Investigando uma abordagem baseada em aprendizado em contexto para geração de consultas SPARQL
Resumo
Interagir com grafos de conhecimento geralmente requer familiaridade com alguma linguagem formal de consulta. Para tornar esse processo mais acessível, sistemas de perguntas e respostas baseados em grafos de conhecimento buscam permitir consultas a partir de perguntas em linguagem natural. Uma forma de projetar esses sistemas envolve a tradução de perguntas em linguagem natural para uma linguagem formal de consulta em grafos, como o SPARQL. Este estudo tem como objetivo avaliar o uso de aprendizagem em contexto na geração de consultas SPARQL, mais especificamente contribuir com uma análise empírica dos resultados provenientes da variação de certos parâmetros experimentais.Referências
Agarwal, P., Kumar, N., and Bedathur, S. (2024). Symkgqa: few-shot knowledge graph question answering via symbolic program generation and execution. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 10119–10140.
Håkansson, A. and Phillips-Wren, G. (2024). Generative ai and large language models-benefits, drawbacks, future and recommendations. Procedia Computer Science, 246:5458–5468.
Hogan, A., Blomqvist, E., Cochez, M., d’Amato, C., Melo, G. D., Gutierrez, C., Kirrane, S., Gayo, J. E. L., Navigli, R., Neumaier, S., et al. (2021). Knowledge graphs. ACM Computing Surveys (Csur), 54(4):1–37.
Meta (2024). Meta-llama-3-70b-instruct. Accessed: 2025-04-15.
Papineni, K., Roukos, S., Ward, T., and Zhu, W.-J. (2002). Bleu: a method for automatic evaluation of machine translation. In Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pages 311–318.
Trivedi, P., Maheshwari, G., Dubey, M., and Lehmann, J. (2017). Lc-quad: A corpus for complex question answering over knowledge graphs. In The Semantic Web–ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part II 16, pages 210–218. Springer.
Wang, P., Jiang, H., Xu, J., and Zhang, Q. (2019). Knowledge graph construction and applications for web search and beyond. Data Intelligence, 1(4):333–349.
Xie, X., Wang, J., Han, Y., and Li, W. (2024). Knowledge graph-based in-context learning for advanced fault diagnosis in sensor networks. Sensors, 24(24):8086.
Yang, Y., Huang, C., Xia, L., and Li, C. (2022). Knowledge graph contrastive learning for recommendation. In Proceedings of the 45th international ACM SIGIR conference on research and development in information retrieval, pages 1434–1443.
Håkansson, A. and Phillips-Wren, G. (2024). Generative ai and large language models-benefits, drawbacks, future and recommendations. Procedia Computer Science, 246:5458–5468.
Hogan, A., Blomqvist, E., Cochez, M., d’Amato, C., Melo, G. D., Gutierrez, C., Kirrane, S., Gayo, J. E. L., Navigli, R., Neumaier, S., et al. (2021). Knowledge graphs. ACM Computing Surveys (Csur), 54(4):1–37.
Meta (2024). Meta-llama-3-70b-instruct. Accessed: 2025-04-15.
Papineni, K., Roukos, S., Ward, T., and Zhu, W.-J. (2002). Bleu: a method for automatic evaluation of machine translation. In Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pages 311–318.
Trivedi, P., Maheshwari, G., Dubey, M., and Lehmann, J. (2017). Lc-quad: A corpus for complex question answering over knowledge graphs. In The Semantic Web–ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part II 16, pages 210–218. Springer.
Wang, P., Jiang, H., Xu, J., and Zhang, Q. (2019). Knowledge graph construction and applications for web search and beyond. Data Intelligence, 1(4):333–349.
Xie, X., Wang, J., Han, Y., and Li, W. (2024). Knowledge graph-based in-context learning for advanced fault diagnosis in sensor networks. Sensors, 24(24):8086.
Yang, Y., Huang, C., Xia, L., and Li, C. (2022). Knowledge graph contrastive learning for recommendation. In Proceedings of the 45th international ACM SIGIR conference on research and development in information retrieval, pages 1434–1443.
Publicado
12/11/2025
Como Citar
FERREIRA, Carlos Eduardo A.; CARBONERA, Joel L..
Investigando uma abordagem baseada em aprendizado em contexto para geração de consultas SPARQL. In: ESCOLA REGIONAL DE APRENDIZADO DE MÁQUINA E INTELIGÊNCIA ARTIFICIAL DA REGIÃO SUL (ERAMIA-RS), 1. , 2025, Porto Alegre/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
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p. 1-4.
DOI: https://doi.org/10.5753/eramiars.2025.16265.
