Abstract
The tenth version of the BioASQ Challenge will be held as an evaluation Lab within CLEF2022. The motivation driving BioASQ is the continuous advancement of approaches and tools to meet the need for efficient and precise access to the ever-increasing biomedical knowledge. In this direction, a series of annual challenges are organized, in the fields of large-scale biomedical semantic indexing and question answering, formulating specific shared-tasks in alignment with the real needs of the biomedical experts. These shared-tasks and their accompanying benchmark datasets provide an unique common testbed for investigating and comparing new approaches developed by distinct teams around the world for identifying and accessing biomedical information. In particular, the BioASQ Challenge consists of shared-tasks in two complementary directions: (a) the automated indexing of large volumes of unlabelled biomedical documents, primarily scientific publications, with biomedical concepts, (b) the automated retrieval of relevant material for biomedical questions and the generation of comprehensible answers. In the first direction on semantic indexing, two shared-tasks are organized for English and Spanish content respectively, the latter considering human-interpretable evidence extraction (NER and concept linking) as well. In the second direction, two shared-tasks are organized as well, one for biomedical question answering and one particularly focusing on the developing issue of COVID-19. As BioASQ rewards the approaches that manage to outperform the state of the art in these shared-tasks, the research frontier is pushed towards ensuring that the valuable biomedical knowledge will be identifiable and accessible by the biomedical experts.
Keywords
- Biomedical information
- Semantic indexing
- Question answering
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Incorporating values for indexing method in medline/pubmed xml. https://www.nlm.nih.gov/pubs/techbull/ja18/ja18_indexing_method.html. Accessed 01 Sep 2019
Bhatia, K., et al.: The extreme classification repository: multi-label datasets and code (2016). http://manikvarma.org/downloads/XC/XMLRepository.html
Donnelly, K., et al.: Snomed-ct: the advanced terminology and coding system for ehealth. Stud. Health Technol. Inform. 121, 279 (2006)
Gasco, L., et al.: Overview of BioASQ 2021-MESINESP track. Evaluation of advance hierarchical classification techniques for scientific literature, patents and clinical trials. In: CEUR Workshop Proceedings (2021)
Kosmopoulos, A., Partalas, I., Gaussier, E., Paliouras, G., Androutsopoulos, I.: Evaluation measures for hierarchical classification: a unified view and novel approaches. Data Min. Knowl. Discov. 29(3), 820–865 (2014). https://doi.org/10.1007/s10618-014-0382-x
Malakasiotis, P., Pavlopoulos, I., Androutsopoulos, I., Nentidis, A.: Evaluation measures for task b. Technical report. BioASQ (2018). http://participants-area.bioasq.org/Tasks/b/eval_meas_2018
Mork, J., Aronson, A., Demner-Fushman, D.: 12 years on-is the nlm medical text indexer still useful and relevant? J. Biomed. Semant. 8(1), 8 (2017)
Mork, J., Jimeno-Yepes, A., Aronson, A.: The nlm medical text indexer system for indexing biomedical literature (2013)
National Library of Medicine (US): Medical subject headings, vol. 41. US Department of Health and Human Services, Public Health Service, National (2000)
Nentidis, A.A., et al.: Overview of BioASQ 2021: the ninth BioASQ challenge on large-scale biomedical semantic indexing and question answering. In: Candan, K.S., et al. (eds.) CLEF 2021. LNCS, vol. 12880, pp. 239–263. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-85251-1_18
Ngomo, A.C.N., Heino, N., Speck, R., Ermilov, T., Tsatsaronis, G.: Annotation tool. Project deliverable D3.3 (February 2013). http://www.bioasq.org/sites/default/files/PublicDocuments/2013-D3.3-AnnotationTool.pdf
Packer, A.L., et al.: Scielo: uma metodologia para publicação eletrônica. Ciência da informação 27, nd-nd (1998)
Salton, G., Buckley, C.: Improving retrieval performance by relevance feedback. J. Am. Soc. Inf. Sci. 41(4), 288–297 (1990). https://doi.org/10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H
ShafieiBavani, E., Ebrahimi, M., Wong, R., Chen, F.: Summarization evaluation in the absence of human model summaries using the compositionality of word embeddings. In: Proceedings of the 27th International Conference on Computational Linguistics, pp. 905–914. Association for Computational Linguistics, Santa Fe, New Mexico, USA (August 2018). https://www.aclweb.org/anthology/C18-1077
Tsatsaronis, G., et al.: An overview of the bioasq large-scale biomedical semantic indexing and question answering competition. BMC Bioinform. 16, 138 (2015). https://doi.org/10.1186/s12859-015-0564-6
Wang, L.L., et al.: Cord-19: The COVID-19 open research dataset. ArXiv (2020). https://arxiv.org/abs/2004.10706v2
Acknowledgments
Google was a proud sponsor of the BioASQ Challenge in 2021. The tenth edition of BioASQ is also sponsored by Atypon Systems inc. The DisTEMIST task is supported by the Spanish Plan for the Advancement of Language Technologies (Plan TL), the 2020 Proyectos de I+D+i-RTI Tipo A (Descifrando El Papel De Las Profesiones En La Salud De Los Pacientes A Traves De La Mineria De Textos, PID2020-119266RA-I00), and HORIZON-CL4-2021-RESILIENCE-01 (BIOMAT+, 101058779).
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Nentidis, A., Krithara, A., Paliouras, G., Gasco, L., Krallinger, M. (2022). BioASQ at CLEF2022: The Tenth Edition of the Large-scale Biomedical Semantic Indexing and Question Answering Challenge. In: Hagen, M., et al. Advances in Information Retrieval. ECIR 2022. Lecture Notes in Computer Science, vol 13186. Springer, Cham. https://doi.org/10.1007/978-3-030-99739-7_53
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