SONAD: Software Name Disambiguation

SONAD (Software Name Disambiguation) is a command-line tool and Python package that links software mentions in scientific papers to their corresponding repository URLs. It leverages NLP, third-party tools like SOMEF, and metadata to resolve software names. It is limited to fetching URLs from GitHub, PyPI and CRAN. Take into account that this is not 100% accurate as it uses a machine learning model trained on data, but it did outperform models llama-3.1-8b-instant, qwen-qwq-32b, gemma2-9b-it and deepseek-r1-distill-llama-70b.

Installation

Install using pip:

pip install sonad

Initial Configuration

Before using SONAD, you must make sure that SOMEF works
(https://github.com/KnowledgeCaptureAndDiscovery/somef/?tab=readme-ov-file),
which is used for software metadata extraction.

Follow their instructions to make sure somef runs correctly on your system. It is installed with sonad, but the user should check if somef works before running sonad.

It is also strongly recommended to provide a GitHub API token to avoid rate limits when querying GitHub. You can configure this for sonad and somef once using:

If you wish not to use GitHub token, you then need to configure somef with automatic option (somef configure -a).

sonad configure

Your token will be saved for future runs.

Requirements

SONAD requires Python 3.10. All dependencies are installed automatically.

Some key libraries:

• pandas
• scikit-learn
• xgboost
• sentence-transformers
• beautifulsoup4
• requests
• SPARQLWrapper
• somef
• textdistance
• lxml
• cloudpickle

Usage

After installation, you can run the main command:

sonad process -i <input_file.csv> -o <output_file.csv> [-t <temp_folder>] 

Parameters

• -i, --input (required): Path to the input CSV file.
• -o, --output (required): Path where the output CSV will be saved.
• -t, --temp (optional): Folder where temporary files will be written it the folder is provided.

Input Format

The input CSV must contain the following columns:

• name: The software name mentioned in the paper.
• doi: The DOI of the paper.
• paragraph: The paragraph in which the software is mentioned.

Optionally, it can include:

• candidate_urls: A comma-separated list of candidate software URLs that might correspond to the software.

Example:

name,doi,paragraph,candidate_urls
Scikit-learn,10.1000/xyz123,"We used Scikit-learn for classification.","https://github.com/scikit-learn/scikit-learn"

Output Format

The output CSV will contain one row for each input mention, with the following columns:

• name: The software name from the input.
• paragraph: The paragraph where the software was mentioned.
• doi: The DOI of the paper in which the software was mentioned.
• synonyms: Alternative names or variants of the software name identified during processing.
• language: The inferred programming language(s) used by the software, if available.
• authors: Names of authors of the paper it they can be fetched from OpenAlex tool.
• urls: A comma-separated list of predicted repository or project URLs (e.g., GitHub, PyPI, CRAN).
• not_urls: URLs that were considered but rejected during disambiguation (e.g., due to low confidence or irrelevance).

Example:

name,paragraph,doi,synonyms,language,authors,urls,not_urls
Scikit-learn,"We used Scikit-learn for classification.",10.1000/xyz123,"scikit learn;sklearn",Python,"Pedregosa et al.","https://github.com/scikit-learn/scikit-learn","https://pypi.org/project/sklearn/"

License

MIT License © Jelena Djuric
https://github.com/jelenadjuric01

Contributions

Feel free to submit issues or pull requests on the GitHub repository:
https://github.com/jelenadjuric01/Software-Disambiguation