Scientific workflows increasingly generate structured JSON data that is easy to exchange but difficult to interpret consistently across systems due to lacking semantic interoperability. While JSON Schema ensures structural validation, it provides no native support for Linked Data semantics. This paper presents an RDF Authoring View extending the open-source JSON Schema editor MetaConfigurator, enabling researchers to transform existing JSON, YAML, or CSV data into RDF using AI-assisted RML mappings, refine triples, execute SPARQL queries, visualize knowledge graphs, and export RDF serializations within a single integrated web interface. This workflow is supported by ontology-aware IRI auto-completion, bidirectional synchronization between JSON-LD text views and RDF triple tables, and AI-assisted SPARQL query generation from natural language hints. We demonstrate the workflow using laboratory data from metal-organic framework (MOF) synthesis experiments. Protocol data describing reagents, procedure steps, and quantities is converted from JSON to ontology-based JSON-LD via RML mappings. We then refine the semantic representation, query relationships between experimental conditions and outcomes, and explore the resulting knowledge graph interactively. This integrated environment bridges conventional structured data management with Semantic Web technologies while preserving experimental context and lowering technical barriers through AI assistance.

翻译：科学工作流日益生成结构化JSON数据，这些数据虽易于交换，但因缺乏语义互操作性而难以在不同系统间形成一致解读。尽管JSON Schema可确保结构验证，但它并不原生支持关联数据语义。本文提出一种RDF创作视图，该视图扩展了开源JSON Schema编辑器MetaConfigurator，使研究人员能够通过AI辅助的RML映射将现有JSON、YAML或CSV数据转换为RDF，并在单一集成Web界面中完成三元组精炼、SPARQL查询执行、知识图谱可视化以及RDF序列化导出。此工作流程由以下功能支撑：本体感知的IRI自动补全、JSON-LD文本视图与RDF三元组表的双向同步、以及基于自然语言提示的AI辅助SPARQL查询生成。我们利用金属有机框架（MOF）合成实验的实验室数据演示了该工作流程。描述试剂、操作步骤及用量的协议数据通过RML映射从JSON转换为基于本体的JSON-LD。随后，我们对语义表示进行精炼，查询实验条件与结果之间的关系，并交互式探索生成的知识图谱。该集成环境将传统结构化数据管理与语义网技术相衔接，同时通过AI辅助手段保留实验上下文并降低技术门槛。