{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T16:30:51Z","timestamp":1753893051832,"version":"3.41.2"},"reference-count":34,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,5,4]],"date-time":"2023-05-04T00:00:00Z","timestamp":1683158400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100010676","name":"H2020 Societal Challenges","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100010676","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Res. Metr. Anal."],"abstract":"<jats:p>Classifying scientific publications according to Field-of-Science taxonomies is of crucial importance, powering a wealth of relevant applications including Search Engines, Tools for Scientific Literature, Recommendation Systems, and Science Monitoring. Furthermore, it allows funders, publishers, scholars, companies, and other stakeholders to organize scientific literature more effectively, calculate impact indicators along Science Impact pathways and identify emerging topics that can also facilitate Science, Technology, and Innovation policy-making. As a result, existing classification schemes for scientific publications underpin a large area of research evaluation with several classification schemes currently in use. However, many existing schemes are domain-specific, comprised of few levels of granularity, and require continuous manual work, making it hard to follow the rapidly evolving landscape of science as new research topics emerge. Based on our previous work of scinobo, which incorporates metadata and graph-based publication bibliometric information to assign Field-of-Science fields to scientific publications, we propose a novel hybrid approach by further employing Neural Topic Modeling and Community Detection techniques to dynamically construct a Field-of-Science taxonomy used as the backbone in automatic publication-level Field-of-Science classifiers. Our proposed Field-of-Science taxonomy is based on the OECD fields of research and development (FORD) classification, developed in the framework of the Frascati Manual containing knowledge domains in broad (first level(L1), one-digit) and narrower (second level(L2), two-digit) levels. We create a 3-level hierarchical taxonomy by manually linking Field-of-Science fields of the sciencemetrix Journal classification to the OECD\/FORD level-2 fields. To facilitate a more fine-grained analysis, we extend the aforementioned Field-of-Science taxonomy to level-4 and level-5 fields by employing a pipeline of AI techniques. We evaluate the coherence and the coverage of the Field-of-Science fields for the two additional levels based on synthesis scientific publications in two case studies, in the knowledge domains of Energy and Artificial Intelligence. Our results showcase that the proposed automatically generated Field-of-Science taxonomy captures the dynamics of the two research areas encompassing the underlying structure and the emerging scientific developments.<\/jats:p>","DOI":"10.3389\/frma.2023.1149834","type":"journal-article","created":{"date-parts":[[2023,5,4]],"date-time":"2023-05-04T04:25:57Z","timestamp":1683174357000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["SCINOBO: a novel system classifying scholarly communication in a dynamically constructed hierarchical Field-of-Science taxonomy"],"prefix":"10.3389","volume":"8","author":[{"given":"Sotiris","family":"Kotitsas","sequence":"first","affiliation":[]},{"given":"Dimitris","family":"Pappas","sequence":"additional","affiliation":[]},{"given":"Natalia","family":"Manola","sequence":"additional","affiliation":[]},{"given":"Haris","family":"Papageorgiou","sequence":"additional","affiliation":[]}],"member":"1965","published-online":{"date-parts":[[2023,5,4]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"377","DOI":"10.1162\/qss_a_00019","article-title":"Scopus as a curated, high-quality bibliometric data source for academic research in quantitative science studies","volume":"1","author":"Baas","year":"2020","journal-title":"Quant. Sci. Stud"},{"key":"B2","doi-asserted-by":"publisher","first-page":"363","DOI":"10.1162\/qss_a_00018","article-title":"Web of Science as a data source for research on scientific and scholarly activity","volume":"1","author":"Birkle","year":"2020","journal-title":"Quant. Sci. Stud"},{"key":"B3","doi-asserted-by":"publisher","first-page":"P10008","DOI":"10.1088\/1742-5468\/2008\/10\/P10008","article-title":"Fast