{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T13:38:13Z","timestamp":1771594693420,"version":"3.50.1"},"reference-count":70,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2021,11,24]],"date-time":"2021-11-24T00:00:00Z","timestamp":1637712000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MTI"],"abstract":"<jats:p>Eliciting knowledge from domain experts can play an important role throughout the machine learning process, from correctly specifying the task to evaluating model results. However, knowledge elicitation is also fraught with challenges. In this work, we consider why and how machine learning researchers elicit knowledge from experts in the model development process. We develop a taxonomy to characterize elicitation approaches according to the elicitation goal, elicitation target, elicitation process, and use of elicited knowledge. We analyze the elicitation trends observed in 28 papers with this taxonomy and identify opportunities for adding rigor to these elicitation approaches. We suggest future directions for research in elicitation for machine learning by highlighting avenues for further exploration and drawing on what we can learn from elicitation research in other fields.<\/jats:p>","DOI":"10.3390\/mti5120073","type":"journal-article","created":{"date-parts":[[2021,11,30]],"date-time":"2021-11-30T22:01:11Z","timestamp":1638309671000},"page":"73","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["A Survey of Domain Knowledge Elicitation in Applied Machine Learning"],"prefix":"10.3390","volume":"5","author":[{"given":"Daniel","family":"Kerrigan","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, New York University, Brooklyn, NY 11201, USA"}]},{"given":"Jessica","family":"Hullman","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Northwestern University, Evanston, IL 60208, USA"}]},{"given":"Enrico","family":"Bertini","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, New York University, Brooklyn, NY 11201, USA"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Chi, M.T. (2006). Laboratory methods for assessing experts\u2019 and novices\u2019 knowledge. The Cambridge Handbook of Expertise and Expert Performance, Cambridge University Press.","DOI":"10.1017\/CBO9780511816796.010"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"O\u2019Hagan, A., Buck, C.E., Daneshkhah, A., Eiser, J.R., Garthwaite, P.H., Jenkinson, D.J., Oakley, J.E., and Rakow, T. (2006). Uncertain Judgements: Eliciting Experts\u2019 Probabilities, John Wiley & Sons.","DOI":"10.1002\/0470033312"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3134664","article-title":"Seeing Sound: Investigating the Effects of Visualizations and Complexity on Crowdsourced Audio Annotations","volume":"1","author":"Cartwright","year":"2017","journal-title":"Proc. ACM Hum.-Comput. Interact."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Cakmak, M., and Thomaz, A.L. (2012, January 5\u20138). Designing robot learners that ask good questions. Proceedings of the 2012 7th ACM\/IEEE International Conference on Human-Robot Interaction (HRI), Boston, MA, USA.","DOI":"10.1145\/2157689.2157693"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1080\/00031305.2018.1518265","article-title":"Expert Knowledge Elicitation: Subjective but Scientific","volume":"73","year":"2019","journal-title":"Am. Stat."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Yang, Q., Suh, J., Chen, N.C., and Ramos, G. (2018, January 9\u201313). Grounding Interactive Machine Learning Tool Design in How Non-Experts Actually Build Models. Proceedings of the 2018 Designing Interactive Systems Conference, Hong Kong, China.","DOI":"10.1145\/3196709.3196729"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"i395","DOI":"10.1093\/bioinformatics\/bty257","article-title":"Improving genomics-based predictions for precision medicine through active elicitation of expert knowledge","volume":"34","author":"Sundin","year":"2018","journal-title":"Bioinformatics"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2271","DOI":"10.1080\/03610929508831616","article-title":"Eliciting prior information to enhance the predictive performance of bayesian graphical models","volume":"24","author":"Madigan","year":"1995","journal-title":"Commun. Stat.