The Linked Data Modeling Language (LinkML): A General-Purpose Data Modeling Framework Grounded in Machine-Readable Semantics

Sierra Moxon, Harold Solbrig, Deepak Unni, Dazhi Jiao, Richard Bruskiewich, James Balhoff, Gaurav Vaidya, William Duncan, Harshad Hegde, Mark Miller, Matthew Brush, Nomi Harris, Melissa Haendel, Christopher Mungall

Research output: Contribution to journalConference articlepeer-review

Abstract

Data integration is a major challenge in the life sciences, due to heterogeneity, complexity, the proliferation of ad-hoc formats and data structures, and poor compliance with FAIR guidelines. The Linked data Modeling Language (LinkML, https://linkml.github.io) is an object-oriented data modeling framework that aims to bring semantic web standards to the masses, simplifying the production of FAIR ontology-ready data. It can be used for schematizing a variety of kinds of data, ranging from simple flat checklist-style standards to complex interrelated normalized data utilizing polymorphism/inheritance. Although it is still a young and evolving standard, LinkML is already in use across a wide variety of projects with different applications including cancer data harmonization, environmental genomics, and knowledge graph integration.

Original languageEnglish (US)
Pages (from-to)148-151
Number of pages4
JournalCEUR Workshop Proceedings
Volume3073
StatePublished - 2021
Externally publishedYes
Event2021 International Conference on Biomedical Ontologies, ICBO 2021 - Bozen-Bolzano, Italy
Duration: Sep 16 2021Sep 18 2021

Keywords

  • JSON-schema
  • Ontology
  • RDF
  • Semantic web

ASJC Scopus subject areas

  • Computer Science(all)

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