Relation Extraction from Unstructured Species Descriptions Using TaxonNERD and Llama 7B

Ontologies have become essential tools in knowledge representation, particularly in organizing information on taxonomy, ecology, and inter-species relationships. Their integration with Natural Language Processing (NLP) methods, such as Named Entity Recognition (NER) and Relation Extraction (RE), has significantly advanced the extraction of relevant ecological data from scientific texts. This research explores the fusion of ontological frameworks and NLP methodologies, focusing on animal feeding relationships. Using a dataset from the National Biodiversity Institute (INBio), we applied TaxoNERD for entity extraction, followed by fine-tuning the LLaMA2-7b model to analyze complex feeding behaviors and predator-prey interactions. Our results show a recall of 0.73 and a precision of 0.68, highlighting the model's effectiveness in capturing feeding relationships. These structured insights into species' diets offer valuable contributions to ecological research and conservation efforts.