{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T02:58:07Z","timestamp":1777690687649,"version":"3.51.4"},"reference-count":63,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,3,11]],"date-time":"2025-03-11T00:00:00Z","timestamp":1741651200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,3,11]],"date-time":"2025-03-11T00:00:00Z","timestamp":1741651200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Ministero delle Imprese e del Made in Italy","award":["Data Mesh Platform Builder with AI (DAMPAI)"],"award-info":[{"award-number":["Data Mesh Platform Builder with AI (DAMPAI)"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Big Data"],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>The rise of big data has introduced significant challenges in managing, storing, analyzing, and modeling data. These challenges require the integration of diverse storage and computing platforms. Consequently, incorporating new data sources and developing new data transformation methods is typically a slow and expensive process. Furthermore, these platforms often lack sufficient abstraction and are heavily reliant on particular technologies, which results in increased costs when adapting to new technological developments. This paper addresses these issues by presenting the <jats:italic>Data Platform Shaper<\/jats:italic>, an open tool for constructing and managing data platform catalogs as knowledge graphs. This system is built on top of the <jats:italic>Agile Data Management Ontology (AGILE-DM)<\/jats:italic>, a novel ontology specifically designed for describing big data platforms. Our solution is characterized by flexibility, technological neutrality, and enhanced adaptability to changes and technological advancements.<\/jats:p>","DOI":"10.1186\/s40537-025-01094-w","type":"journal-article","created":{"date-parts":[[2025,3,11]],"date-time":"2025-03-11T12:40:57Z","timestamp":1741696857000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Modelling big data platforms as knowledge graphs: the data platform shaper"],"prefix":"10.1186","volume":"12","author":[{"given":"David","family":"Greco","sequence":"first","affiliation":[]},{"given":"Francesco","family":"Osborne","sequence":"additional","affiliation":[]},{"given":"Simone","family":"Pusceddu","sequence":"additional","affiliation":[]},{"given":"Diego","family":"Reforgiato Recupero","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,3,11]]},"reference":[{"key":"1094_CR1","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1016\/j.procs.2021.12.013","volume":"196","author":"IA Machado","year":"2022","unstructured":"Machado IA, Costa C, Santos MY. Data mesh: concepts and principles of a paradigm shift in data architectures. Proced Comput Sci. 2022;196:263\u201371. https:\/\/doi.org\/10.1016\/j.procs.2021.12.013.","journal-title":"Proced Comput Sci"},{"key":"1094_CR2","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-023-10465-9","author":"C Peng","year":"2023","unstructured":"Peng C, Xia F, Naseriparsa M, Osborne F. Knowledge graphs: opportunities and challenges. AI Rev. 2023. https:\/\/doi.org\/10.1007\/s10462-023-10465-9.","journal-title":"AI Rev"},{"key":"1094_CR3","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1007\/978-3-540-92673-3_3","volume-title":"Handbook on ontologies","author":"JZ Pan","year":"2009","unstructured":"Pan JZ. Resource description framework. In: Handbook on ontologies. Berlin, Heidelberg: Springer; 2009. p. 71\u201390. https:\/\/doi.org\/10.1007\/978-3-540-92673-3_3."},{"issue":"3","key":"1094_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/11926078_3","volume":"34","author":"J P\u00e9rez","year":"2009","unstructured":"P\u00e9rez J, Arenas M, Gutierrez C. Semantics and complexity of sparql. ACM Trans Database Syst (TODS). 