Busca e Recuperação de Workflows em Repositórios por meio de Transformers e Modelagem de Tópicos
Resumo
Diversos repositórios disponibilizam workflows previamente modelados para reúso e adaptação, dada a complexidade inerente à modelagem de um workflow. Embora esses repositórios ofereçam mecanismos de rotulação, nem sempre tais rótulos são preenchidos e, quando o são, os valores informados acabam limitando a busca. Um modo alternativo de realizar a busca nesses repositórios é utilizar as descrições em linguagem natural dos workflows, em vez de se limitar à busca por rótulos ou à comparação estrutural dos workflows. Este artigo apresenta a abordagem Athena++, que utiliza técnicas de processamento de linguagem natural para realizar a busca por workflows em repositórios, em especial o uso de Transformers e Modelagem de Tópicos. A Athena++ foi avaliada com um conjunto de workflows obtidos no repositório do Galaxy, e os resultados se mostraram promissores.
Palavras-chave:
workflows científicos, recuperação de workflows, transformers, modelagem de tópicos
Referências
Blankenberg, D. et al. (2014). Dissemination of scientific software with galaxy toolshed. Genome Biology, 15(2):403.
Blei, D. M. (2012). Probabilistic topic models. Commun. of the ACM, 55(4):77–84.
Costa, F. et al. (2012). Athena: text mining based discovery of scientific workflows in disperse repositories. In RED 2010, Paris, France, pages 104–121. Springer.
de Oliveira, D., Liu, J., and Pacitti, E. (2019). Data-Intensive Workflow Management: For Clouds and Data-Intensive and Scalable Computing Environments. Morgan & Claypool.
Dias, L. G. et al. (2024). Maestro: a lightweight ontology-based framework for composing and analyzing script-based scientific experiments. Knowledge and Information Systems.
Goble, C. A. et al. (2010). myexperiment: a repository and social network for the sharing of bioinformatics workflows. Nucleic Acids Res., 38:677–682.
Grootendorst, M. (2022). Bertopic: Neural topic modeling with a class-based tf-idf procedure. CoRR, abs/2203.05794.
Gu, Y., Cao, J., Qian, S., and Guan, W. (2023). Sworts: a scientific workflow retrieval approach by learning textual and structural semantics. IEEE Trans. on Services Computing.
Reimers, N. and Gurevych, I. (2019). Sentence-bert: Sentence embeddings using siamese bert-networks. pages 3980–3990.
Silva, V. et al. (2011). Similarity-based workflow clustering. Journal of Computational Interdisciplinary Sciences, 2(1):23–35.
Souza, F., Nogueira, R., and Lotufo, R. (2020). Bertimbau: Pretrained bert models for brazilian portuguese. In Intelligent Systems, pages 403–417, Cham. Springer International.
Starlinger, J. et al. (2016). Effective and efficient similarity search in scientific workflow repositories. Future Generation Computer Systems, 56:584–594.
Thirunavukarasu, A. J., Ting, D. S. J., Elangovan, K., Gutierrez, L., Tan, T. F., and Ting, D. S. W. (2023). Large language models in medicine. Nature medicine, 29(8):1930–1940.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems, 30.
Zhou, Z., Cheng, Z., Zhang, L.-J., Gaaloul, W., and Ning, K. (2018). Scientific workflow clustering and recommendation leveraging layer hierarchical analysis. IEEE Transactions on Services Computing, 11(1):169–183.
Blei, D. M. (2012). Probabilistic topic models. Commun. of the ACM, 55(4):77–84.
Costa, F. et al. (2012). Athena: text mining based discovery of scientific workflows in disperse repositories. In RED 2010, Paris, France, pages 104–121. Springer.
de Oliveira, D., Liu, J., and Pacitti, E. (2019). Data-Intensive Workflow Management: For Clouds and Data-Intensive and Scalable Computing Environments. Morgan & Claypool.
Dias, L. G. et al. (2024). Maestro: a lightweight ontology-based framework for composing and analyzing script-based scientific experiments. Knowledge and Information Systems.
Goble, C. A. et al. (2010). myexperiment: a repository and social network for the sharing of bioinformatics workflows. Nucleic Acids Res., 38:677–682.
Grootendorst, M. (2022). Bertopic: Neural topic modeling with a class-based tf-idf procedure. CoRR, abs/2203.05794.
Gu, Y., Cao, J., Qian, S., and Guan, W. (2023). Sworts: a scientific workflow retrieval approach by learning textual and structural semantics. IEEE Trans. on Services Computing.
Reimers, N. and Gurevych, I. (2019). Sentence-bert: Sentence embeddings using siamese bert-networks. pages 3980–3990.
Silva, V. et al. (2011). Similarity-based workflow clustering. Journal of Computational Interdisciplinary Sciences, 2(1):23–35.
Souza, F., Nogueira, R., and Lotufo, R. (2020). Bertimbau: Pretrained bert models for brazilian portuguese. In Intelligent Systems, pages 403–417, Cham. Springer International.
Starlinger, J. et al. (2016). Effective and efficient similarity search in scientific workflow repositories. Future Generation Computer Systems, 56:584–594.
Thirunavukarasu, A. J., Ting, D. S. J., Elangovan, K., Gutierrez, L., Tan, T. F., and Ting, D. S. W. (2023). Large language models in medicine. Nature medicine, 29(8):1930–1940.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems, 30.
Zhou, Z., Cheng, Z., Zhang, L.-J., Gaaloul, W., and Ning, K. (2018). Scientific workflow clustering and recommendation leveraging layer hierarchical analysis. IEEE Transactions on Services Computing, 11(1):169–183.
Publicado
14/10/2024
Como Citar
OLIVEIRA, Lyncoln S.; AMORIM, Annie; LAGE, Marcos; PAES, Aline; OLIVEIRA, Daniel de.
Busca e Recuperação de Workflows em Repositórios por meio de Transformers e Modelagem de Tópicos. In: BRAZILIAN E-SCIENCE WORKSHOP (BRESCI), 18. , 2024, Florianópolis/SC.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 40-47.
ISSN 2763-8774.
DOI: https://doi.org/10.5753/bresci.2024.243907.
