Streamlining Knowledge Graph Creation with PyRML

2025-05-27

AI Summary

To address high barriers to knowledge graph (KG) construction, opaque workflows, challenges in ontology alignment, and poor reproducibility, this paper introduces PyRML—the first native Python RML mapping execution engine. PyRML tightly integrates declarative RML semantics with software engineering best practices, enabling unified mapping definition, execution, and unit testing. Leveraging Pandas and RDFlib, it supports programmable, reusable, and verifiable transformation of structured and semi-structured data into RDF. Its modular architecture enforces ontology constraints and ensures workflow reproducibility. Experimental evaluation demonstrates that PyRML significantly reduces KG construction complexity, improves mapping development efficiency and maintainability, and validates transparent, ontology-aligned, and reusable data integration capabilities in domains including climate science and cultural heritage.

Technology Category

• Knowledge Representation and Reasoning: Ontologies
• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Machine Learning: Graph-based Machine Learning

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

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.

Problem

• Simplifying Knowledge Graph creation using Python-native tools
• Enhancing RML-based mapping for scalable data integration
• Bridging declarative semantics with practical KG engineering

Innovation

• Python-native library for declarative KG mappings
• Supports core RML constructs and Python integration
• Enables modular workflows with popular data libraries