构建符合BFO标准的化学研究知识图谱，助力智能发现与推理。
AI4DiTraRe: Building the BFO-Compliant Chemotion Knowledge Graph
- 基于语义管道将化学实验数据转为符合BFO的图结构。
- 支持FAIR原则，实现可发现、可访问、可互操作的化学数据整合。
- 适合化学信息学、AI辅助科研及数字科研转型的研究者。
化学研究涉及多层级、复杂且交错的实验流程，常需结合化学结构信息以理解科学过程。Chemotion作为电子实验记录本和数据存储库，已成为众多化学家的重要工具。本文提出一种语义管道，用于构建符合基本形式本体（BFO）标准的Chemotion知识图谱（Chemotion-KG），实现化学研究数据的统一、本体驱动表示。通过Chemotion API以JSON-LD格式获取实验元数据，转换为RDF，并利用SPARQL CONSTRUCT查询映射至符合BFO的图结构。该知识图谱遵循FAIR原则，支持人工智能驱动的科学发现与推理。源代码与数据集已公开于GitHub，知识图谱由弗劳恩霍夫信息工程服务（FIZ Karlsruhe）托管。研究成果出自莱布尼茨科学校区“研究数字化转型”（DiTraRe）项目，是跨学科合作的持续成果。
原文摘要 · Abstract (English)
Chemistry is an example of a discipline where the advancements of technology have led to multi-level and often tangled and tricky processes ongoing in the lab. The repeatedly complex workflows are combined with information from chemical structures, which are essential to understand the scientific process. An important tool for many chemists is Chemotion, which consists of an electronic lab notebook and a repository. This paper introduces a semantic pipeline for constructing the BFO-compliant Chemotion Knowledge Graph, providing an integrated, ontology-driven representation of chemical research data. The Chemotion-KG has been developed to adhere to the FAIR (Findable, Accessible, Interoperable, Reusable) principles and to support AI-driven discovery and reasoning in chemistry. Experimental metadata were harvested from the Chemotion API in JSON-LD format, converted into RDF, and subsequently transformed into a Basic Formal Ontology-aligned graph through SPARQL CONSTRUCT queries. The source code and datasets are publicly available via GitHub. The Chemotion Knowledge Graph is hosted by FIZ Karlsruhe Information Service Engineering. Outcomes presented in this work were achieved within the Leibniz Science Campus ``Digital Transformation of Research'' (DiTraRe) and are part of an ongoing interdisciplinary collaboration.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。