground truth names, eg. fit the tfidf matrix of the first indexer.
p.fit(ground_truth)

# 3. create and fit a supervised model for the PandasEntityMatching object, to pick the best match (this takes a while)
#    input is "positive" names column 'Name' that are all supposed to match to the ground truth,
#    and an id column 'Index' to check with candidate name-pairs are matching and which not.
#    A fraction of these names may be turned into negative names (= no match to the ground truth).
#    (internally, candidate name-pairs are automatically generated, these are the input to the classification)
p.fit_classifier(train_names, create_negative_sample_fraction=0.5)

# 4. scoring: generate pandas dataframe of all name-pair candidates.
#    The classifier-based probability of match is provided in the column 'nm_score'.
#    Note: can also call p.transform() without training the classifier first.
candidates_scored_pd = p.transform(test_names)

# 5. scoring: for each name-to-match, select the best ground-truth candidate.
best_candidates = candidates_scored_pd[candidates_scored_pd.best_match]
best_candidates.head()

For Spark, you can use the class SparkEntityMatching instead, with the same API as the Pandas version. For all available examples, please see the tutorial notebooks under notebooks/.

Project contributors#

This package was authored by ING Analytics Wholesale Banking.

Contact and support#

Contact the WBAA team via Github issues. Please note that INGA-WB provides support only on a best-effort basis.

License#

Copyright ING WBAA 2023. Entity Matching Model is completely free, open-source and licensed under the MIT license.

Table of Contents#

Index#

Index