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English Information

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Authors
# Name
1 Leonardo Nascimento(leonardo.nascimento@alvorada.ifrs.edu.br)
2 José Palazzo Oliveira(palazzo@inf.ufrgs.br)

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Reference
# Reference
1 Ali, M., Taha, Z., and Morsey, M. M. (2026). Ontology-grounded knowledge graphs for mitigating hallucinations in large language models for clinical question answering. Journal of Biomedical Informatics, 175:104993.
2 Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020). Language models are few-shot learners. Advances in neural information processing systems, 33:1877–1901.
3 Caufield, J. H., Hegde, H., Emonet, V., Harris, N. L., Joachimiak, M. P., Matentzoglu, N., Kim, H., Moxon, S., Reese, J. T., Haendel, M. A., et al. (2024). Structured prompt interrogation and recursive extraction of semantics (spires): A method for populating knowledge bases using zero-shot learning. Bioinformatics, 40(3):btae104.
4 Ciatto, G., Agiollo, A., Magnini, M., and Omicini, A. (2025). Large language models as oracles for instantiating ontologies with domain-specific knowledge. Knowledge-based systems, 310:112940.
5 Frey, J., Meyer, L.-P., Arndt, N., Brei, F., and Bulert, K. (2023). Benchmarking the abilities of large language models for rdf knowledge graph creation and comprehension: how well do llms speak turtle? In ISWC: Workshop Deep Learning for Knowledge Graphs.
6 Gong, R. and Li, X. (2025). The application progress and research trends of knowledge graphs and large language models in agriculture. Computers and electronics in agriculture, 235:110396.
7 Norouzi, S. S., Barua, A., Christou, A., Gautam, N., Eells, A., Hitzler, P., and Shimizu, C. (2025). Ontology population using llms. In Handbook on Neurosymbolic AI and Knowledge Graphs, pages 421–438. IOS Press.
8 Pan, S., Luo, L., Wang, Y., Chen, C., Wang, J., and Wu, X. (2024). Unifying large language models and knowledge graphs: A roadmap. IEEE Transactions on Knowledge and Data Engineering, 36(7):3580–3599.
9 Wilson, S., Ginige, A., and Goonatilake, J. (2024). Design science research approach for ontology development in agriculture: Utilising advances of llm for automated entity extraction.
10 Zeng, C., Hartmann, T., and Ma, L. (2026). Ontology-based prompting with large language models for inferring construction activities from construction images. Advanced Engineering Informatics, 69:103869.