{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T23:13:51Z","timestamp":1778022831364,"version":"3.51.4"},"reference-count":64,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,1,6]],"date-time":"2023-01-06T00:00:00Z","timestamp":1672963200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Grant Agency of the Czech Technical University in Prague","award":["SGS20\/208\/OHK3\/3T\/18"],"award-info":[{"award-number":["SGS20\/208\/OHK3\/3T\/18"]}]},{"name":"Grant Agency of the Czech Technical University in Prague","award":["NFDI2\/12020"],"award-info":[{"award-number":["NFDI2\/12020"]}]},{"name":"Grant Agency of the Czech Technical University in Prague","award":["467401796"],"award-info":[{"award-number":["467401796"]}]},{"DOI":"10.13039\/501100001659","name":"German Research Foundation (DFG)","doi-asserted-by":"publisher","award":["SGS20\/208\/OHK3\/3T\/18"],"award-info":[{"award-number":["SGS20\/208\/OHK3\/3T\/18"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001659","name":"German Research Foundation (DFG)","doi-asserted-by":"publisher","award":["NFDI2\/12020"],"award-info":[{"award-number":["NFDI2\/12020"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001659","name":"German Research Foundation (DFG)","doi-asserted-by":"publisher","award":["467401796"],"award-info":[{"award-number":["467401796"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>The paper presents a machine-learning based approach to text-to-ontology mapping. We explore a possibility of matching texts to the relevant ontologies using a combination of artificial neural networks and classifiers. Ontologies are formal specifications of the shared conceptualizations of application domains. While describing the same domain, different ontologies might be created by different domain experts. To enhance the reasoning and data handling of concepts in scientific papers, finding the best fitting ontology regarding description of the concepts contained in a text corpus. The approach presented in this work attempts to solve this by selection of a representative text paragraph from a set of scientific papers, which are used as data set. Then, using a pre-trained and fine-tuned Transformer, the paragraph is embedded into a vector space. Finally, the embedded vector becomes classified with respect to its relevance regarding a selected target ontology. To construct representative embeddings, we experiment with different training pipelines for natural language processing models. Those embeddings in turn are later used in the task of matching text to ontology. Finally, the result is assessed by compressing and visualizing the latent space and exploring the mappings between text fragments from a database and the set of chosen ontologies. To confirm the differences in behavior of the proposed ontology mapper models, we test five statistical hypotheses about their relative performance on ontology classification. To categorize the output from the Transformer, different classifiers are considered. These classifiers are, in detail, the Support Vector Machine (SVM), k-Nearest Neighbor, Gaussian Process, Random Forest, and Multilayer Perceptron. Application of these classifiers in a domain of scientific texts concerning catalysis research and respective ontologies, the suitability of the classifiers is evaluated, where the best result was achieved by the SVM classifier.<\/jats:p>","DOI":"10.3390\/computers12010014","type":"journal-article","created":{"date-parts":[[2023,1,9]],"date-time":"2023-01-09T02:31:30Z","timestamp":1673231490000},"page":"14","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Text-to-Ontology Mapping via Natural Language Processing with Application to Search for Relevant Ontologies in Catalysis"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4071-0360","authenticated-orcid":false,"given":"Luk\u00e1\u0161","family":"Korel","sequence":"first","affiliation":[{"name":"Faculty of Information Technology, Czech Technical University, 16636 Prague, Czech Republic"}]},{"given":"Uladzislau","family":"Yorsh","sequence":"additional","affiliation":[{"name":"Faculty of Mathematics and Physics, Charles