unfolding of communities in large networks","volume":"2008","author":"Blondel","year":"2008","journal-title":"J. Stat. Mech. Theory Exp"},{"key":"B4","first-page":"1877","article-title":"Language models are few-shot learners","volume":"33","author":"Brown","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst"},{"key":"B5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.knosys.2018.08.011","article-title":"Experimental explorations on short text topic mining between lda and nmf based schemes","volume":"163","author":"Chen","year":"2019","journal-title":"Knowledge-Based Syst"},{"key":"B6","doi-asserted-by":"publisher","first-page":"1001754","DOI":"10.3389\/frma.2022.1001754","article-title":"A simple, interpretable method to identify surprising topic shifts in scientific fields","volume":"7","author":"Cheng","year":"2022","journal-title":"Front. Res. Metr. Anal"},{"key":"B7","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1162\/qss_a_00106","article-title":"Fine-grained classification of social science journal articles using textual data: a comparison of supervised machine learning approaches","volume":"2","author":"Eykens","year":"2021","journal-title":"Quant. Sci. Stud"},{"key":"B8","doi-asserted-by":"crossref","DOI":"10.1145\/3487553.3524677","article-title":"\u201cScinobo: A hierarchical multi-label classifier of scientific publications,\u201d","volume-title":"Companion Proceedings of the Web Conference 2022, WWW '22","author":"Gialitsis","year":"2022"},{"key":"B9","article-title":"Bertopic: Neural topic modeling with a class-based tf-idf procedure","author":"Grootendorst","year":"2022","journal-title":"arXiv preprint arXiv:2203.05794"},{"key":"B10","doi-asserted-by":"publisher","first-page":"387","DOI":"10.1162\/qss_a_00020","article-title":"Dimensions: Bringing down barriers between scientometricians and data","volume":"1","author":"Herzog","year":"2020","journal-title":"Quant. Sci. Stud"},{"key":"B11","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1145\/3442442.3451361","article-title":"\u201cDeep learning meets knowledge graphs for scholarly data classification,\u201d","volume-title":"Companion Proceedings of the Web Conference 2021, WWW '21","author":"Hoppe","year":"2021"},{"key":"B12","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1080\/17521740701702073","article-title":"Crossref: an overview","volume":"2","author":"Howells","year":"2006","journal-title":"Edit. Bull"},{"key":"B13","doi-asserted-by":"publisher","first-page":"264","DOI":"10.1145\/331499.331504","article-title":"Data clustering: a review","volume":"31","author":"Jain","year":"1999","journal-title":"ACM Computing Surveys (CSUR)"},{"key":"B14","doi-asserted-by":"publisher","DOI":"10.3389\/frma.2020.600382","article-title":"Large scale subject category classification of scholarly papers with deep attentive neural networks","volume":"600382","author":"Kandimalla","year":"2021","journal-title":"Front. Res. Metrics and Analy"},{"volume-title":"Finding groups in Data: An Introduction to Cluster Analysis","year":"2009","author":"Kaufman","key":"B15"},{"key":"B16","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1002\/asi.5090140103","article-title":"Bibliographic coupling between scientific papers","volume":"14","author":"Kessler","year":"1963","journal-title":"Am Document"},{"key":"B17","doi-asserted-by":"publisher","first-page":"984","DOI":"10.1002\/asi.23734","article-title":"Which type of citation analysis generates the most accurate taxonomy of scientific and technical knowledge?","volume":"68","author":"Klavans","year":"2017","journal-title":"J. Assoc. Inf. Syst"},{"key":"B18","doi-asserted-by":"publisher","first-page":"349","DOI":"10.1177\/0165551520962775","article-title":"Analysis of direct citation, co-citation and bibliographic coupling in scientific topic identification","volume":"48","author":"Kleminski","year":"2022","journal-title":"J. Inf. Sci"},{"key":"B19","doi-asserted-by":"publisher","first-page":"396","DOI":"10.1162\/qss_a_00021","article-title":"Microsoft academic graph: when experts are not enough","volume":"1","author":"Kuansan","year":"2020","journal-title":"Quant. Sci. Stud"},{"key":"B20","doi-asserted-by":"publisher","first-page":"683","DOI":"10.1109\/ICISCE.2016.151","article-title":"\u201cTextrank