Advancing Agricultural Knowledge Systems: Leveraging Ontology Matching, Query Expansion, and Synonym Substitution with Large Language Models
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 chall...
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| Formato: | tesis doctoral |
| Fecha de publicación: | 2025 |
| País: | España |
| Recursos: | Universidad de Santiago de Compostela (USC) |
| Repositorio: | Minerva. Repositorio Institucional de la Universidad de Santiago de Compostela |
| Idioma: | inglés |
| OAI Identifier: | oai:minerva.usc.gal:10347/45334 |
| Acesso em linha: | https://hdl.handle.net/10347/45334 |
| Access Level: | acceso abierto |
| Palavra-chave: | Agricultural Knowledge Systems Ontology Matching Query Expansion Synonym Substitution Large Language Models 120304 Inteligencia artificial |
| Resumo: | 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 |
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