University, 16636 Prague, Czech Republic"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4620-8248","authenticated-orcid":false,"given":"Alexander S.","family":"Behr","sequence":"additional","affiliation":[{"name":"Faculty of Biochemical and Chemical Engineering, TU Dortmund University, 44227 Dortmund, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8852-3812","authenticated-orcid":false,"given":"Norbert","family":"Kockmann","sequence":"additional","affiliation":[{"name":"Faculty of Biochemical and Chemical Engineering, TU Dortmund University, 44227 Dortmund, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2536-9328","authenticated-orcid":false,"given":"Martin","family":"Hole\u0148a","sequence":"additional","affiliation":[{"name":"Institute of Computer Science, Czech Academy of Sciences, 16636 Prague, Czech Republic"},{"name":"Leibniz-Institut f\u00fcr Katalyse e.V., Albert-Einstein-Str. 29a, 18059 Rostock, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"160018","DOI":"10.1038\/sdata.2016.18","article-title":"The FAIR Guiding Principles for scientific data management and stewardship","volume":"3","author":"Wilkinson","year":"2016","journal-title":"Sci. Data"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3223","DOI":"10.1002\/cctc.202001974","article-title":"A Unified Research Data Infrastructure for Catalysis Research\u2014Challenges and Concepts","volume":"13","author":"Wulf","year":"2021","journal-title":"ChemCatChem"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1006\/knac.1993.1008","article-title":"A translation approach to portable ontology specifications","volume":"5","author":"Gruber","year":"1993","journal-title":"Knowl. Acquis."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"852","DOI":"10.1002\/cite.202100177","article-title":"From Coiled Flow Inverter to Stirred Tank Reactor\u2014Bioprocess Development and Ontology Design","volume":"94","author":"Behr","year":"2022","journal-title":"Chem. Ing. Tech."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Pozanenko, A., Stupnikov, S., Thalheim, B., Mendez, E., and Kiselyova, N. (2022). Interoperability and Architecture Requirements Analysis and Metadata Standardization for a Research Data Infrastructure in Catalysis. Proceedings of the Data Analytics and Management in Data Intensive Domains, Springer International Publishing.","DOI":"10.1007\/978-3-031-12285-9"},{"key":"ref_6","unstructured":"Fensel, D. (2011). Ontologies: A Silver Bullet for Knowledge Management and Electronic Commerce, Springer. [2nd ed.]."},{"key":"ref_7","unstructured":"Guarino, N. (1998, January 6\u20138). Formal Ontology and Information Systems. Proceedings of the FOIS\u201998 Conference, Trento, Italy."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1186\/s13326-017-0128-y","article-title":"NCBO Ontology Recommender 2.0: An enhanced approach for biomedical ontology recommendation","volume":"8","author":"Jonquet","year":"2017","journal-title":"J. Biomed. Semant."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Al-Aswadi, F., Chan, H., and Gan, K. (2021, January 21\u201322). Extracting Semantic Concepts and Relations from Scientific Publications by Using Deep Learning. Proceedings of the IRICT 2020, Langkawi, Malaysia.","DOI":"10.1007\/978-3-030-70713-2_35"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Gupta, N., Podder, S., Annervaz, K., and Sengupta, S. (2016, January 18\u201320). Domain Ontology Induction Using Word Embeddings. Proceedings of the ICMLA, Anaheim, CA, USA.","DOI":"10.1109\/ICMLA.2016.0027"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Katyshev, A., Anikin, A., Denisov, M., and Petrova, T. (2021, January 25\u201326). Intelligent Approaches for the Automated Domain Ontology Extraction. Proceedings of the International Congress on Information and Communication Technology, London, UK.","DOI":"10.1007\/978-981-15-5856-6_41"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13326-019-0218-0","article-title":"Combining Lexical and Context Features for Automatic Ontology Extension","volume":"11","author":"Althubaiti","year":"2020","journal-title":"J. Biomed. Semant."