{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T20:57:57Z","timestamp":1777409877410,"version":"3.51.4"},"reference-count":51,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2025,1,12]],"date-time":"2025-01-12T00:00:00Z","timestamp":1736640000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"AIARD (Artificial Intelligence Aided Research and Development) industrial chair"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MAKE"],"abstract":"<jats:p>Interdisciplinary research (IDR) is essential for addressing complex global challenges that surpass the capabilities of any single discipline. However, measuring interdisciplinarity remains challenging due to conceptual ambiguities and inconsistent methodologies. To overcome these challenges, we propose a deep learning approach that quantifies interdisciplinarity in scientific articles through semantic analysis of titles and abstracts. Utilizing the Semantic Scholar Open Research Corpus (S2ORC), we leveraged metadata field tags to categorize papers as either interdisciplinary or monodisciplinary, establishing the foundation for supervised learning in our model. Specifically, we preprocessed the textual data and employed a Text Convolutional Neural Network (Text CNN) architecture to identify semantic patterns indicative of interdisciplinarity. Our model achieved an F1 score of 0.82, surpassing baseline machine learning models. By directly analyzing semantic content and incorporating metadata for training, our method addresses the limitations of previous approaches that rely solely on bibliometric features such as citations and co-authorship. Furthermore, our large-scale analysis of 136 million abstracts revealed that approximately 25% of the literature within the specified disciplines is interdisciplinary. Additionally, we outline how our quantification method can be integrated into a TRIZ-based (Theory of Inventive Problem Solving) methodological framework for cross-disciplinary innovation, providing a foundation for systematic knowledge transfer and inventive problem solving across domains. Overall, this approach not only offers a scalable measurement of interdisciplinarity but also contributes to a framework for facilitating innovation through structured cross-domain knowledge integration.<\/jats:p>","DOI":"10.3390\/make7010007","type":"journal-article","created":{"date-parts":[[2025,1,13]],"date-time":"2025-01-13T07:40:08Z","timestamp":1736754008000},"page":"7","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Quantifying Interdisciplinarity in Scientific Articles Using Deep Learning Toward a TRIZ-Based Framework for Cross-Disciplinary Innovation"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9999-0125","authenticated-orcid":false,"given":"Nicolas","family":"Douard","sequence":"first","affiliation":[{"name":"National Institute of Applied Sciences (INSA), University of Strasbourg, 24 Boulevard de la Victoire, 67000 Strasbourg, France"},{"name":"Department of Electrical and Computer Engineering, Manhattan University, 3825 Corlear Ave, Riverdale, NY 10463, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1612-3465","authenticated-orcid":false,"given":"Ahmed","family":"Samet","sequence":"additional","affiliation":[{"name":"National Institute of Applied Sciences (INSA), University of Strasbourg, 24 Boulevard de la Victoire, 67000 Strasbourg, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5294-7728","authenticated-orcid":false,"given":"George","family":"Giakos","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Manhattan University, 3825 Corlear Ave, Riverdale, NY 10463, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1815-5601","authenticated-orcid":false,"given":"Denis","family":"Cavallucci","sequence":"additional","affiliation":[{"name":"National Institute of Applied Sciences (INSA), University of Strasbourg, 24 Boulevard de la Victoire, 67000 Strasbourg, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,1,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1038\/525306a","article-title":"Interdisciplinary research by the numbers","volume":"525","year":"2015","journal-title":"Nature"},{"key":"ref_2","first-page":"14","article-title":"Approaches to understanding and measuring interdisciplinary scientific research (IDR): A review of the literature","volume":"5","author":"Wagner","year":"2011","journal-title":"J. Inf."},{"key":"ref_3","unstructured":"National Academy of Sciences, National Academy of Engineering, and Institute of Medicine (2005). Facilitating Interdisciplinary Research, The National Academies Press."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1093\/nar\/28.1.27","article-title":"KEGG: Kyoto Encyclopedia of Genes and Genomes","volume":"28","author":"Kanehisa","year":"2000","journal-title":"Nucleic Acids Res."},{"key":"ref_5","unstructured":"Allen, D.T., and Shonnard, D.R. (2011). Sustainable Engineering: Concepts, Design and Case Studies, Prentice Hall."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"681","DOI":"10.1108\/OIR-12-2016-0361","article-title":"Measuring the interdisciplinarity of Big Data research: A longitudinal study","volume":"42","author":"Hu","year":"2018","journal-title":"Online Inf. Rev."