neer-match-utilities 1.0.1
Extended funcationality for NEural-symbolic Entity Reasoning and Matching
Requires Python: <3.13,>=3.10

# Neer Match Utilities


<a href="https://www.marius-liebald.com/py-neer-utilities/index.html" style="float:right; margin-left:10px;">
<img src="docs/source/_static/img/hex-logo.png" style="height:139px !important; width:auto !important;" alt="neermatch utilities website" />
</a>

<!-- badges: start -->

![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)
<!-- badges: end -->

`neermatch` is a framework for entity matching that supports deep
learning, symbolic learning, and hybrid approaches, using neural
networks, gradient boosting, logit, or probit as classifiers. It is
tailored to the needs of economic historians and quantitative
economists, but generalizes to any domain requiring entity matching.

The `neermatch` framework encompasses three packages:

1.  `py-neer-match`: The `Python` implementation of the basic
    functionalities (ANNs only). [Learn
    more](https://py-neer-match.pikappa.eu)
2.  `py-neer-utilities`: A `Python` package that provides additional
    models and functionalities to streamline and support the entity
    matching workflow. ([this
    project](https://www.marius-liebald.com/py-neer-utilities/index.html))
3.  `r-neer-match`: The `R` implementation of the basic functionalites.
    [Learn more](https://github.com/pi-kappa-devel/r-neer-match)

The project is financially supported by the [Deutsche
Forschungsgemeinschaft](https://www.dfg.de/de) (DFG) under Grant
539465691 as part of the Infrastructure Priority Programme [*New Data
Spaces for the Social Sciences*](https://www.new-data-spaces.de/en-us/)
(SPP 2431). Reading the article [*Karapanagiotis and Liebald
(2023)*](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4541376)
helps to understand the theoretical foundation and design of `neermatch`
(note that the article refers to an earlier version of the framework,
previously labeled as `mlmatch`).

The documentation provides examples of how `neermatch` may be used. The
data used in these examples are available in this
[folder](https://github.com/maliedvp/py-neer-utilities/tree/master/docs/source/docs/source/_static/examples)
of the GitHub repository.

# Contributors

[Marius Liebald](https://www.marius-liebald.de) (maintainer)

[Pantelis Karapanagiotis](https://www.pikappa.eu) (contributor)

[Erik
Hellberg](https://www.uu.se/en/research/uppsala-history-of-inequality-and-labor-lab/members/erik-hellberg)
(contributor)

# Installation

``` bash
pip install neer-match
pip install neer-match-utilities
```

# Official Documentation

The documentation is hosted under
<https://www.marius-liebald.com/py-neer-utilities/index.html>

# License

The package is distributed under the [MIT license](LICENSE.txt).

# References

<div id="refs" class="references csl-bib-body hanging-indent"
entry-spacing="0">

<div id="ref-gram2022" class="csl-entry">

Gram, Dennis, Pantelis Karapanagiotis, Marius Liebald, and Uwe Walz.
2022. “Design and Implementation of a Historical German Firm-Level
Financial Database.” *ACM Journal of Data and Information Quality
(JDIQ)* 14 (3): 1–22. <https://doi.org/10.1145/3531533>.

</div>

<div id="ref-karapanagiotis2023" class="csl-entry">

Karapanagiotis, Pantelis, and Marius Liebald. 2023. “Entity Matching
with Similarity Encoding: A Supervised Learning Recommendation Framework
for Linking (Big) Data.” <http://dx.doi.org/10.2139/ssrn.4541376>.

</div>

<div id="ref-pyneermatch2024" class="csl-entry">

———. 2024a. “<span class="nocase">NEural-symbolic</span> Entity
Reasoning and Matching (Python Neer Match).”
<https://github.com/pi-kappa-devel/py-neer-match>.

</div>

<div id="ref-rneermatch2024" class="csl-entry">

———. 2024b. “<span class="nocase">NEural-symbolic</span> Entity
Reasoning and Matching (R Neer Match).”
<https://github.com/pi-kappa-devel/r-neer-match>.

</div>

<div id="ref-lin2017" class="csl-entry">

Lin, Tsung-Yi, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár.
2017. “Focal Loss for Dense Object Detection.” In *Proceedings of the
IEEE International Conference on Computer Vision (ICCV)*, 2980–88. IEEE.
<https://doi.org/10.1109/ICCV.2017.324>.

</div>

<div id="ref-lipton2014optimal" class="csl-entry">

Lipton, Zachary C., Charles Elkan, and Balakrishnan Naryanaswamy. 2014.
“Optimal Thresholding of Classifiers to Maximize F1 Measure.” In *ECML
PKDD 2014*.

</div>

</div>