Constructing Knowledge Graphs from Text Using Large Language Models: Scoping Review
Resumo
The use of Large Language Models (LLMs) for Knowledge Graph (KG) construction has gained significant traction, yet the field lacks methodological standardization. This study conducts a scope review following the PRISMA framework to map existing techniques and categorize them into four main approaches: (i) RDF-Based; (ii) Prompt-Based; (iii) RAG-Based; (iv) Hybrid Pipelines. Analyzing 126 primary studies, we identify key benefits such as scalability and automation, alongside challenges like low precision and manual curation. Our findings highlight research art-state.
Palavras-chave:
Knowledge Graph, Large Language Model, Scoping Review, Automatic Knowledge Graphs Construction
Referências
Alammar, J. and Grootendorst, M. (2024). Hands-On Large Language Models. O’Reilly Media.
Berryman, J. and Ziegler, A. (2024). Prompt Engineering for LLMs: The Art and Science of Building Large Language Model–Based Applications. ”O’Reilly Media, Inc.”.
Institute, J. B. (2015). The Joanna Briggs Institute Reviewers’ Manual 2015: Methodology for JBI Scoping Reviews. Adelaide, Australia.
Kejriwal, M., Knoblock, C. A., and Szekely, P. (2021). Knowledge graphs: Fundamentals, techniques, and applications. MIT Press.
Li, Z., Xu, T., Wang, W., Wu, H., Xiong, F., Chen, E., Lyu, Y., Niu, S., Liu, H., and Tang, B. (2024). Crud-rag: A comprehensive chinese benchmark for retrieval-augmented generation of large language models. ACM Transactions on Information Systems, 43:1 – 32.
Ma, Y., Chen, Z., and Church, K. (2021). Emerging trends: A gentle introduction to fine-tuning. Natural Language Engineering, 27:763 – 778.
Muscolino, H., Machado, A., Vesset, D., and Rydning, J. (2023). Untapped value: What every executive needs to know about unstructured data. White paper, IDC, Framingham, MA. Sponsored by Box.
Nandigam, J., Patil, R., Boit, S., and Gudivada, V. (2023). A survey of text representation and embedding techniques in nlp. IEEE Access, 11:36120–36146.
Negro, A., Kus, V., Futia, G., and Montagna, F. (2022). Knowledge graphs applied. Manning.
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., et al. (2021). The prisma 2020 statement: an updated guideline for reporting systematic reviews. bmj, 372.
Zhong, L., Wu, J., Li, Q., Peng, H., and Wu, X. (2023). A comprehensive survey on automatic knowledge graph construction. ACM Computing Surveys, 56(4):1–62.
Berryman, J. and Ziegler, A. (2024). Prompt Engineering for LLMs: The Art and Science of Building Large Language Model–Based Applications. ”O’Reilly Media, Inc.”.
Institute, J. B. (2015). The Joanna Briggs Institute Reviewers’ Manual 2015: Methodology for JBI Scoping Reviews. Adelaide, Australia.
Kejriwal, M., Knoblock, C. A., and Szekely, P. (2021). Knowledge graphs: Fundamentals, techniques, and applications. MIT Press.
Li, Z., Xu, T., Wang, W., Wu, H., Xiong, F., Chen, E., Lyu, Y., Niu, S., Liu, H., and Tang, B. (2024). Crud-rag: A comprehensive chinese benchmark for retrieval-augmented generation of large language models. ACM Transactions on Information Systems, 43:1 – 32.
Ma, Y., Chen, Z., and Church, K. (2021). Emerging trends: A gentle introduction to fine-tuning. Natural Language Engineering, 27:763 – 778.
Muscolino, H., Machado, A., Vesset, D., and Rydning, J. (2023). Untapped value: What every executive needs to know about unstructured data. White paper, IDC, Framingham, MA. Sponsored by Box.
Nandigam, J., Patil, R., Boit, S., and Gudivada, V. (2023). A survey of text representation and embedding techniques in nlp. IEEE Access, 11:36120–36146.
Negro, A., Kus, V., Futia, G., and Montagna, F. (2022). Knowledge graphs applied. Manning.
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., et al. (2021). The prisma 2020 statement: an updated guideline for reporting systematic reviews. bmj, 372.
Zhong, L., Wu, J., Li, Q., Peng, H., and Wu, X. (2023). A comprehensive survey on automatic knowledge graph construction. ACM Computing Surveys, 56(4):1–62.
Publicado
29/09/2025
Como Citar
BORGES BOTTINO, Giovanna; DE JESÚS PÉREZ ALCÁZAR, José.
Constructing Knowledge Graphs from Text Using Large Language Models: Scoping Review. In: LLMS, ANÁLISE DE GRAFOS E ONTOLOGIAS (LAGO) - SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 40. , 2025, Fortaleza/CE.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 382-392.
DOI: https://doi.org/10.5753/sbbd_estendido.2025.247977.