2009;34(3):1\u201345. https:\/\/doi.org\/10.1007\/11926078_3.","journal-title":"ACM Trans Database Syst (TODS)"},{"issue":"10","key":"1094_CR5","first-page":"2004","volume":"10","author":"DL McGuinness","year":"2004","unstructured":"McGuinness DL, Van Harmelen F. Owl web ontology language overview. W3C Recomm. 2004;10(10):2004.","journal-title":"W3C Recomm"},{"issue":"2\u20133","key":"1094_CR6","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1006\/ijhc.1996.0091","volume":"46","author":"N Guarino","year":"1997","unstructured":"Guarino N. Understanding, building and using ontologies. Int J Hum Comput Stud. 1997;46(2\u20133):293\u2013310. https:\/\/doi.org\/10.1006\/ijhc.1996.0091.","journal-title":"Int J Hum Comput Stud"},{"key":"1094_CR7","unstructured":"Belhajjame K, Cheney J, Corsar D, Garijo D, Soiland-Reyes S, Zednik S, Zhao J. Prov-o: The prov ontology. Technical report. 2012. http:\/\/www.w3.org\/TR\/prov-o\/ Accessed 14 Dec 2024."},{"key":"1094_CR8","doi-asserted-by":"publisher","unstructured":"Golshan B, Halevy A, Mihaila G, Tan W-C. Data integration: After the teenage years. In: Proc. of the 36th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems, 2017. pp. 101\u2013106. https:\/\/doi.org\/10.1145\/3034786.3056124","DOI":"10.1145\/3034786.3056124"},{"key":"1094_CR9","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-74322-6_12","author":"K Hinkelmann","year":"2018","unstructured":"Hinkelmann K, Laurenzi E, Martin A, Th\u00f6nssen B. Ontology-based metamodeling. Bus Inf Syst Technol 40 New Trends Age Digit Change. 2018. https:\/\/doi.org\/10.1007\/978-3-319-74322-6_12.","journal-title":"Bus Inf Syst Technol 4.0 New Trends Age Digit Change"},{"issue":"4","key":"1094_CR10","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1016\/0950-5849(95)01052-1","volume":"38","author":"Y Wand","year":"1996","unstructured":"Wand Y. Ontology as a foundation for meta-modelling and method engineering. Inf Softw Technol. 1996;38(4):281\u20137. https:\/\/doi.org\/10.1016\/0950-5849(95)01052-1.","journal-title":"Inf Softw Technol"},{"issue":"2","key":"1094_CR11","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1145\/3329781.3332266","volume":"17","author":"N Noy","year":"2019","unstructured":"Noy N, Gao Y, Jain A, Narayanan A, Patterson A, Taylor J. Industry-scale knowledge graphs: lessons and challenges: five diverse technology companies show how it\u2019s done. Queue. 2019;17(2):48\u201375.","journal-title":"Queue"},{"key":"1094_CR12","doi-asserted-by":"crossref","unstructured":"Cheng D, Yang F, Wang X, Zhang Y, Zhang L. Knowledge graph-based event embedding framework for financial quantitative investments. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2020;2221\u20132230","DOI":"10.1145\/3397271.3401427"},{"issue":"1","key":"1094_CR13","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1038\/s41597-020-0543-2","volume":"7","author":"J Xu","year":"2020","unstructured":"Xu J, Kim S, Song M, Jeong M, Kim D, Kang J, Rousseau JF, Li X, Xu W, Torvik VI. Building a pubmed knowledge graph. Sci Data. 2020;7(1):205.","journal-title":"Sci Data"},{"issue":"4","key":"1094_CR14","doi-asserted-by":"publisher","first-page":"1356","DOI":"10.1162\/qss_a_00162","volume":"2","author":"S Angioni","year":"2021","unstructured":"Angioni S, Salatino A, Osborne F, Recupero DR, Motta E. Aida: a knowledge graph about research dynamics in academia and industry. Quantit Sci Stud. 2021;2(4):1356\u201398.","journal-title":"Quantit Sci Stud"},{"key":"1094_CR15","unstructured":"Motta E, Daga E, Gangemi A, Gjelsvik M, Osborne F, Salatino A. The epistemology of fine-grained news classification. Semantic Web Journal. 