{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T23:03:34Z","timestamp":1780959814176,"version":"3.54.1"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2023,4,4]],"date-time":"2023-04-04T00:00:00Z","timestamp":1680566400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,4,4]],"date-time":"2023-04-04T00:00:00Z","timestamp":1680566400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Softw Syst Model"],"published-print":{"date-parts":[[2023,10]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Model-driven engineering (MDE) is an effective means of synchronizing among stakeholders, thereby being a crucial part of the software development life cycle. In recent years, MDE has been on the rise, triggering the need for automatic modeling assistants to support metamodelers during their daily activities. Among others, it is crucial to enable model designers to choose suitable components while working on new (meta)models. In our previous work, we proposed MORGAN, a graph kernel-based recommender system to assist developers in completing models and metamodels. To provide input for the recommendation engine, we convert training data into a graph-based format, making use of various natural language processing (NLP) techniques. The extracted graphs are then fed as input for a recommendation engine based on graph kernel similarity, which performs predictions to provide modelers with relevant recommendations to complete the partially specified (meta)models. In this paper, we extend the proposed tool in different dimensions, resulting in a more advanced recommender system. Firstly, we equip it with the ability to support recommendations for JSON schema that provides a model representation of data handling operations. Secondly, we introduce additional preprocessing steps and a kernel similarity function based on item frequency, aiming to enhance the capabilities, providing more precise recommendations. Thirdly, we study the proposed enhancements, conducting a well-structured evaluation by considering three real-world datasets. Although the increasing size of the training data negatively affects the computation time, the experimental results demonstrate that the newly introduced mechanisms allow MORGAN to improve its recommendations compared to its preceding version.<\/jats:p>","DOI":"10.1007\/s10270-023-01102-8","type":"journal-article","created":{"date-parts":[[2023,4,4]],"date-time":"2023-04-04T11:52:19Z","timestamp":1680609139000},"page":"1427-1449","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["MORGAN: a modeling recommender system based on graph kernel"],"prefix":"10.1007","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9872-9542","authenticated-orcid":false,"given":"Claudio","family":"Di\u00a0Sipio","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7909-3902","authenticated-orcid":false,"given":"Juri","family":"Di\u00a0Rocco","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5077-6793","authenticated-orcid":false,"given":"Davide","family":"Di\u00a0Ruscio","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3666-4162","authenticated-orcid":false,"given":"Phuong T.","family":"Nguyen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,4,4]]},"reference":[{"key":"1102_CR1","doi-asserted-by":"crossref","unstructured":"Nguyen, P.\u00a0T., Di Rocco, J., Di Ruscio, D., Pierantonio, A., Iovino, L.: Automated classification of metamodel repositories: a machine learning approach. In: 2019 ACM\/IEEE 22nd International Conference on Model Driven Engineering Languages and Systems (MODELS), pp. 272\u2013282, Sep 2019","DOI":"10.1109\/MODELS.2019.00011"},{"key":"1102_CR2","doi-asserted-by":"publisher","first-page":"110860","DOI":"10.1016\/j.jss.2020.110860","volume":"172","author":"PT Nguyen","year":"2021","unstructured":"Nguyen, P.T., Ruscio, D.D., Pierantonio, A., Rocco, J.D., Iovino, L.: Convolutional neural networks for enhanced classification mechanisms of metamodels. J. Syst. Softw. 172, 110860 (2021)","journal-title":"J. Syst. Softw."},{"issue":"5","key":"1102_CR3","doi-asserted-by":"publisher","first-page":"1045","DOI":"10.1007\/s10270-020-00814-5","volume":"19","author":"G Mussbacher","year":"2020","unstructured":"Mussbacher, G., Combemale, B., Kienzle, J., Abrah\u00e3o, S., Ali, H., Bencomo, N., B\u00far, M., Burgue\u00f1o, L., Engels, G., Jeanjean, P., J\u00e9z\u00e9quel, J.-M., K\u00fchn, T., Mosser, S., Sahraoui, H., Syriani, E., Varr\u00f3, D., Weyssow, M.: Opportunities in intelligent modeling assistance. Softw. Syst. Model. 