LinkML – A Linked Open Data Modeling Language

Chris MungallHarold Solbrig and Chris Mungall

Recorded: Wednesday April. 14, 2021
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LinkML is a modeling language and collection of tools that joins the data processing world with the semantic, making it possible for developers to continue to employ tools such as JSON, YAML, SQL, spreadsheets, etc., while _directly_ working with RDF and ontology based semantics. LinkML leverages and integrates technologies such as JSON-LD, Knowledge Graphs, RDF and Shape Expressions (ShEx), and provides an environment where semantics and data are seamlessly integrated and structural and representational transformations between communities can be based on the combination of RDF and ontology.

Why LinkML? Twenty years ago, Tim Berners-Lee coined the term “The Semantic Web” in a seminal article in Scientific American. While it is heartening to see how much of his vision has been realized, gaps still remain. Berners-Lee’s vision included the notion that the “Semantic Web is not a separate Web but an _extension_ of the current one, in which information is given well-defined meaning…”. While progress has been made in some areas (e.g., the world of data and the world of semantics still exist in separate spaces. While tools and techniques exist to _transform_ (lift) data into the RDF space, to date these environments have been separate. LinkML helps to bridge this gap.

This webinar provides a short introduction to the LinkML modeling language and tools: how it came to be, what it can be used for today, how it is being used in biomedical research, and where the developers intend to take it in the future.

About the Speakers

Harold Solbrig is an active contributor to healthcare information modeling, semantics and standards-based information exchange for 40+ years. He has served in multiple roles in the ISO, W3C, HL7 and other standards communities. He has a master’s degree in Software Engineering from Oxford University and is an assistant professor at Johns Hopkins University.

Chris Mungall is Department Head of Biosystems Data Science at Lawrence Berkeley National Laboratory, working on the application of computational techniques to problems life sciences of relevance to the health of humans and the health of the planet. His main interest is the application of artificial intelligence, knowledge-based methods, and bio-curation to advance our understanding of the interconnected role of genes and genetic mechanisms in key biological processes.