algorithm by exploiting wikipedia for short text keywords extraction,\u201d","author":"Li","year":"2016","journal-title":"2016 3rd International Conference on Information Science and Control Engineering (ICISCE)"},{"journal-title":"The Openaire Research Graph Data Model","year":"2019","author":"Manghi","key":"B21"},{"volume-title":"Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities.","year":"2015","key":"B22"},{"key":"B23","doi-asserted-by":"publisher","first-page":"428","DOI":"10.1162\/qss_a_00023","article-title":"OpenCitations, an infrastructure organization for open scholarship","volume":"1","author":"Peroni","year":"2020","journal-title":"Quant. Sci. Stud"},{"key":"B24","article-title":"Studies in scientometrics i transience and continuance in scientific authorship","author":"Price","year":"1975","journal-title":"Ci\u0142ncia da Informao"},{"key":"B25","first-page":"2515","article-title":"\u201cShort-text domain specific key terms\/phrases extraction using an n-gram model with wikipedia,\u201d","volume-title":"Proceedings of the 21st ACM International Conference on Information and Knowledge Management, CIKM '12","author":"Qureshi","year":"2012"},{"key":"B26","doi-asserted-by":"crossref","first-page":"3982","DOI":"10.18653\/v1\/D19-1410","article-title":"\u201cSentence-BERT: Sentence embeddings using Siamese BERT-networks,\u201d","volume-title":"Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)","author":"Reimers","year":"2019"},{"key":"B27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0251493","article-title":"Article-level classification of scientific publications: a comparison of deep learning, direct citation and bibliographic coupling","volume":"16","author":"Rivest","year":"2021","journal-title":"PLoS ONE"},{"key":"B28","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1007\/978-3-030-00668-6_12","article-title":"\u201cThe computer science ontology: A large-scale taxonomy of research areas,\u201d","volume-title":"The Semantic Web-ISWC 2018","author":"Salatino","year":"2018"},{"key":"B29","doi-asserted-by":"publisher","first-page":"87","DOI":"10.18653\/v1\/P18-4015","author":"Shen","year":"2018","journal-title":"A Web-Scale System for Scientific Knowledge Exploration"},{"key":"B30","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1162\/qss_a_00004","article-title":"Granularity of algorithmically constructed publication-level classifications of research publications: identification of specialties","volume":"1","author":"Sjgrde","year":"2020","journal-title":"Quant. Sci. Stud"},{"key":"B31","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1002\/asi.4630240406","article-title":"Co-citation in the scientific literature: A new measure of the relationship between two documents","volume":"24","author":"Small","year":"1973","journal-title":"J. Am. Soc. Inf. Sci"},{"key":"B32","doi-asserted-by":"publisher","first-page":"2378","DOI":"10.1002\/asi.22748","article-title":"A new methodology for constructing a publication-level classification system of science","volume":"63","author":"Waltman","year":"2012","journal-title":"J. Am. Soc. Inf. Sci"},{"key":"B33","doi-asserted-by":"publisher","first-page":"153","DOI":"10.3233\/WEB-150318","article-title":"Dikea: exploiting wikipedia for keyphrase extraction","volume":"13","author":"Wang","year":"2015","journal-title":"Web Intell"},{"key":"B34","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1803.09000","article-title":"Wikirank: Improving keyphrase extraction based on background knowledge","author":"Yu","year":"2018","journal-title":"ArXiv"}],"container-title":["Frontiers in Research Metrics and Analytics"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/frma.2023.1149834\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,4]],"date-time":"2023-05-04T04:26:07Z","timestamp":1683174367000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/frma.2023.1149834\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,4]]},"references-count":34,"alternative-id":["10.3389\/frma.2023.1149834"],"URL":"https:\/\/doi.org\/10.3389\/frma.2023.1149834","relation":{},"ISSN":["2504-0537"],"issn-type":[{"type":"electronic","value":"2504-0537"}],"subject":[],"published":{"date-parts":[[2023,5,4]]},"article-number":"1149834"}}