19(5), 1045\u20131053 (2020)","journal-title":"Softw. Syst. Model."},{"key":"1102_CR4","doi-asserted-by":"crossref","unstructured":"Burgue\u00f1o, L., Claris\u00f3, R., G\u00e9rard, S., Li, S., Cabot, J.: An nlp-based architecture for the autocompletion of partial domain models. In: M.\u00a0L. Rosa, S.\u00a0W. Sadiq, and E.\u00a0Teniente (eds.), Advanced Information Systems Engineering - 33rd International Conference, CAiSE 2021, Melbourne, VIC, Australia, June 28 - July 2, 2021, Proceedings, vol. 12751, pp. 91\u2013106. Springer, Heidelberg (2021)","DOI":"10.1007\/978-3-030-79382-1_6"},{"issue":"3","key":"1102_CR5","doi-asserted-by":"publisher","first-page":"1071","DOI":"10.1007\/s10270-022-00975-5","volume":"21","author":"M Weyssow","year":"2022","unstructured":"Weyssow, M., Sahraoui, H.A., Syriani, E.: Recommending metamodel concepts during modeling activities with pre-trained language models. Softw. Syst. Model. 21(3), 1071\u20131089 (2022)","journal-title":"Softw. Syst. Model."},{"key":"1102_CR6","doi-asserted-by":"crossref","unstructured":"Di\u00a0Rocco, J., Di\u00a0Sipio, C., Di\u00a0Ruscio, D., Nguyen, P.\u00a0T.: A gnn-based recommender system to assist the specification of metamodels and models. In: 2021 ACM\/IEEE 24th International Conference on Model Driven Engineering Languages and Systems (MODELS), pp. 70\u201381, (2021)","DOI":"10.1109\/MODELS50736.2021.00016"},{"key":"1102_CR7","unstructured":"JSON schema. http:\/\/json-schema.org\/. Accessed 29 Feb 2022"},{"key":"1102_CR8","doi-asserted-by":"crossref","unstructured":"Colantoni, A., Garmendia, A., Berardinelli, L., Wimmer, M., Br\u00e4uer, J.: Leveraging model-driven technologies for json artefacts: The shipyard case study. In: 2021 ACM\/IEEE 24th International Conference on Model Driven Engineering Languages and Systems (MODELS), pp. 250\u2013260, (2021)","DOI":"10.1109\/MODELS50736.2021.00033"},{"key":"1102_CR9","unstructured":"Sugiyama, M., Borgwardt, K.\u00a0M.: Halting in random walk kernels. In: Proceedings of the 28th International Conference on Neural Information Processing Systems\u2014Volume 1, NIPS\u201915, pp. 1639-1647, MIT Press, Cambridge, MA, USA, (2015)"},{"issue":"3","key":"1102_CR10","doi-asserted-by":"publisher","first-page":"967","DOI":"10.1007\/s10270-021-00929-3","volume":"21","author":"JAH L\u00f3pez","year":"2022","unstructured":"L\u00f3pez, J.A.H., C\u00e1novas Izquierdo, J.L., Cuadrado, J.S.: Modelset: a dataset for machine learning in model-driven engineering. Softw. Syst. Model. 21(3), 967\u2013986 (2022)","journal-title":"Softw. Syst. Model."},{"key":"1102_CR11","doi-asserted-by":"publisher","DOI":"10.1142\/7731","volume-title":"Graph classification and clustering based on vector space embedding","author":"K Riesen","year":"2010","unstructured":"Riesen, K., Bunke, H.: Graph classification and clustering based on vector space embedding. World Scientific Publishing Co. Inc., USA (2010)"},{"issue":"40","key":"1102_CR12","first-page":"1201","volume":"11","author":"S Vishwanathan","year":"2010","unstructured":"Vishwanathan, S., Schraudolph, N.N., Kondor, R., Borgwardt, K.M.: Graph kernels. J. Mach. Learn. Res. 11(40), 1201\u20131242 (2010)","journal-title":"J. Mach. Learn. Res."},{"key":"1102_CR13","doi-asserted-by":"crossref","unstructured":"Claris\u00f3, R., Cabot, J.: Applying graph kernels to model-driven engineering problems. In: Proceedings of the 1st International Workshop on Machine Learning and Software Engineering in Symbiosis, MASES 2018, pp. 1\u20135, Association for Computing Machinery, New York, NY, USA (2018)","DOI":"10.1145\/3243127.3243128"},{"key":"1102_CR14","unstructured":"Kriege, N.\u00a0M., Giscard, P.-L., Wilson, R.: On Valid Optimal Assignment Kernels and Applications to Graph Classification. In Advances in Neural Information Processing Systems, volume\u00a029. Curran Associates, Inc., 2016"},{"issue":"9","key":"1102_CR15","first-page":"12","volume":"2","author":"B Weisfeiler","year":"1968","unstructured":"Weisfeiler, B., Leman, A.: The reduction of a graph to canonical form and the algebra which appears therein. NTI Ser. 2(9), 12\u201316 (1968)","journal-title":"NTI Ser."