-Theory Methods"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Ashdown, G.W., Dimon, M., Fan, M., Ter\u00e1n, F.S.R., Witmer, K., Gaboriau, D.C.A., Armstrong, Z., Ando, D.M., and Baum, J. (2020). A machine learning approach to define antimalarial drug action from heterogeneous cell-based screens. Sci. Adv., 6.","DOI":"10.1126\/sciadv.aba9338"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"520","DOI":"10.1001\/jamapsychiatry.2017.0298","article-title":"The World Health Organization Adult Attention-Deficit\/Hyperactivity Disorder Self-Report Screening Scale for DSM-5","volume":"74","author":"Ustun","year":"2017","journal-title":"JAMA Psychiatry"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Sendak, M., Elish, M.C., Gao, M., Futoma, J., Ratliff, W., Nichols, M., Bedoya, A., Balu, S., and O\u2019Brien, C. (2020, January 27\u201330). \u201cThe Human Body is a Black Box\u201d: Supporting Clinical Decision-Making with Deep Learning. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, Barcelona, Spain.","DOI":"10.1145\/3351095.3372827"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1097\/NNA.0b013e3182942c3c","article-title":"Conducting research using the electronic health record across multi-hospital systems: Semantic harmonization implications for administrators","volume":"43","author":"Bowles","year":"2013","journal-title":"J. Nurs. Adm."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"368","DOI":"10.4338\/ACI-2015-11-RA-0161","article-title":"Using Electronic Case Summaries to Elicit Multi-Disciplinary Expert Knowledge about Referrals to Post-Acute Care","volume":"7","author":"Bowles","year":"2016","journal-title":"Appl. Clin. Inform."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1007\/BF00994016","article-title":"Learning Bayesian Networks: The Combination of Knowledge and Statistical Data","volume":"20","author":"Heckerman","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Cai, C.J., Reif, E., Hegde, N., Hipp, J., Kim, B., Smilkov, D., Wattenberg, M., Viegas, F., Corrado, G.S., and Stumpe, M.C. (2019, January 4\u20139). Human-Centered Tools for Coping with Imperfect Algorithms During Medical Decision-Making. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, Glasgow, UK.","DOI":"10.1145\/3290605.3300234"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Lee, M.H., Siewiorek, D.P., Smailagic, A., Bernardino, A., and Berm\u00fadez i Badia, S. (2020, January 2\u20134). Interactive Hybrid Approach to Combine Machine and Human Intelligence for Personalized Rehabilitation Assessment. Proceedings of the ACM Conference on Health, Inference, and Learning, Toronto, ON, Canada.","DOI":"10.1145\/3368555.3384452"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Barricelli, B.R., Roto, V., Clemmensen, T., Campos, P., Lopes, A., Gon\u00e7alves, F., and Abdelnour-Nocera, J. (2019). A Methodology to Involve Domain Experts and Machine Learning Techniques in the Design of Human-Centered Algorithms. Human Work Interaction Design. Designing Engaging Automation, Springer International Publishing.","DOI":"10.1007\/978-3-030-05297-3"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Schaekermann, M., Hammel, N., Terry, M., Ali, T.K., Liu, Y., Basham, B., Campana, B., Chen, W., Ji, X., and Krause, J. (2019). Remote Tool-Based Adjudication for Grading Diabetic Retinopathy. Transl. Vis. Sci. Technol., 8.","DOI":"10.1167\/tvst.8.6.40"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1168","DOI":"10.1016\/j.ijar.2013.03.009","article-title":"An interactive approach for Bayesian network learning using domain\/expert knowledge","volume":"54","author":"Masegosa","year":"2013","journal-title":"Int. J. Approx. Reason."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1382","DOI":"10.1109\/TSMCB.2011.2148197","article-title":"A Method for Integrating Expert Knowledge When Learning Bayesian Networks From Data","volume":"41","author":"Cano","year":"2011","journal-title":"IEEE Trans. Syst. Man Cybern. Part B"},{"key":"ref_21","unstructured":"Richardson, M., and Domingos, P. (2003, January 21\u201324). Learning with Knowledge from Multiple Experts. Proceedings of the Twentieth International Conference on Machine Learning, Washington, DC, USA."},{"key":"ref_22","first-page":"339","article-title":"Fusion of Domain Knowledge with Data for Structural Learning in Object Oriented Domains","volume":"4","author":"Langseth","year":"2003","journal-title":"J. Mach. Learn. Res."