Increased discoverability of rare disease datasets through knowledge graph integration
Our methodology enriches datasets with a wealth of additional biological knowledge, improving discoverability. Using condition concepts, we illustrate techniques that could be applied to other entities within source data such as measurements and observations. This work provides a foundational framework for how source data can be modeled to improve accuracy of upstream language models for natural language querying.
Authors
- Ramona Walls (ORCID: https://orcid.org/0000-0001-8815-0078)
- Emily Hartley (ORCID: https://orcid.org/0000-0001-5839-2535)
- Nicolas Matentzoglu
- Nicole Vasilevsky (ORCID: https://orcid.org/0000-0001-5208-3432)
- Kevin Schaper (ORCID: https://orcid.org/0000-0003-3311-7320)
- Ian Braun (ORCID: https://orcid.org/0000-0002-2389-9288)
- Daniel Olson (ORCID: https://orcid.org/0000-0002-8134-1207)
Institutions
- University of North Carolina at Chapel Hill (US)
- Critical Path Institute (US)
- Semantic Designs (United States) (US)
Publication Details
- Journal
- JAMIA Open
- Published
- 2024-12-26
- DOI
- https://doi.org/10.1093/jamiaopen/ooaf001
- Citations
- 1
- Primary Topic
- Biomedical Text Mining and Ontologies
- Type
- article
- Field-Weighted Citation Impact
- 0.23
Funders
- U.S. Department of Health and Human Services
- U.S. Food and Drug Administration