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Espinoza-Anke, L., Ronzano, F., and Saggion, H. (2015, January 14\u201320). Hypernym Extraction: Combining Machine-Learning and Dependency Grammar. Proceedings of the CICLing, Cairo, Egypt.","DOI":"10.1007\/978-3-319-18111-0_28"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Martel, F., and Zouaq, A. (2021, January 22\u201326). Taxonomy Extraction Using Knowledge Graph Embeddings and Hierarchical Clustering. Proceedings of the SAC\u201921, Virtual.","DOI":"10.1145\/3412841.3441959"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Navarro-Almanza, R., Ju\u00e1rez-Ram\u00edrez, R., Licea, G., and Castro, J.R. (2020). Automated Ontology Extraction from Unstructured Texts using Deep Learning. Intuitionistic and Type-2 Fuzzy Logic Enhancements in Neural and Optimization Algorithms: Theory and Applications, Springer.","DOI":"10.1007\/978-3-030-35445-9_50"},{"key":"ref_16","unstructured":"Bento, A., Zouaq, A., and Gagnon, M. (2020, January 11\u201316). Ontology Matching Using Convolutional Neural Networks. Proceedings of the LREC, Marseille, France."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Chakraborty, J., Yaman, B., Virgili, L., Konar, K., and Bansal, S. (2020, January 2). OntoConnect: Results for OAEI 2020. Proceedings of the OM ISWC, Virtual.","DOI":"10.1145\/3412841.3442059"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Hao, J., Lei, C., Efthymiou, V., Quamar, A., \u00d6zcan, F., Sun, Y., and Wang, W. (2021, January 14\u201318). MEDTO: Medical Data to Ontology Matching Using Hybrid Graph Neural Networks. Proceedings of the KDD\u201921, Virtual.","DOI":"10.1145\/3447548.3467138"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Wu, J., Lv, J., Guo, H., and Ma, S. (2020). Daeom: A Deep Attentional Embedding Approach for Biomedical Ontology Matching. Appl. Sci., 10.","DOI":"10.3390\/app10217909"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3914","DOI":"10.1016\/j.proeng.2012.01.594","article-title":"Using ART2 Neural Network and Bayesian Network for Automating the Ontology Constructing Process","volume":"29","author":"Hourali","year":"2012","journal-title":"Procedia Eng."},{"key":"ref_21","unstructured":"Mercier, C., Chateau-Laurent, H., Alexandre, F., and Vi\u00e9ville, T. (2021, January 8\u20139). Ontology as Neuronal-Space Manifold: Towards Symbolic and Numerical Artificial Embedding. Proceedings of the Workshop on Knowledge Representation for Hybrid and Compositional AI, Virtual."},{"key":"ref_22","unstructured":"Kolozali, S., Fazekas, G., Barthet, M., and Sandler, M. (2014, January 9\u201312). A Framework for Automatic Ontology Generation Based on Semantic Audio analysis. Proceedings of the Audio Engineering Society International Conference, Los Angeles, CA, USA."},{"key":"ref_23","first-page":"1","article-title":"CNN Based Ontology Learning Algorithm and Applied in PE Data","volume":"48","author":"Li","year":"2021","journal-title":"IAENG Int. J. Comput. Sci."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Mueller, R., and Abdullaev, S. (2019, January 8\u201311). Deep Cause: Hypothesis Extraction from Information Systems Papers with Deep Learning for Theory Ontology Learning. Proceedings of the Annual Hawaii International Conference on System Sciences, Maui, HI, USA.","DOI":"10.24251\/HICSS.2019.752"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.websem.2018.10.002","article-title":"Expressive Ontology Learning as Neural Machine Translation","volume":"52\u201353","author":"Petrucci","year":"2018","journal-title":"J. Web Semant."},{"key":"ref_26","first-page":"1481","article-title":"Learning OWL 2 Property Characteristics as an Explanation for an RNN","volume":"68","author":"Potoniec","year":"2020","journal-title":"Bull. Pol. Acad. Sci. Tech. Sci."},{"key":"ref_27","unstructured":"Memariani, A., Glauer, M., Neuhaus, F., Mossakowski, T., and Hatings, J. (June, January 29). Automated and Explainable Ontology Extension Based on Deep Learning: A Case Study in the Chemical Domain. Proceedings of the 3rd International Workshop on Data Meets Applied Ontologies, Hersonissos, Greece."