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Assessing the effectiveness of ontology-grounded AI term extraction using OntoGPT for environmental evidence synthesis

Abstract Evidence syntheses are valuable sources of robust and transparent knowledge that can identify gaps in research and inform evidence-based decision making. However, the process of synthesis is time consuming and costly. We investigate a new AI-based method that uses a large-language model (LL...

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Principais autores: Ryan Y. Hodgson, Steven A. Robinson, Amélie C. Boutin, Felix K. Chan, Joseph R. Bennett, Rachel T. Buxton, J. Harry Caufield, Dalal E. L. Hanna, Tim Alamenciak
Format: Artigo
Sprog:Inglês
Udgivet: BMC 2026-02-01
Serier:Environmental Evidence
Fag:
Evidence-based conservation
LLM
Structured data extraction
Ontology
Restoration ecology
Online adgang:https://doi.org/10.1186/s13750-026-00381-0
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