# Streamlining Knowledge Graph Creation with PyRML

arXiv: https://arxiv.org/abs/2505.20949 · HTML: https://mlsanity.com/paper/2505.20949/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 14 references that are in the map.
- First posted on arXiv 27 May 2025.

## Builds on (most cited first, 4 of 4)

- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [Ontology Generation using Large Language Models](https://mlsanity.com/paper/2503.05388/) (2025)
- [Facade-X: an opinionated approach to SPARQL anything](https://mlsanity.com/paper/2106.02361/) (2021)
- [Streamlining Knowledge Graph Construction with a faccade: The SPARQL Anything project](https://mlsanity.com/paper/2310.16700/) (2023)

## Abstract (arXiv)

Knowledge Graphs (KGs) are increasingly adopted as a foundational technology for integrating heterogeneous data in domains such as climate science, cultural heritage, and the life sciences. Declarative mapping languages like R2RML and RML have played a central role in enabling scalable and reusable KG construction, offering a transparent means of transforming structured and semi-structured data into RDF. In this paper, we present PyRML, a lightweight, Python-native library for building Knowledge Graphs through declarative mappings. PyRML supports core RML constructs and provides a programmable interface for authoring, executing, and testing mappings directly within Python environments. It integrates with popular data and semantic web libraries (e.g., Pandas and RDFlib), enabling transparent and modular workflows. By lowering the barrier to entry for KG creation and fostering reproducible, ontology-aligned data integration, PyRML bridges the gap between declarative semantics and practical KG engineering.