LinkML: An Open Data Modeling Framework
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Abstract (arXiv)
Scientific research relies on well-structured, standardized data; however, much of it is stored in formats such as free-text lab notebooks, non-standardized spreadsheets, or data repositories. This lack of structure challenges interoperability, making data integration, validation, and reuse difficult. LinkML (Linked Data Modeling Language) is an open framework that simplifies the process of authoring, validating, and sharing data. LinkML can describe a range of data structures, from flat, list-based models to complex, interrelated, and normalized models that utilize polymorphism and compound inheritance. It offers an approachable syntax that is not tied to any one technical architecture and can be integrated seamlessly with many existing frameworks. The LinkML syntax provides a standard way to describe schemas, classes, and relationships, allowing modelers to build well-defined, stable, and optionally ontology-aligned data structures. Once defined, LinkML schemas may be imported into other LinkML schemas. These key features make LinkML an accessible platform for interdisciplinary collaboration and a reliable way to define and share data semantics. LinkML helps reduce heterogeneity, complexity, and the proliferation of single-use data models while simultaneously enabling compliance with FAIR data standards. LinkML has seen increasing adoption in various fields, including biology, chemistry, biomedicine, microbiome research, finance, electrical engineering, transportation, and commercial software development. In short, LinkML makes implicit models explicitly computable and allows data to be standardized at its origin. LinkML documentation and code are available at linkml.io.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Nov 2025.
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Citing papers per year: 2026: 6.
6 papers.
- 2026PREFER: An Ontology for the PREcision FERmentation Communitycited by 0
- 2026The Semantic Ladder: A Framework for Progressive Formalization of Natural Language Content for Knowledge Graphs and AI Systemscited by 0
- 2026Building AI-Ready Data Systems for Space Life Sciences, Aerospace Medicine, and Deep Space Explorationcited by 0
- 2026PULSE: An Executable Contract Language for Spatiotemporal Knowledge Graph Engineeringcited by 0
- 2026SchemaLink: An Intelligent Web Editor for LinkML Schema Curationcited by 0
- 2026Agents for Everyone: A Workshop Framework for Building Agentic AI Capabilities in a Distributed Curation Communitycited by 0
- 2026Agents for Everyone: A Workshop Framework for Building Agentic AI Capabilities in a Distributed Curation Community27 Aug 2026
- 2026SchemaLink: An Intelligent Web Editor for LinkML Schema Curation12 Aug 2026
- 2026PULSE: An Executable Contract Language for Spatiotemporal Knowledge Graph Engineering26 Jul 2026
- 2026Building AI-Ready Data Systems for Space Life Sciences, Aerospace Medicine, and Deep Space Exploration27 Jun 2026
- 2026The Semantic Ladder: A Framework for Progressive Formalization of Natural Language Content for Knowledge Graphs and AI Systems23 Mar 2026
- 2026PREFER: An Ontology for the PREcision FERmentation Community18 Feb 2026