2024."},{"key":"1094_CR16","doi-asserted-by":"crossref","unstructured":"Chessa A, Fenu G, Motta E, Osborne F, Recupero DR, Salatino A, Secchi L. Data-driven methodology for knowledge graph generation within the tourism domain. IEEE Access. 2023","DOI":"10.1109\/ACCESS.2023.3292153"},{"key":"1094_CR17","doi-asserted-by":"crossref","unstructured":"Dess\u00ed D, Osborne F, Reforgiato\u00a0Recupero D, Buscaldi D, Motta E. Cs-kg: A large-scale knowledge graph of research entities and claims in computer science. In: International Semantic Web Conference. Springer. 2022; pp. 678\u2013696","DOI":"10.1007\/978-3-031-19433-7_39"},{"key":"1094_CR18","doi-asserted-by":"crossref","unstructured":"Burel G, Mensio M, Peskine Y, Troncy R, Papotti P, Alani H. Cimplekg: A continuously updated knowledge graph on misinformation, factors and fact-checks. In: International Semantic Web Conference. Springer. 2024;97\u2013114","DOI":"10.1007\/978-3-031-77847-6_6"},{"key":"1094_CR19","doi-asserted-by":"crossref","unstructured":"Pan S, Luo L, Wang Y, Chen C, Wang J, Wu X. Unifying large language models and knowledge graphs: A roadmap. IEEE Transactions on Knowledge and Data Engineering. 2024","DOI":"10.1109\/TKDE.2024.3352100"},{"key":"1094_CR20","doi-asserted-by":"crossref","unstructured":"Agrawal G, Kumarage T, Alghamdi Z, Liu H. Can knowledge graphs reduce hallucinations in llms?: A survey. 2023. arXiv preprint arXiv:2311.07914. Accessed 24 Feb 2025","DOI":"10.18653\/v1\/2024.naacl-long.219"},{"issue":"1","key":"1094_CR21","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1007\/s10796-022-10295-0","volume":"26","author":"P Ni","year":"2024","unstructured":"Ni P, Okhrati R, Guan S, Chang V. Knowledge graph and deep learning-based text-to-graphql model for intelligent medical consultation chatbot. Inf Syst Front. 2024;26(1):137\u201356.","journal-title":"Inf Syst Front"},{"key":"1094_CR22","doi-asserted-by":"publisher","first-page":"22468","DOI":"10.1109\/ACCESS.2023.3253388","volume":"11","author":"A Meloni","year":"2023","unstructured":"Meloni A, Angioni S, Salatino A, Osborne F, Recupero DR, Motta E. Integrating conversational agents and knowledge graphs within the scholarly domain. Ieee Access. 2023;11:22468\u201389.","journal-title":"Ieee Access"},{"issue":"10","key":"1094_CR23","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1007\/s10462-024-10902-3","volume":"57","author":"F Bolanos","year":"2024","unstructured":"Bolanos F, Salatino A, Osborne F, Motta E. Artificial intelligence for literature reviews: opportunities and challenges. Artif Intell Rev. 2024;57(10):259.","journal-title":"Artif Intell Rev"},{"key":"1094_CR24","doi-asserted-by":"crossref","unstructured":"Oniani D, Wu X, Visweswaran S, Kapoor S, Kooragayalu S, Polanska K, Wang Y. Enhancing large language models for clinical decision support by incorporating clinical practice guidelines. 2024. arXiv preprint arXiv:2401.11120. Accessed 24 Feb 2025","DOI":"10.1109\/ICHI61247.2024.00111"},{"key":"1094_CR25","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1007\/978-3-642-40897-7_9","volume-title":"Discovery science","author":"P Panov","year":"2013","unstructured":"Panov P, Soldatova L, D\u017eeroski S. Ontodm-kdd: ontology for representing the knowledge discovery process. In: F\u00fcrnkranz J, H\u00fcllermeier E, Higuchi T, editors. Discovery science. Berlin, Heidelberg: Springer; 2013. p. 126\u201340. https:\/\/doi.org\/10.1007\/978-3-642-40897-7_9."