Streamlining Knowledge Graph Creation with PyRML

TL;DR

PyRML is a Python library that simplifies the creation of Knowledge Graphs by supporting declarative mappings, integrating with common data tools, and enabling easier, reproducible KG development.

Contribution

PyRML introduces a lightweight, Python-native tool that supports core RML constructs, facilitating accessible and modular Knowledge Graph construction within Python environments.

Findings

Supports core RML constructs in Python

Integrates with Pandas and RDFlib for workflow transparency

Reduces barriers to Knowledge Graph creation

Abstract

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.