Introducing Quadipy: A Python package for transforming structured data into RDF graph format

Vouch is excited to announce the release of Quadipy, Vouch’s first open-source project. Built by the Vouch engineering team, Quadipy is a Python package that makes it easy to transform structured data into RDF graph format.

Quadipy was designed to help developers build a config-based ingestion pipeline to an RDF data store, similar to FiveTran or Stitch, but for RDF. Quadipy doesn’t handle connections to different systems, but it allows you to configure the RDF data you want to create from any data source. By leveraging RDFLib, Quadipy can pythonically structure RDF data, making it easy to work with and manipulate.

We believe that Quadipy can be a powerful tool for anyone who needs to work with RDF data. Whether you’re building a knowledge graph or working with other kinds of structured data, Quadipy makes it easy to convert that data into RDF graph format, opening up new possibilities for data analysis and exploration.

We’ve used this config-based system to build out our internal knowledge graph at Vouch, and gave a talk on the subject at KGC’22: “Modeling the startup ecosystem using a config-based knowledge graph.”

So what can you do with Quadipy? Here are just a few examples:

• Build a knowledge graph: Quadipy makes it easy to convert any structured data into RDF graph format, which is perfect for building knowledge graphs. By leveraging the power of RDF, you can create a network of relationships between entities and unlock new insights from your data.
• Ingest data from multiple sources: Quadipy allows you to configure the RDF data you want to create from any data source. This means you can ingest data from multiple sources and transform it into a unified RDF graph format.
• Perform data analysis and exploration: Once your data is in RDF graph format, you can use a variety of tools and techniques to perform data analysis and exploration. For example, you could use SPARQL queries to explore relationships between entities, or use graph visualization tools to gain new insights from your data.

We believe that Quadipy can be a powerful tool for anyone who needs to work with RDF data. Whether you’re building a knowledge graph, ingesting data from multiple sources, or performing data analysis and exploration, Quadipy makes it easy to convert your data into RDF graph format.

To get started with Quadipy, check out our repo on GitHub or the documentation. We hope that you’ll find Quadipy useful and that you’ll consider contributing to the project. We’re excited to see what new things people will build with Quadipy!

A big shout out to Avy Faingezicht, Nabeel Zewail, Shuyu Wang, Nikki Hu, Emily Ekdahl, and Conor Mongey for their contributions to the project, their dedication and hard work have been invaluable to its success.