},{"issue":"2","key":"1094_CR26","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1007\/s00766-022-00385-5","volume":"28","author":"M Bandara","year":"2023","unstructured":"Bandara M, Rabhi FA, Bano M. A knowledge-driven approach for designing data analytics platforms. Requir Eng. 2023;28(2):195\u2013212. https:\/\/doi.org\/10.1007\/s00766-022-00385-5.","journal-title":"Requir Eng"},{"key":"1094_CR27","doi-asserted-by":"publisher","first-page":"525","DOI":"10.3233\/SW-190352","volume":"11","author":"M Bandara","year":"2020","unstructured":"Bandara M, Rabhi FA. Semantic modeling for engineering data analytics solutions. Semant Web. 2020;11:525\u201347. https:\/\/doi.org\/10.3233\/SW-190352.","journal-title":"Semant Web"},{"issue":"27","key":"1094_CR28","doi-asserted-by":"publisher","first-page":"19821","DOI":"10.1007\/s00521-023-08783-8","volume":"35","author":"A Kourid","year":"2023","unstructured":"Kourid A, Chikhi S, Recupero DR. Fuzzy optimized v-detector algorithm on apache spark for class imbalance issue of intrusion detection in big data. NCA. 2023;35(27):19821\u201345. https:\/\/doi.org\/10.1007\/s00521-023-08783-8.","journal-title":"NCA"},{"key":"1094_CR29","doi-asserted-by":"publisher","first-page":"5511","DOI":"10.24963\/ijcai.2018\/777","volume-title":"IJCAI","author":"G Xiao","year":"2018","unstructured":"Xiao G, Calvanese D, Kontchakov R, Lembo D, Poggi A, Rosati R, Zakharyaschev M. Ontology-based data access: a survey. In: Lang J, editor. IJCAI. Stockholm: ijcai.org; 2018. p. 5511\u20139. https:\/\/doi.org\/10.24963\/ijcai.2018\/777."},{"key":"1094_CR30","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1145\/543613.543644","volume-title":"PODS","author":"M Lenzerini","year":"2002","unstructured":"Lenzerini M. Data integration: a theoretical perspective. In: Popa L, Abiteboul S, Kolaitis PG, editors. PODS. Madison: ACM; 2002. p. 233\u201346. https:\/\/doi.org\/10.1145\/543613.543644."},{"issue":"1","key":"1094_CR31","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1145\/3154385","volume":"43","author":"L Libkin","year":"2018","unstructured":"Libkin L, Reutter JL, Soto A, Vrgoc D. Trial: a navigational algebra for rdf triplestores. ACM Trans Database Syst. 2018;43(1):5\u20131546. https:\/\/doi.org\/10.1145\/3154385.","journal-title":"ACM Trans Database Syst"},{"issue":"5","key":"1094_CR32","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1145\/3104031","volume":"50","author":"R Angles","year":"2017","unstructured":"Angles R, Arenas M, Barcel\u00f3 P, Hogan A, Reutter JL, Vrgoc D. Foundations of modern query languages for graph databases. ACM Comput Surv. 2017;50(5):68\u201316840. https:\/\/doi.org\/10.1145\/3104031.","journal-title":"ACM Comput Surv"},{"key":"1094_CR33","doi-asserted-by":"publisher","unstructured":"Aranda, C.B.: Federated query processing for the semantic web. PhD thesis, Madrid, Univ. Polite\u2019cnica. 2014. https:\/\/doi.org\/10.3233\/978-1-61499-350-6-i","DOI":"10.3233\/978-1-61499-350-6-i"},{"issue":"2","key":"1094_CR34","first-page":"10","volume":"41","author":"C Chen","year":"2018","unstructured":"Chen C, Golshan B, Halevy AY, Tan W-C, Doan A. Biggorilla: an open-source ecosystem for data preparation and integration. IEEE Data Eng Bull. 2018;41(2):10\u201322.","journal-title":"IEEE Data Eng Bull"},{"key":"1094_CR35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/234313.234368","volume-title":"Computing handbook","author":"MT \u00d6zsu","year":"2014","unstructured":"\u00d6zsu MT, Valduriez P. Distributed and parallel database systems. In: Topi H, Tucker A, editors. Computing handbook, vol. 13. 3rd ed. Boca Raton: CRC Press; 2014. p. 1\u201324. https:\/\/doi.org\/10.1145\/234313.234368.","edition":"3"},{"key":"1094_CR36","first-page":"1","volume-title":"Linked data - storing, querying, and reasoning","author":"S Sakr","year":"2018","unstructured":"Sakr S, Wylot M, Mutharaju R, Phuoc DL, Fundulaki I. Linked data - storing, querying, and reasoning. Cham: Springer; 2018. p. 1\u2013223."