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Oba, A., Paik, I., and Kuwana, A. (2021, January 7\u20139). Automatic Classification for Ontology Generation by Pretrained Language Model. Proceedings of the International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, London, UK.","DOI":"10.1007\/978-3-030-79457-6_18"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Teslya, N., and Savosin, S. (2019, January 1\u20134). Matching Ontologies with Word2Vec-Based Neural Network. Proceedings of the ICCSA, Saint Petersburg, Russia.","DOI":"10.1007\/978-3-030-24289-3_55"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Ristoski, P., and Paulheim, H. (2016, January 17\u201321). Rdf2vec: Rdf Graph Embeddings for Data Mining. Proceedings of the International Semantic Web Conference, Kobe, Japan.","DOI":"10.1007\/978-3-319-46523-4_30"},{"key":"ref_31","unstructured":"Ritchie, A., Chen, J., Castro, L., Rebholz-Schuhmann, D., and Jim\u00e9nez-Ruiz, E. (2021, January 6\u201310). Ontology Clustering with OWL2Vec. Proceedings of the DeepOntoNLP, Hersonissos, Greece."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Petrucci, G., Ghindini, C., and Rospocher, M. (2016, January 19\u201323). Ontology Learning in the Deep. Proceedings of the EKAW, Bologna, Italy.","DOI":"10.1007\/978-3-319-49004-5_31"},{"key":"ref_33","unstructured":"Hirschman, L., Krallinger, M., Valencia, A., Fluck, J., Mevissen, H.T., Dach, H., Oster, M., and Hofmann-Apitius, M. (2007, January 23\u201325). ProMiner: Recognition of Human Gene and Protein Names using regularly updated Dictionaries. Proceedings of the Second BioCreAtIvE Challenge Evaluation Workshop, Madrid, Spain."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"S3","DOI":"10.1186\/gb-2008-9-s2-s3","article-title":"Overview of BioCreative II gene normalization","volume":"9","author":"Morgan","year":"2008","journal-title":"Genome Biol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2909","DOI":"10.1093\/bioinformatics\/btt474","article-title":"DNorm: Disease name normalization with pairwise learning to rank","volume":"29","author":"Leaman","year":"2013","journal-title":"Bioinformatics"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Karadeniz, \u0130., and \u00d6zg\u00fcr, A. (2019). Linking entities through an ontology using word embeddings and syntactic re-ranking. BMC Bioinform., 20.","DOI":"10.1186\/s12859-019-2678-8"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Bojanowski, P., Grave, E., Joulin, A., and Mikolov, T. (2016). Enriching Word Vectors with Subword Information. arXiv.","DOI":"10.1162\/tacl_a_00051"},{"key":"ref_38","unstructured":"Devlin, J., Chang, M.W., Lee, K., and Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), Association for Computational Linguistics."},{"key":"ref_39","unstructured":"Liu, Z., Jiang, F., Hu, Y., Shi, C., and Fung, P. (2021). NER-BERT: A Pre-trained Model for Low-Resource Entity Tagging. CoRR."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Lu, K., Grover, A., Abbeel, P., and Mordatch, I. (2021). Pretrained Transformers as Universal Computation Engines. CoRR.","DOI":"10.1609\/aaai.v36i7.20729"},{"key":"ref_41","unstructured":"Group, O.W. (2023, January 04). OWL. Available online: https:\/\/www.w3.org\/OWL\/."},{"key":"ref_42","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_43","unstructured":"Ho, T.K. (1995, January 14\u201316). Random decision forests. Proceedings of the 3rd International Conference on Document Analysis and Recognition, Montreal, QC, Canada."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Sch\u00f6lkopf, B., Smola, A.J., and Bach, F. (2002). Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, MIT Press.","DOI":"10.7551\/mitpress\/4175.001.0001"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Rasmussen, C.E., and Williams, C.K.I. (2005). Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning), The MIT Press.","DOI":"10.7551\/mitpress\/3206.001.0001"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Kramer, O. (2013). K-Nearest Neighbors. Dimensionality Reduction with Unsupervised Nearest Neighbors, Springer.","DOI":"10.1007\/978-3-642-38652-7"},{"key":"ref_47","unstructured":"Vang-Mata, R. (2020). Multilayer Perceptrons: Theory and Applications, Nova Science Publishers."