Evidence map›Paper›PMID 40538705›Full record

ArticleCEUR workshop proceedings2024

Analyzing Llama 3-based Approach for Axiom Translation from Ontologies.

Xubing Hao, Licong Cui, Cui Tao, Kirk Roberts, Muhammad Amith

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Article in CEUR workshop proceedings, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Xubing HaoMcWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston.
Licong CuiMcWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston.
Cui TaoDepartment of Artificial Intelligence and Informatics, Mayo Clinic.
Kirk RobertsMcWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston.
Muhammad AmithDepartment of Biostatistics and Data Science, University of Texas Medical Branch.

Funding

ACCELERATE-BASSO: Coordinating Center for Accelerating Behavioral and Social Science through OntologyU24AG088019 · NIA · UNIVERSITY OF FLORIDA · PI Jiang Bian, Mark A Musen · 2024 to 2026
$2.3M
Standardizing and Harmonizing Behavioral and Social Science Research Factors in Alzheimer's Disease through Ontology-Based ApproachesU01AG088076 · NIA · MAYO CLINIC JACKSONVILLE · PI Jiang Bian, Cui Tao · 2024 to 2026
$2.3M
Trustworthy and High-Performance Question Answering for Electronic Health RecordsR01LM014508 · NLM · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI Kirk Edward Roberts · 2024 to 2026
$1.1M
Transforming motivational interviewing into a computable model for automated patient diabetic counseling.R21DK134815 · NIDDK · UNIVERSITY OF TEXAS MED BR GALVESTON · PI MUHAMMAD FAHEEM AMITH · 2024 to 2026
$621k
NIA NIH HHS U01 AG088076NIA NIH HHS U24 AG088019NIDDK NIH HHS R21 DK134815NLM NIH HHS R01 LM014508
6 · The paper itself

Abstract

Ontology development involves a top-down approach where ontology engineers and domain experts collaboratively define and evaluate ontological elements and axioms. Translating ontology axioms into natural language can significantly aid in ontology evaluation by making the content more understandable to subject matter experts who may lack a background in knowledge engineering. In this preliminary study, we investigate the potential of large language models (LLMs) in axiom translation from ontologies to facilitate ontology evaluation. We utilize Llama 3 to translate 1,192 ontology axioms across 19 distinct axiom types from five published ontologies. Results show that 163 (13.67%) of the Llama 3 translation of the axiom are accurately represented, 268 (22.48%) are not accurately represented, and 761 (63.84%) are partially accurate. Our manual evaluation of the Llama 3 translation indicates some competency in producing hierarchical natural language equivalents while revealing some limitations when translating complex axioms. Nonetheless, there are opportunities to improve the results with few-shot training or using LLMs to provide support in knowledge engineering for ontologies.

Identifiers

PMID40538705
PMCPMC12178606

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.