An approach for collaborative development of a federated biomedical knowledge graph-based question-answering system: Question-of-the-Month challenges

Karamarie Fecho, Chris Bizon, Tursynay Issabekova, Sierra Moxon, Anne E. Thessen, Shervin Abdollahi, Sergio E. Baranzini, Basazin Belhu, William E. Byrd, Lawrence Chung, Andrew Crouse, Marc P. Duby, Stephen Ferguson, Aleksandra Foksinska, Laura Forero, Jennifer Friedman, Vicki Gardner, Gwênlyn Glusman, Jennifer Hadlock, Kristina HanspersEugene Hinderer, Charlotte Hobbs, Gregory Hyde, Sui Huang, David Koslicki, Philip Mease, Sandrine Muller, Christopher J. Mungall, Stephen A. Ramsey, Jared Roach, Irit Rubin, Shepherd H. Schurman, Anath Shalev, Brett Smith, Karthik Soman, Sarah Stemann, Andrew I. Su, Casey Ta, Paul B. Watkins, Mark D. Williams, Chunlei Wu, Colleen H. Xu

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Knowledge graphs have become a common approach for knowledge representation. Yet, the application of graph methodology is elusive due to the sheer number and complexity of knowledge sources. In addition, semantic incompatibilities hinder efforts to harmonize and integrate across these diverse sources. As part of The Biomedical Translator Consortium, we have developed a knowledge graph-based question-answering system designed to augment human reasoning and accelerate translational scientific discovery: the Translator system. We have applied the Translator system to answer biomedical questions in the context of a broad array of diseases and syndromes, including Fanconi anemia, primary ciliary dyskinesia, multiple sclerosis, and others. A variety of collaborative approaches have been used to research and develop the Translator system. One recent approach involved the establishment of a monthly Question-of-the-Month (QotM) Challenge series. Herein, we describe the structure of the QotM Challenge; the six challenges that have been conducted to date on drug-induced liver injury, cannabidiol toxicity, coronavirus infection, diabetes, psoriatic arthritis, and ATP1A3-related phenotypes; the scientific insights that have been gleaned during the challenges; and the technical issues that were identified over the course of the challenges and that can now be addressed to foster further development of the prototype Translator system. We close with a discussion on Large Language Models such as ChatGPT and highlight differences between those models and the Translator system.

Original languageEnglish (US)
Article numbere214
JournalJournal of Clinical and Translational Science
Volume7
Issue number1
DOIs
StatePublished - Sep 14 2023

All Science Journal Classification (ASJC) codes

  • General Medicine

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