},{"key":"ref_48","unstructured":"Benvenuto, M., and Plauman, H. (2021). Industrial Catalysis, De Gruyter STEM, De Gruyter."},{"key":"ref_49","first-page":"915","article-title":"Technology vision 2020: The U.S. chemical industry","volume":"Volume 72","author":"Schneider","year":"1998","journal-title":"Air Pollution in the 21st Century"},{"key":"ref_50","unstructured":"National Cancer Institue (2021, December 01). National Cancer Institue Thesaurus, 2022, Available online: https:\/\/ncit.nci.nih.gov."},{"key":"ref_51","unstructured":"Batchelor, C. (2021, December 01). Chemical Methods Ontology. Available online: http:\/\/purl.obolibrary.org\/obo\/chmo.owl."},{"key":"ref_52","unstructured":"Allotrope Foundation (2021, December 01). Allotrope Foundation Ontology, 2022. Available online: https:\/\/www.allotrope.org\/ontologies."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"D1214","DOI":"10.1093\/nar\/gkv1031","article-title":"ChEBI in 2016: Improved services and an expanding collection of metabolites","volume":"44","author":"Hastings","year":"2015","journal-title":"Nucleic Acids Res."},{"key":"ref_54","unstructured":"Nguen, T., Karr, J., and Sheriff, R. (2022, December 12). Systems Biology Ontology. Available online: http:\/\/biomodels.net\/SBO\/."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1194","DOI":"10.1021\/acs.jcim.9b00995","article-title":"Inorganic Materials Synthesis Planning with Literature-Trained Neural Networks","volume":"60","author":"Kim","year":"2020","journal-title":"J. Chem. Inf. Model."},{"key":"ref_56","unstructured":"Company, R.A. (2022, November 21). BERT for Chemical Industry. Available online: https:\/\/huggingface.co\/recobo\/chemical-bert-uncased."},{"key":"ref_57","unstructured":"Hugging Face (2022, November 21). BERT. Available online: https:\/\/huggingface.co\/docs\/transformers\/model_doc\/bert."},{"key":"ref_58","unstructured":"Honnibal, M., and Montani, I. (2022, November 21). SpaCy 2: Natural Language Understanding with Bloom Embeddings, Convolutional Neural Networks and Incremental Parsing. Available online: https:\/\/spacy.io\/."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Neumann, M., King, D., Beltagy, I., and Ammar, W. (2019). ScispaCy: Fast and Robust Models for Biomedical Natural Language Processing. Proceedings of the 18th BioNLP Workshop and Shared Task, Association for Computational Linguistics.","DOI":"10.18653\/v1\/W19-5034"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"McInnes, L., Healy, J., and Melville, J. (2018). UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. arXiv.","DOI":"10.21105\/joss.00861"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"861","DOI":"10.21105\/joss.00861","article-title":"UMAP: Uniform Manifold Approximation and Projection","volume":"3","author":"McInnes","year":"2018","journal-title":"J. Open Source Softw."},{"key":"ref_62","unstructured":"Gal, Y. (2016). Uncertainty in Deep Learning. [Ph.D. Thesis, University of Cambridge]."},{"key":"ref_63","first-page":"1","article-title":"Should We Really Use Post-Hoc Tests Based on Mean-Ranks?","volume":"17","author":"Benavoli","year":"2016","journal-title":"J. Mach. Learn. Res."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1825","DOI":"10.1017\/pds.2022.185","article-title":"Generative Pre-Trained Transformer for Design Concept Generation: An Exploration","volume":"2","author":"Zhu","year":"2022","journal-title":"Proc. Des. Soc."}],"container-title":["Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-431X\/12\/1\/14\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:01:31Z","timestamp":1760119291000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-431X\/12\/1\/14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,6]]},"references-count":64,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["computers12010014"],"URL":"https:\/\/doi.org\/10.3390\/computers12010014","relation":{},"ISSN":["2073-431X"],"issn-type":[{"value":"2073-431X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,6]]}}}