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"633","DOI":"10.1007\/s11192-007-0305-3","article-title":"How cross-disciplinary is bionanotechnology? Explorations in the specialty of molecular motors","volume":"70","author":"Rafols","year":"2007","journal-title":"Scientometrics"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1162\/qss_a_00011","article-title":"Consistency and validity of interdisciplinarity measures","volume":"1","author":"Wang","year":"2018","journal-title":"Quant. Sci. Stud."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"187","DOI":"10.3152\/147154406781775841","article-title":"Interdisciplinary research: Meaning, metrics and nurture","volume":"15","author":"Porter","year":"2006","journal-title":"Res. Eval."},{"key":"ref_10","first-page":"87","article-title":"Indicators of the Interdisciplinarity of Journals: Diversity, Centrality, and Citations","volume":"5","author":"Leydesdorff","year":"2011","journal-title":"J. Inf."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1237","DOI":"10.1002\/asi.10326","article-title":"Interdisciplinarity in science: A tentative typology of disciplines and research areas","volume":"54","author":"Morillo","year":"2003","journal-title":"J. Assoc. Inf. Sci. Technol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1007\/s11192-007-1700-5","article-title":"Measuring researcher interdisciplinarity","volume":"72","author":"Porter","year":"2007","journal-title":"Scientometrics"},{"key":"ref_13","doi-asserted-by":"crossref","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":"ref_14","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1002\/j.1538-7305.1948.tb00917.x","article-title":"A mathematical theory of communication","volume":"27","author":"Shannon","year":"1948","journal-title":"Bell Syst. Tech. J."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"688","DOI":"10.1038\/163688a0","article-title":"Measurement of Diversity","volume":"163","author":"Simpson","year":"1949","journal-title":"Nature"},{"key":"ref_16","first-page":"761","article-title":"The Paternity of an Index","volume":"54","author":"Hirschman","year":"1964","journal-title":"Am. Econ. Rev."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1038\/s41586-019-1335-8","article-title":"Unsupervised word embeddings capture latent knowledge from materials science literature","volume":"571","author":"Tshitoyan","year":"2019","journal-title":"Nature"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"604","DOI":"10.1109\/TNNLS.2020.2979670","article-title":"A Survey of the Usages of Deep Learning for Natural Language Processing","volume":"32","author":"Otter","year":"2020","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1145\/2133806.2133826","article-title":"Probabilistic topic models","volume":"55","author":"Blei","year":"2012","journal-title":"Commun. ACM"},{"key":"ref_20","unstructured":"Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2019, January 2\u20137). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of the 17th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT 2019), Minneapolis, MN, USA."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Kim, Y. (2014, January 25\u201329). Convolutional Neural Networks for Sentence Classification. Proceedings of the Conference on Empirical Methods in Natural Language Processing 2014, Doha, Qatar.","DOI":"10.3115\/v1\/D14-1181"},{"key":"ref_22","unstructured":"Altshuller, G.S., and Shulyak, L.A. (1996). And Suddenly the Inventor Appeared: TRIZ, the Theory of Inventive Problem Solving, Technical Innovation Center, Inc."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Douard, N., Samet, A., Giakos, G.C., and Cavallucci, D. (2022, January 27\u201329). Bridging Two Different Domains to Pair Their Inherent Problem-Solution Text Contents: Applications to Quantum Sensing and Biology. Proceedings of the TRIZ Future Conference 2022, Warsaw, Poland.","DOI":"10.1007\/978-3-031-17288-5_6"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Douard, N., Samet, A., Giakos, G.C., and Cavallucci, D. (2023, January 12\u201314). Navigating the Knowledge Network: How Inter-Domain Information Pairing and Generative AI Can Enable Rapid Problem-Solving. Proceedings of the TRIZ Future Conference 2023, Offenburg University, Offenburg, Germany.","DOI":"10.1007\/978-3-031-42532-5_11"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"707","DOI":"10.1098\/rsif.2007.0213","article-title":"A general framework for analysing diversity in science, technology and society","volume":"4","author":"Stirling","year":"2007","journal-title":"J. R. Soc. Interface"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1162\/posc_e_00315","article-title":"Investigating Interdisciplinary Practice: Methodological Challenges (Introduction)","volume":"27","author":"MacLeod","year":"2019","journal-title":"Perspect. Sci."},{"key":"ref_27","first-page":"9","article-title":"Capturing Interdisciplinarity in Academic Abstracts","volume":"22","author":"Nanni","year":"2016","journal-title":"D-lib Mag."