},{"key":"1094_CR37","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1007\/978-3-319-48429-7_32","volume-title":"Hard and soft computing for artificial intelligence, multimedia and security","author":"A Konys","year":"2017","unstructured":"Konys A. Ontology-based approaches to big data analytics. In: Kobayashi S-Y, Piegat A, Peja\u015b J, El Fray I, Kacprzyk J, editors. Hard and soft computing for artificial intelligence, multimedia and security. Cham: Springer; 2017. p. 355\u201365."},{"key":"1094_CR38","unstructured":"Chessa A, Fenu G, Motta E, Reforgiato\u00a0Recupero D, Osborne F, Salatino A, Secchi L. Enriching data lakes with knowledge graphs. In: Proceedings of the 1st International Workshop on Knowledge Graph Generation From Text and the 1st International Workshop on Modular Knowledge Co-located with 19th Extended Semantic Conference (ESWC 2022). 2022."},{"issue":"3\u20134","key":"1094_CR39","doi-asserted-by":"publisher","first-page":"245","DOI":"10.3233\/AO-170189","volume":"12","author":"K Baclawski","year":"2017","unstructured":"Baclawski K, Chan ES, Gawlick D, Ghoneimy A, Gross K, Liu ZH, Zhang X. Framework for ontology-driven decision making. Appl Ontol. 2017;12(3\u20134):245\u201373. https:\/\/doi.org\/10.3233\/AO-170189.","journal-title":"Appl Ontol"},{"key":"1094_CR40","unstructured":"Boyd J. Destruction and Creation. WA, Fort Leavenworth, KS: US Army Comand and General Staff College Leavenworth; 1987."},{"key":"1094_CR41","unstructured":"Consortium, W.W.W., et al.: The rdf data cube vocabulary. Technical report. 2014. https:\/\/www.w3.org\/TR\/vocab-data-cube\/ Accessed 14 Dec 2024."},{"key":"1094_CR42","doi-asserted-by":"publisher","first-page":"95402","DOI":"10.1109\/ACCESS.2024.3417291","volume":"12","author":"J Bode","year":"2024","unstructured":"Bode J, K\u00fchl N, Kreuzberger D, Holtmann C. Toward avoiding the data mess: industry insights from data mesh implementations. IEEE Access. 2024;12:95402\u201316. https:\/\/doi.org\/10.1109\/ACCESS.2024.3417291.","journal-title":"IEEE Access"},{"key":"1094_CR43","unstructured":"Perrin J-G, Broda E. Implementing Data Mesh. O\u2019Reilly Media, Sebastopol, CA. 2024. https:\/\/www.oreilly.com\/library\/view\/implementing-data-mesh\/9781098156213\/. Accessed 24 Feb 2025"},{"key":"1094_CR44","doi-asserted-by":"publisher","unstructured":"Sisson D, Nabben K, Ben-Meir I, Zargham M. Data mesh architecture: Interoperability, co-operation, and co-regulation. 2024. https:\/\/doi.org\/10.2139\/ssrn.4880709","DOI":"10.2139\/ssrn.4880709"},{"key":"1094_CR45","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1007\/978-3-030-62625-9_5","volume-title":"Big data, machine learning, and applications","author":"RK Batwada","year":"2020","unstructured":"Batwada RK, Mittal N, Pilli ES. Uncovering data warehouse issues and challenges in big data management. In: Patgiri R, Bandyopadhyay S, Borah MD, Thounaojam DM, editors. Big data, machine learning, and applications. Cham: Springer; 2020. p. 48\u201359. https:\/\/doi.org\/10.1007\/978-3-030-62625-9_5."},{"key":"1094_CR46","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1007\/978-3-030-91452-3_3","volume-title":"Product-focused software process improvement","author":"A Loukiala","year":"2021","unstructured":"Loukiala A, Joutsenlahti J-P, Raatikainen M, Mikkonen T, Lehtonen T. Migrating from a centralized data warehouse to a decentralized data platform architecture. In: Ardito L, Jedlitschka A, Morisio M, Torchiano M, editors. Product-focused software process improvement. Cham: Springer; 2021. p. 36\u201348. https:\/\/doi.org\/10.1007\/978-3-030-91452-3_3."