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Afrabandpey, H., Peltola, T., and Kaski, S. (2019, January 10\u201316). Human-in-the-loop Active Covariance Learning for Improving Prediction in Small Data Sets. Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI-19, Macao, China.","DOI":"10.24963\/ijcai.2019\/271"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1109\/TVCG.2018.2864769","article-title":"Visual Analytics for Topic Model Optimization based on User-Steerable Speculative Execution","volume":"25","author":"Sperrle","year":"2019","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"ref_25","first-page":"1001","article-title":"Semantic Concept Spaces: Guided Topic Model Refinement using Word-Embedding Projections","volume":"26","author":"Kehlbeck","year":"2020","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1599","DOI":"10.1007\/s10994-017-5651-7","article-title":"Knowledge elicitation via sequential probabilistic inference for high-dimensional prediction","volume":"106","author":"Daee","year":"2017","journal-title":"Mach. Learn."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.apenergy.2018.10.107","article-title":"Design of machine learning models with domain experts for automated sensor selection for energy fault detection","volume":"235","author":"Hu","year":"2019","journal-title":"Appl. Energy"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1016\/0950-7051(96)01033-7","article-title":"Integrating machine learning with knowledge acquisition through direct interaction with domain experts","volume":"9","author":"Webb","year":"1996","journal-title":"Knowl.-Based Syst."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1023\/A:1008193214890","article-title":"Application of Machine Learning in Water Distribution Networks Assisted by Domain Experts","volume":"26","author":"Martinelli","year":"1999","journal-title":"J. Intell. Robot. Syst."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"269","DOI":"10.14778\/3157794.3157797","article-title":"Snorkel: Rapid Training Data Creation with Weak Supervision","volume":"11","author":"Ratner","year":"2017","journal-title":"Proc. VLDB Endow."},{"key":"ref_31","first-page":"1","article-title":"Learning Optimized Risk Scores","volume":"20","author":"Ustun","year":"2019","journal-title":"J. Mach. Learn. Res."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Amershi, S., Lee, B., Kapoor, A., Mahajan, R., and Christian, B. (2011, January 7\u201312). CueT: Human-Guided Fast and Accurate Network Alarm Triage. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Vancouver, BC, Canada.","DOI":"10.1145\/1978942.1978966"},{"key":"ref_33","unstructured":"Altendorf, E.E., Restificar, A.C., and Dietterich, T.G. (2005, January 26\u201329). Learning from Sparse Data by Exploiting Monotonicity Constraints. Proceedings of the Twenty-First Conference on Uncertainty in Artificial Intelligence, Edinburgh, Scotland."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Holstein, K., Wortman Vaughan, J., Daum\u00e9, H., Dudik, M., and Wallach, H. (2019, January 4\u20139). Improving fairness in machine learning systems: What do industry practitioners need?. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, Glasgow, UK.","DOI":"10.1145\/3290605.3300830"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Kaur, H., Nori, H., Jenkins, S., Caruana, R., Wallach, H., and Wortman Vaughan, J. (2020, January 25\u201330). Interpreting Interpretability: Understanding Data Scientists\u2019 Use of Interpretability Tools for Machine Learning. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, Honolulu, HI, USA.","DOI":"10.1145\/3313831.3376219"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Law, P.M., Malik, S., Du, F., and Sinha, M. (2020). Designing Tools for Semi-Automated Detection of Machine Learning Biases: An Interview Study. arXiv.","DOI":"10.31219\/osf.io\/uvjqh"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3361118","article-title":"How Data ScientistsWork Together with Domain Experts in Scientific Collaborations: To Find the Right Answer or to Ask the Right Question?","volume":"3","author":"Mao","year":"2019","journal-title":"Proc. ACM Hum.-Comput. Interact."