Streamlining Knowledge Graph Creation with PyRML
This work addresses the problem of streamlining KG creation for practitioners in domains like climate science and life sciences, though it appears incremental as it builds on existing mapping languages like RML.
The paper tackles the challenge of creating Knowledge Graphs (KGs) by introducing PyRML, a Python-native library that supports declarative mappings to transform structured and semi-structured data into RDF, enabling more accessible and reproducible KG construction.
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.