},{"key":"1094_CR47","doi-asserted-by":"publisher","unstructured":"Klettke M, Awolin H, St\u00f6rl U, M\u00fcller D, Scherzinger S. Uncovering the evolution history of data lakes. In: 2017 IEEE International Conference on Big Data (Big Data), 2017; pp. 2462\u20132471. https:\/\/doi.org\/10.1109\/BigData.2017.8258204","DOI":"10.1109\/BigData.2017.8258204"},{"issue":"12","key":"1094_CR48","doi-asserted-by":"publisher","first-page":"12571","DOI":"10.1109\/TKDE.2023.3270101","volume":"35","author":"R Hai","year":"2023","unstructured":"Hai R, Koutras C, Quix C, Jarke M. Data lakes: a survey of functions and systems. IEEE Trans Knowl Data Eng. 2023;35(12):12571\u201390. https:\/\/doi.org\/10.1109\/TKDE.2023.3270101.","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"1094_CR49","unstructured":"Majchrzak J, Balnojan S, Siwiak M. Data Mesh in Action. Manning, New York. 2023. https:\/\/books.google.it\/books?id=DV6mEAAAQBAJ. Accessed 24 Feb 2025"},{"key":"1094_CR50","volume-title":"Data mesh: delivering data-driven value at scale","author":"Z Dehghani","year":"2022","unstructured":"Dehghani Z, Fowler M. Data mesh: delivering data-driven value at scale. Sebastopol: O\u2019Reilly Media; 2022."},{"key":"1094_CR51","doi-asserted-by":"publisher","unstructured":"Wider A, Verma S, Akhtar A. Decentralized data governance as part of a data mesh platform: Concepts and approaches. In: 30th International Conference on Web Services, July 2-8, 2023, Chicago, USA. 2023. https:\/\/doi.org\/10.1109\/ICWS60048.2023.00101","DOI":"10.1109\/ICWS60048.2023.00101"},{"key":"1094_CR52","doi-asserted-by":"publisher","unstructured":"Monfardini GKQ, Salamon JS, Barcellos MP: Use of competency questions in ontology engineering: A survey. In: International Conference on Conceptual Modeling,Springer. 2023; pp. 45\u201364 . https:\/\/doi.org\/10.1007\/978-3-031-47262-6_3","DOI":"10.1007\/978-3-031-47262-6_3"},{"issue":"2","key":"1094_CR53","doi-asserted-by":"publisher","first-page":"7","DOI":"10.4018\/ijswis.2014040102","volume":"10","author":"M Poveda-Villal\u00f3n","year":"2014","unstructured":"Poveda-Villal\u00f3n M, G\u00f3mez-P\u00e9rez A, Su\u00e1rez-Figueroa MC. Oops!(ontology pitfall scanner!): an on-line tool for ontology evaluation. Int J Semant Web Inf Syst (IJSWIS). 2014;10(2):7\u201334.","journal-title":"Int J Semant Web Inf Syst (IJSWIS)"},{"key":"1094_CR54","unstructured":"Hartig O. Foundations of rdf* and sparql*:(an alternative approach to statement-level metadata in rdf). In: AMW 2017 11th Alberto Mendelzon International Workshop on Foundations of Data Management and the Web, Montevideo, Uruguay, June 7-9, 2017. Juan Reutter, Divesh Srivastava. 2017; vol. 1912 https:\/\/ceur-ws.org\/Vol-1912\/"},{"key":"1094_CR55","doi-asserted-by":"publisher","unstructured":"Nguyen V, Bodenreider O, Sheth A. Don\u2019t like rdf reification? making statements about statements using singleton property. In: Proceedings of the 23rd International Conference on World Wide Web. WWW \u201914. Association for Computing Machinery, New York, NY, USA. 2014; pp. 759\u2013770. https:\/\/doi.org\/10.1145\/2566486.2567973 .","DOI":"10.1145\/2566486.2567973"},{"issue":"2","key":"1094_CR56","doi-asserted-by":"publisher","first-page":"167","DOI":"10.3233\/SW-140134","volume":"6","author":"J Lehmann","year":"2015","unstructured":"Lehmann J, Isele R, Jakob M, Jentzsch A, Kontokostas D, Mendes PN, Hellmann S, Morsey M, Van Kleef P, Auer S. Dbpedia - a large-scale, multilingual knowledge base extracted from wikipedia. Semantic web. 2015;6(2):167\u201395. https:\/\/doi.org\/10.3233\/SW-140134.","journal-title":"Semantic web"},{"issue":"10","key":"1094_CR57","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1145\/2629489","volume":"57","author":"D Vrande\u010di\u0107","year":"2014","unstructured":"Vrande\u010di\u0107 