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3392878","article-title":"Human Factors in Model Interpretability: Industry Practices, Challenges, and Needs","volume":"4","author":"Hong","year":"2020","journal-title":"Proc. ACM Hum.-Comput. Interact."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3359206","article-title":"\u201cHello AI\u201d: Uncovering the Onboarding Needs of Medical Practitioners for Human-AI Collaborative Decision-Making","volume":"3","author":"Cai","year":"2019","journal-title":"Proc. ACM Hum.-Comput. Interact."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Ericsson, K., Hoffman, R., Kozbelt, A., and Williams, A. (2018). The Cambridge Handbook of Expertise and Expert Performance, Cambridge University Press. [2nd ed.].","DOI":"10.1017\/9781316480748"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1207\/s15516709cog0502_2","article-title":"Categorization and representation of physics problems by experts and novices","volume":"5","author":"Chi","year":"1981","journal-title":"Cogn. Sci."},{"key":"ref_42","unstructured":"Chi, M.T., Glaser, R., and Rees, E. (1982). Expertise in Problem Solving: Advances in the Psychology of Human Intelligence, Erlbaum."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"680","DOI":"10.1198\/016214505000000105","article-title":"Statistical methods for eliciting probability distributions","volume":"100","author":"Garthwaite","year":"2005","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1017\/S1930297500004940","article-title":"Lay understanding of probability distributions","volume":"9","author":"Goldstein","year":"2014","journal-title":"Judgm. Decis. Mak."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1111\/1468-0394.00159","article-title":"Selection of knowledge acquisition techniques based upon the problem domain characteristics of production and operations management expert systems","volume":"18","author":"Wagner","year":"2001","journal-title":"Expert Syst."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.eswa.2017.01.028","article-title":"Trends in expert system development: A longitudinal content analysis of over thirty years of expert system case studies","volume":"76","author":"Wagner","year":"2017","journal-title":"Expert Syst. Appl."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Rahman, P., Nandi, A., and Hebert, C. (2020). Amplifying Domain Expertise in Clinical Data Pipelines. JMIR Med. Inform., 8.","DOI":"10.2196\/preprints.19612"},{"key":"ref_48","first-page":"105","article-title":"Power to the people: The role of humans in interactive machine learning","volume":"35","author":"Amershi","year":"2014","journal-title":"AI Mag."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1109\/TAMD.2010.2051030","article-title":"Designing interactions for robot active learners","volume":"2","author":"Cakmak","year":"2010","journal-title":"IEEE Trans. Auton. Ment. Dev."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"716","DOI":"10.1016\/j.artint.2007.09.009","article-title":"Teachable robots: Understanding human teaching behavior to build more effective robot learners","volume":"172","author":"Thomaz","year":"2008","journal-title":"Artif. Intell."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Rosenthal, S.L., and Dey, A.K. (2010, January 7\u201310). Towards maximizing the accuracy of human-labeled sensor data. Proceedings of the 15th International Conference on Intelligent User Interfaces, Hong Kong, China.","DOI":"10.1145\/1719970.1720006"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Daee, P., Peltola, T., Vehtari, A., and Kaski, S. (2018, January 7\u201311). User modelling for avoiding overfitting in interactive knowledge elicitation for prediction. Proceedings of the 23rd International Conference on Intelligent User Interfaces, Tokyo, Japan.","DOI":"10.1145\/3172944.3172989"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Budd, S., Robinson, E.C., and Kainz, B. (2021). A survey on active learning and human-in-the-loop deep learning for medical image analysis. Med. Image Anal., 71.","DOI":"10.1016\/j.media.2021.102062"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Wu, X., Xiao, L., Sun, Y., Zhang, J., Ma, T., and He, L. (2021). A Survey of Human-in-the-loop for Machine Learning. arXiv.","DOI":"10.1016\/j.future.2022.05.014"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Lasecki, W.S., Rzeszotarski, J.M., Marcus, A., and Bigham, J.P. (2015, January 18\u201323). The Effects of Sequence and Delay on Crowd Work. Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, Seoul, Korea.","DOI":"10.1145\/2702123.2702594"},{"key":"ref_56","unstructured":"Attenberg, J., Ipeirotis, P.G., and Provost, F.J. (2011, January 7\u20138). Beat the Machine: Challenging Workers to Find the Unknown Unknowns. Proceedings of the Twenty-Fifth Conference on Artificial Intelligence (AAAI-11), San Francisco, CA, USA."},{"key":"ref_57","unstructured":"Lofland, J., and Lofland, L.H. (1971). Analyzing Social Settings, Wadsworth Pub. Co."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/S0022-5371(83)90189-5","article-title":"Common ground at the understanding of demonstrative reference","volume":"22","author":"Clark","year":"1983","journal-title":"J. Verbal Learn. Verbal Behav."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.envsoft.2011.03.003","article-title":"Probabilistic Uncertainty Specification: Overview, Elaboration Techniques and Their Application to a Mechanistic Model of Carbon Flux","volume":"36","year":"2012","journal-title":"Environ. Model. Softw."},{"key":"ref_60","unstructured":"O\u2019Hagan, A., and Oakley, J.E. (2019). SHELF: The Sheffield Elicitation Framework (Version 4), University of Sheffield."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Dias, L.C., Morton, A., and Quigley, J. (2018). SHELF: The Sheffield Elicitation Framework. Elicitation: The Science and Art of Structuring Judgement, Springer International Publishing.","DOI":"10.1007\/978-3-319-65052-4"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Cooke, R.M. (1991). Experts in Uncertainty: Opinion and Subjective Probability in Science, Oxford University Press.","DOI":"10.1093\/oso\/9780195064650.001.0001"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1016\/S0169-2070(99)00018-7","article-title":"The Delphi technique as a forecasting tool: Issues and analysis","volume":"15","author":"Rowe","year":"1999","journal-title":"Int. J. Forecast."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"446","DOI":"10.1109\/TVCG.2017.2743898","article-title":"Imagining replications: Graphical prediction & discrete visualizations improve recall & estimation of effect uncertainty","volume":"24","author":"Hullman","year":"2017","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Cheng, P.W. (1997). From covariation to causation: A causal power theory. Psychol. Rev., 104.","DOI":"10.1037\/\/0033-295X.104.2.367"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1016\/j.cogpsych.2005.05.004","article-title":"Structure and strength in causal induction","volume":"51","author":"Griffiths","year":"2005","journal-title":"Cogn. Psychol."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"661","DOI":"10.1037\/a0017201","article-title":"Theory-based causal induction","volume":"116","author":"Griffiths","year":"2009","journal-title":"Psychol. Rev."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Hullman, J., and Gelman, A. (2021). Designing for Interactive Exploratory Data Analysis Requires Theories of Graphical Inference. Harvard Data Science Review.","DOI":"10.1162\/99608f92.3ab8a587"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Kim, Y.S., Walls, L.A., Krafft, P., and Hullman, J. (2019, January 4\u20139). A bayesian cognition approach to improve data visualization. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, Glasgow, UK.","DOI":"10.1145\/3290605.3300912"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"2301","DOI":"10.1109\/TVCG.2011.185","article-title":"D3 Data-Driven Documents","volume":"17","author":"Bostock","year":"2011","journal-title":"IEEE Trans. Vis. Comput. Graph."}],"container-title":["Multimodal Technologies and Interaction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2414-4088\/5\/12\/73\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:35:25Z","timestamp":1760168125000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2414-4088\/5\/12\/73"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,24]]},"references-count":70,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["mti5120073"],"URL":"https:\/\/doi.org\/10.3390\/mti5120073","relation":{},"ISSN":["2414-4088"],"issn-type":[{"value":"2414-4088","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,24]]}}}