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"61995","DOI":"10.1109\/ACCESS.2023.3287935","article-title":"A Metadata-Based Approach for Research Discipline Prediction Using Machine Learning Techniques and Distance Metrics","volume":"11","author":"Pham","year":"2023","journal-title":"IEEE Access"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Beltagy, I., Lo, K., and Cohan, A. (2019, January 3\u20137). SciBERT: A Pretrained Language Model for Scientific Text. Proceedings of the Conference on Empirical Methods in Natural Language Processing 2019, Hong Kong, China.","DOI":"10.18653\/v1\/D19-1371"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy function approximation: A gradient boosting machine","volume":"29","author":"Friedman","year":"2001","journal-title":"Ann. Stat."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-Vector Networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/s10994-006-6226-1","article-title":"Extremely randomized trees","volume":"63","author":"Geurts","year":"2006","journal-title":"Mach. Learn."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Lo, K., Wang, L.L., Neumann, M., Kinney, R.M., and Weld, D.S. (2020, January 5\u201310). S2ORC: The Semantic Scholar Open Research Corpus. Proceedings of the Annual Meeting of the Association for Computational Linguistics 2020, Online.","DOI":"10.18653\/v1\/2020.acl-main.447"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Sennrich, R., Haddow, B., and Birch, A. (2015). Neural Machine Translation of Rare Words with Subword Units. arXiv.","DOI":"10.18653\/v1\/P16-1162"},{"key":"ref_36","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A Method for Stochastic Optimization. arXiv."},{"key":"ref_37","first-page":"281","article-title":"Random Search for Hyper-Parameter Optimization","volume":"13","author":"Bergstra","year":"2012","journal-title":"J. Mach. Learn. Res."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Manaswi, N. (2018). Understanding and Working with Keras. Deep Learning with Applications Using Python: Chatbots and Face, Object, and Speech Recognition with TensorFlow and Keras, Springer.","DOI":"10.1007\/978-1-4842-3516-4_2"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Chicco, D., and Jurman, G. (2020). The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genom., 21.","DOI":"10.1186\/s12864-019-6413-7"},{"key":"ref_40","unstructured":"Grootendorst, M.R. (2022). BERTopic: Neural topic modeling with a class-based TF-IDF procedure. arXiv."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Li, Z., Hui, J., Yang, P., and Mao, H. (2022). Microfluidic Organ-on-a-Chip System for Disease Modeling and Drug Development. Biosensors, 12.","DOI":"10.3390\/bios12060370"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1098\/rsif.2006.0127","article-title":"Biomimetics: Its practice and theory","volume":"3","author":"Vincent","year":"2006","journal-title":"J. R. Soc. Interface"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"3213","DOI":"10.1016\/j.matpr.2022.04.217","article-title":"Optimization of Cutting Parameters During CNC Milling of EN24 Steel with Tungsten Carbide Coated Inserts: A Critical Review","volume":"62","author":"Patil","year":"2022","journal-title":"Mater. Today Proc."},{"key":"ref_44","unstructured":"Kitada, S. (2023). Improving Prediction Performance and Model Interpretability through Attention Mechanisms from Basic and Applied Research Perspectives. arXiv."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Montavon, G., Binder, A., Lapuschkin, S., Samek, W., and M\u00fcller, K. (2019). Layer-Wise Relevance Propagation: An Overview. Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, Springer.","DOI":"10.1007\/978-3-030-28954-6_10"},{"key":"ref_46","first-page":"26726","article-title":"Improving Deep Learning Interpretability by Saliency Guided Training","volume":"34","author":"Ismail","year":"2021","journal-title":"Neural Inf. Process. Syst."},{"key":"ref_47","unstructured":"Ancona, M., Ceolini, E., Oztireli, C., and Gross, M.H. (2017). A unified view of gradient-based attribution methods for Deep Neural Networks. arXiv."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"525","DOI":"10.3390\/make3030027","article-title":"Deterministic Local Interpretable Model-Agnostic Explanations for Stable Explainability","volume":"3","author":"Zafar","year":"2021","journal-title":"Mach. Learn. Knowl. Extr."},{"key":"ref_49","unstructured":"Likhareva, D., Sankaran, H., and Thiyagarajan, S. (2024). Empowering Interdisciplinary Research with BERT-Based Models: An Approach Through SciBERT-CNN with Topic Modeling. arXiv."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"109948","DOI":"10.1016\/j.knosys.2022.109948","article-title":"Set-CNN: A text convolutional neural network based on semantic extension for short text classification","volume":"257","author":"Zhou","year":"2022","journal-title":"Knowl. Based Syst."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"117942","DOI":"10.1016\/j.eswa.2022.117942","article-title":"PaTRIZ: A framework for mining TRIZ contradictions in patents","volume":"207","author":"Guarino","year":"2022","journal-title":"Expert Syst. 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