Bibliografia

[1] R. Johnson, A. Watkinson, and M. Mabe, “The STM report: An overview of scientific and scholarly publishing,” 2018.

[2] H. Alani et al., “Automatic Ontology-Based Knowledge Extraction from Web Documents,” IEEE Intell. Syst., vol. 18, no. 1, 2003, doi: 10.1109/MIS.2003.1179189.

[3] P. Buitelaar, P. Cimiano, S. Racioppa, and M. Siegel, “Ontology-based information extraction with SOBA,” 2006.

[4] M. Craven et al., “Learning to construct knowledge bases from the World Wide Web,” Artif. Intell., vol. 118, no. 1–2, 2000, doi: 10.1016/S0004-3702(00)00004-7.

[5] H. Stoermer, I. Palmisano, D. Redavid, L. Iannone, P. Bouquet, and G. Semeraro, “Contextualization of a RDF knowledge base in the VIKEF project,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2006, vol. 4312 LNCS, doi: 10.1007/11931584_13.

[6] C. Brewster, F. Ciravegna, and Y. Wilks, “User-centred ontology learning for knowledge management,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2002, vol. 2553, doi: 10.1007/3-540-36271-1_18.

[7] and H.-G. P. Sang-Soo Kim, Jeong-Woo Son, Seong-Bae Park, Se-Young Park, Changki Lee, Ji-Hyun Wang, Myung-Gil Jang, “Optima: An ontology population system,” 2008.

[8] N. Weber and P. Buitelaar, “Web-based Ontology Learning with ISOLDE,” Proceeding Work. Web Content Min. with Hum. Lang. Int. Semant. Web Conf., 2006.

[9] P. Buitelaar and M. Sintek, “Ontolt version 1.0: Middleware for ontology extraction from text,” Proc. Demo Sess. Int. Semant. Web Conf., 2004.

[10] F. M. Suchanek, G. Ifrim, and G. Weikum, “LEILA: Learning to Extract Information by Linguistic Analysis,” Comput. Linguist., vol. 0, 2006.

[11] P. Cimiano and J. Völker, “Text2Onto A framework for ontology learning and data-driven change discovery,” in Lecture Notes in Computer Science, 2005, vol. 3513, doi: 10.1007/11428817_21.

[12] O. Etzioni et al., “Web-scale information extraction in knowltAll (preliminary results),” 2004.

[13] E. Drymonas, K. Zervanou, and E. G. M. Petrakis, “Unsupervised ontology acquisition from plain texts: The OntoGain system,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2010, vol. 6177 LNCS, doi: 10.1007/978-3-642-13881-2_29.

[14] D. Faure and T. Poibeau, “First experiments of using semantic knowledge learned by ASIUM for information extraction task using INTEX,” 2000.

[15] S. Castano et al., “Ontology Dynamics with Multimedia Information: The BOEMIE Evolution Methodology,” 2007.

[16] T. Mitchell et al., “Never-ending learning,” Commun. ACM, vol. 61, no. 5, 2018, doi: 10.1145/3191513.
[17] A. Piad-Morffis, Y. Gutiérrez, Y. Almeida-Cruz, and R. Muñoz, “A computational ecosystem to support eHealth Knowledge Discovery technologies in Spanish,” J. Biomed. Inform., vol. 109, 2020, doi: 10.1016/j.jbi.2020.103517.

[18] A. Gonzalez-Agirre, M. Marimon, A. Intxaurrondo, O. Rabal, M. Villegas, and M. Krallinger, “Pharmaconer: Pharmacological substances, compounds and proteins named entity recognition track,” in Proceedings of The 5th Workshop on BioNLP Open Shared Tasks, 2019, pp. 1–10.

[19] A. Miranda-Escalada, E. Farré, and M. Krallinger, “Named Entity Recognition, Concept Normalization and Clinical Coding: Overview of the Cantemist Track for Cancer Text Mining in Spanish, Corpus, Guidelines, Methods and Results.,” IberLEF@ SEPLN, pp. 303– 323, 2020.

[20] S. Lima-López, E. Farré-Maduell, A. Miranda-Escalada, V. Brivá-Iglesias, and M. Krallinger, “NLP applied to occupational health: MEDDOPROF shared task at IberLEF 2021 on automatic recognition, classification and normalization of professions and occupations from medical texts,” 2021.

[21] S. L. Brunton, M. S. Hemati, and K. Taira, “Special issue on machine learning and datadriven methods in fluid dynamics,” Theoretical and Computational Fluid Dynamics, vol. 34, no. 4. 2020, doi: 10.1007/s00162-020-00542-y.

