Metadata-Version: 2.2
Name: neer-match-utilities
Version: 1.0.6b0
Summary: Extended funcationality for NEural-symbolic Entity Reasoning and Matching
Author-email: Marius Liebald <maliedvp@gmail.com>, Pantelis Karapanagiotis <pikappa.devel@gmail.com>
Project-URL: Homepage, https://www.marius-liebald.com/py-neer-utilities/
Project-URL: Documentation, https://www.marius-liebald.com/py-neer-utilities/
Project-URL: Source Code, https://github.com/maliedvp/py-neer-utilities
Project-URL: Bug Tracker, https://github.com/maliedvp/py-neer-utilities/issues
Classifier: Environment :: Console
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Information Technology
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Database
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: <3.13,>=3.10
Description-Content-Type: text/markdown
License-File: LICENSE.txt
Requires-Dist: ltn>=0.0.1
Requires-Dist: matplotlib>=3.5.0
Requires-Dist: numpy>=1.26.4
Requires-Dist: pandas>=2.2.3
Requires-Dist: rapidfuzz>=3.10.1
Requires-Dist: tensorflow>=2.18.0
Requires-Dist: neer-match>=0.7.38
Requires-Dist: dill>=0.3.8
Provides-Extra: tests
Requires-Dist: pytest; extra == "tests"

# 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>

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

The framework `neermatch` provides a set of tools for entity matching
based on deep learning, symbolic learning, and a hybrid approach
combining both deep and symbolic learning. It is designed to support
easy set-up, training, and inference of entity matching models. The
package provides automated fuzzy logic reasoning (by refutation)
functionality that can be used to examine the significance of particular
associations between fields in an entity matching task.

The `neermatch` framework encompasses three packages:

1.  `py-neer-match`: The `Python` implementation of the basic
    functionalities. [Learn more](https://py-neer-match.pikappa.eu)
2.  `py-neer-utilities`: A `Python` package that provides additional
    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`).

# Contributors

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

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