D, Kr\u00f6tzsch M. Wikidata: a free collaborative knowledgebase. Commun ACM. 2014;57(10):78\u201385. https:\/\/doi.org\/10.1145\/2629489.","journal-title":"Commun ACM"},{"key":"1094_CR58","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1016\/j.future.2020.10.026","volume":"116","author":"D Dess\u00ec","year":"2021","unstructured":"Dess\u00ec D, Osborne F, Recupero DR, Buscaldi D, Motta E. Generating knowledge graphs by employing natural language processing and machine learning techniques within the scholarly domain. Future Gener Comput Syst. 2021;116:253\u201364. https:\/\/doi.org\/10.1016\/j.future.2020.10.026.","journal-title":"Future Gener Comput Syst"},{"key":"1094_CR59","doi-asserted-by":"crossref","unstructured":"Moritz D, Fisher D, Ding B, Wang C. Trust, but verify: Optimistic visualizations of approximate queries for exploring big data. In: Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, 2017; pp. 2904\u20132915","DOI":"10.1145\/3025453.3025456"},{"key":"1094_CR60","doi-asserted-by":"crossref","unstructured":"Cleland B, Wallace J, Bond R, Muuraiskangas S, Pajula J, Epelde G, Arr\u00fae M, \u00c1lvarez R, Black M, Mulvenna MD. Usability evaluation of a co-created big data analytics platform for health policy-making. In: Human Interface and the Management of Information. Visual Information and Knowledge Management: Thematic Area, HIMI 2019, Held as Part of the 21st HCI International Conference, HCII 2019, Orlando, FL, USA, July 26\u201331, 2019, Proceedings, Part I 21, Springer, 2019; pp. 194\u2013207","DOI":"10.1007\/978-3-030-22660-2_13"},{"key":"1094_CR61","doi-asserted-by":"publisher","first-page":"2677","DOI":"10.1145\/3660825","volume":"1","author":"Y Wu","year":"2024","unstructured":"Wu Y, Humayun A, Gulzar MA, Kim M. Natural symbolic execution-based testing for big data analytics. Proc ACM Softw Eng (FSE). 2024;1:2677\u2013700.","journal-title":"Proc ACM Softw Eng (FSE)"},{"key":"1094_CR62","doi-asserted-by":"crossref","unstructured":"Islam MR, Razzak I, Wang X, Tilocca P, Xu G: Ucbvis: understanding customer behavior sequences with visual interactive system. In: 2021 International Joint Conference on Neural Networks (IJCNN), IEEE. 2021; pp. 1\u20138.","DOI":"10.1109\/IJCNN52387.2021.9533354"},{"key":"1094_CR63","doi-asserted-by":"crossref","unstructured":"Ebel P, G\u00fclle KJ, Lingenfelder C, Vogelsang A. Exploring millions of user interactions with iceboat: Big data analytics for automotive user interfaces. In: Proceedings of the 15th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, 2023; pp. 81\u201392","DOI":"10.1145\/3580585.3607158"}],"container-title":["Journal of Big Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-025-01094-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s40537-025-01094-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-025-01094-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,11]],"date-time":"2025-03-11T12:41:10Z","timestamp":1741696870000},"score":1,"resource":{"primary":{"URL":"https:\/\/journalofbigdata.springeropen.com\/articles\/10.1186\/s40537-025-01094-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,11]]},"references-count":63,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["1094"],"URL":"https:\/\/doi.org\/10.1186\/s40537-025-01094-w","relation":{},"ISSN":["2196-1115"],"issn-type":[{"value":"2196-1115","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,11]]},"assertion":[{"value":"30 July 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 February 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 March 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no Competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"64"}}