用Python简化知识图谱构建，让数据转换更透明高效。
Streamlining Knowledge Graph Creation with PyRML
- 基于RML语言设计，支持在Python中直接编写和执行数据映射规则。
- 集成Pandas和RDFlib等库，实现数据处理与语义网技术的无缝衔接。
- 适合需要快速构建可复现知识图谱的研究者和开发者使用。
知识图谱（KGs）在气候科学、文化遗产和生命科学等领域被广泛用于整合异构数据。声明式映射语言如R2RML和RML在实现可扩展、可重用的知识图谱构建中发挥了核心作用，能够将结构化和半结构化数据透明地转换为RDF。本文提出PyRML，一个轻量级的原生Python库，支持通过声明式映射构建知识图谱。PyRML支持核心RML构造，并提供在Python环境中直接编写、执行和测试映射的编程接口。它与流行的数据和语义网库（如Pandas和RDFlib）集成，实现透明且模块化的数据工作流。PyRML降低了知识图谱创建的门槛，促进了与本体对齐、可复现的数据集成，弥合了声明式语义与实际知识图谱工程之间的差距。
原文摘要 · Abstract (English)
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.
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