[22] J. R. Oliver, “A Machine-Learning Approach to Automated Negotiation and Prospects for Electronic Commerce,” J. Manag. Inf. Syst., vol. 13, no. 3, 1996, doi: 10.1080/07421222.1996.11518135.

[23] R. Bhardwaj, A. R. Nambiar, and D. Dutta, “A Study of Machine Learning in Healthcare,” in Proceedings – International Computer Software and Applications Conference, 2017, vol. 2, doi: 10.1109/COMPSAC.2017.164.

[24] D. Zhang et al., “The ai index 2021 annual report,” arXiv Prepr. arXiv2103.06312, 2021.

[25] Y. Gutiérrez, D. Tomás, and I. Moreno, “Developing an ontology schema for enriching and linking digital media assets,” Futur. Gener. Comput. Syst., vol. 101, 2019, doi: 10.1016/j.future.2019.06.023.

[26] Y. Gutiérrez, E. Lloret, and J. M. Gómez, “Human language technologies: Key issues for representing knowledge from textual information,” J. Univers. Comput. Sci., vol. 24, no. 11, 2018, doi: 10.3217/jucs-024-11-1651.

[27] E. L. Estevanell-Valladares et al., “Knowledge Discovery in COVID-19 Research Literature,” 2021, doi: 10.26615/978-954-452-072-4_046.

[28] S. Estevez-Velarde, A. Montoyo, Y. Almeida-Cruz, Y. Gutiérrez, A. Piad-Morffis, and R. Muñoz, “Demo application for LETO: Learning engine through ontologies,” in
International Conference Recent Advances in Natural Language Processing, RANLP, 2019, vol. 2019-September, doi: 10.26615/978-954-452-056-4_032.

[29] J. Fernández, F. Llopis, P. Martínez-Barco, Y. Gutiérrez, and Á. Díez, “Analizando opiniones en las redes sociales,” Proces. Leng. Nat., vol. 58, 2017.

[30] J. Fernández, Y. Gutiérrez, J. M. Gomez, and P. Martínez-Barco, “Social rankings: Análisis visual de sentimientos en redes sociales,” Proces. Leng. Nat., vol. 55, pp. 199–202, 2015.

[31] F. Agulló, A. Guillén, Y. Gutiérrez, and P. Martínez-Barco, “ElectionMap: Una representación geolocalizada de intenciones de voto hacia partidos políticos sobre la
base de comentarios de usuarios de Twitter,” Proces. Leng. Nat., vol. 55, pp. 195–198, 2015.

[32] J. Fernández, F. Llopis, Y. Gutiérrez, P. Martínez-Barco, and Á. Díez, “Opinion mining in social networks versus electoral polls,” in International Conference Recent Advances in Natural Language Processing, RANLP, 2017, vol. 2017-September, doi: 10.26615/978-954-452-049-6-032.

[33] A. Guillén, Y. Gutiérrez, and R. Muñoz, “Natural language processing technologies for document profiling,” in International Conference Recent Advances in Natural Language Processing, RANLP, 2017, vol. 2017-September, doi: 10.26615/978-954-452-049-6-039.

[34] Y. Gutiérrez, S. Vázquez, and A. Montoyo, “Spreading semantic information by Word Sense Disambiguation,” Knowledge-Based Syst., vol. 132, 2017, doi: 10.1016/j.knosys.2017.06.013.

[35] A. Nielsen, “Practical Time Series Analysis: Prediction with Statistics & Machine Learning,” Pesquisa Operacional, vol. 21, no. 2. 2020.

[36] N. Aileen, Practical Time Series Analysis Prediction with Statistics & Machine Learning, vol. 21, no. 2. 2019.

[37] A. Pal and P. Prakash, Practical Time Series Analysis: Master Time Series Data Processing, Visualization, and Modeling using Python. 2017.

[38] J. Carletta, “Squibs and Discussions: Assessing Agreement on Classification Tasks: The Kappa Statistic,” Comput. Linguist., vol. 22, no. 2, 1996.

[39] D. M. . POWERS, “Estimation of high affinity estradiol binding sites in human breast cancer EVALUATION: FROM PRECISION, RECALL AND F-MEASURE TO ROC,
INFORMEDNESS, MARKEDNESS & CORRELATION,” J. Mach. Learn. Technol., vol. 2, no. 1, 2011.