Advancing Agricultural Knowledge Systems: leveraging Ontology Matching, Query Expansion, and Synonym Substitution with Large Language Models

• Autores: Mohammed Arideh
• Directores de la Tesis: María Jesús Taboada Iglesias (dir. tes.)
• Lectura: En la Universidade de Santiago de Compostela ( España ) en 2025
• Idioma: inglés
• Enlaces
  • Tesis en acceso abierto en: MINERVA
• Resumen

  • This PhD research introduces a comprehensive framework for enhancing agricultural knowledge systems by integrating pretrained language models PLMs and large language models LLMs with advanced techniques such as ontology matching query expansion and synonym substitution The work addresses major challenges in agricultural text mining particularly the complexity of domain specific terminology and the limitations of traditional annotators Key components of the proposed framework include 1 ZeroShot Prompting for Fine Grained Annotation Utilizes AGROVOC subgraphs and AgricultureBERT to annotate texts in the domain of animal welfare without prior training data The method achieved up to 84 F1score and enables contextrich precise entity recognition 2 Query Expansion and Synonym Generation Enhances AGROVOC by automatically generating and validating multiword synonyms through hierarchical relationships and semantic filtering using AgricultureBERT