},{"issue":"3","key":"1102_CR16","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1108\/eb046814","volume":"14","author":"M Porter","year":"1980","unstructured":"Porter, M.: An algorithm for suffix stripping. Program 14(3), 130\u2013137 (1980)","journal-title":"Program"},{"key":"1102_CR17","unstructured":"Siglidis, G., Nikolentzos, G., Limnios, S., Giatsidis, C., Skianis, K., Vazirgiannis, M.: GraKeL: A graph kernel library in Python. arXiv:1806.02193 [cs, stat], Mar 2020"},{"key":"1102_CR18","unstructured":"Babur, \u00d6.: A labeled Ecore metamodel dataset for domain clustering, (2019)"},{"key":"1102_CR19","unstructured":"GitHub. https:\/\/docs.github.com\/en\/rest\/overview\/resources-in-the-rest-api#rate-limiting. Accessed 29 Jan 2021"},{"key":"1102_CR20","unstructured":"GitHub Archive Dataset. https:\/\/console.cloud.google.com\/marketplace\/product\/github\/github-repos. Accessed 29 Jan 2021"},{"key":"1102_CR21","doi-asserted-by":"crossref","unstructured":"L\u00f3pez, J.\u00a0A.\u00a0H., Cuadrado, J.\u00a0S.: Mar: a structure-based search engine for models. In: Proceedings of the 23rd ACM\/IEEE International Conference on Model Driven Engineering Languages and Systems, MODELS \u201920, pp. 57\u201367, Association for Computing Machinery, New York, NY, USA (2020)","DOI":"10.1145\/3365438.3410947"},{"key":"1102_CR22","first-page":"1","volume":"2022","author":"J Di Rocco","year":"2022","unstructured":"Di Rocco, J., Di Ruscio, D., Di Sipio, C., Nguyen, P.T., Pierantonio, A.: Memorec: a recommender system for assisting modelers in specifying metamodels. Softw. Syst. Model. 2022, 1\u201321 (2022)","journal-title":"Softw. Syst. Model."},{"key":"1102_CR23","doi-asserted-by":"crossref","unstructured":"Nguyen, P.\u00a0T., Di Rocco, J., Di Ruscio, D., Ochoa, L., Degueule, T., Di Penta, M.: FOCUS: a recommender system for mining API function calls and usage patterns. In: Atlee, J.\u00a0M., Bultan, T., and Whittle, J. (eds.) Proceedings of the 41st International Conference on Software Engineering, ICSE 2019, Montreal, QC, Canada, May 25-31, 2019, pp. 1050\u20131060. IEEE \/ ACM, (2019)","DOI":"10.1109\/ICSE.2019.00109"},{"key":"1102_CR24","unstructured":"Raschka, S.: Model evaluation, model selection, and algorithm selection in machine learning. CoRR, abs\/1811.12808, 2018"},{"key":"1102_CR25","volume-title":"Recommendation systems in software engineering","year":"2014","unstructured":"Robillard, M.P., Maalej, W., Walker, R.J., Zimmermann, T. (eds.): Springer, Berlin, Heidelberg (2014)"},{"issue":"2","key":"1102_CR26","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1080\/00031305.1998.10480559","volume":"52","author":"JL Hintze","year":"1998","unstructured":"Hintze, J.L., Nelson, R.D.: Violin plots: a box plot-density trace synergism. Am. Stat. 52(2), 181\u2013184 (1998)","journal-title":"Am. Stat."},{"key":"1102_CR27","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1016\/j.scico.2019.05.003","volume":"180","author":"A Mora Segura","year":"2019","unstructured":"Mora Segura, A., de Lara, J.: Extremo: an Eclipse plugin for modelling and meta-modelling assistance. Sci. Comput. Program. 180, 71\u201380 (2019)","journal-title":"Sci. Comput. Program."},{"key":"1102_CR28","doi-asserted-by":"crossref","unstructured":"Dupont, G., Mustafiz, S., Khendek, F., Toeroe, M.: Building Domain-Specific Modelling Environments with Papyrus: An Experience Report. In 2018 IEEE\/ACM 10th International Workshop on Modelling in Software Engineering (MiSE), pp. 49\u201356, May 2018. ISSN: 2575-4475","DOI":"10.1145\/3193954.3193962"},{"key":"1102_CR29","doi-asserted-by":"crossref","unstructured":"Goldsby, H.\u00a0J., Cheng, B.\u00a0H.: Avida-MDE: a digital evolution approach to generating models of adaptive software behavior. In: Proceedings of the 10th annual conference on Genetic and evolutionary computation\u2014GECCO \u201908, p. 1751, ACM Press, Atlanta, GA, USA, (2008)","DOI":"10.1145\/1389095.1389434"},{"issue":"2","key":"1102_CR30","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1177\/0037549709340530","volume":"86","author":"S Sen","year":"2010","unstructured":"Sen, S., Baudry, B., Vangheluwe, H.: Towards domain-specific model editors with automatic model completion. Simulation 86(2), 109\u2013126 (2010)","journal-title":"Simulation"},{"key":"1102_CR31","doi-asserted-by":"crossref","unstructured":"Wang, K., Sullivan, A., Marinov, D., Khurshid, S.: Asketch: a sketching framework for alloy. In: Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (2018)","DOI":"10.1145\/3236024.3264594"},{"key":"1102_CR32","doi-asserted-by":"crossref","unstructured":"Batot, E., Sahraoui, H.: A generic framework for model-set selection for the unification of testing and learning MDE tasks. In: Proceedings of the ACM\/IEEE 19th International Conference on Model Driven Engineering Languages and Systems, pp. 374\u2013384, ACM, Saint-malo France, Oct 2016","DOI":"10.1145\/2976767.2976785"},{"issue":"4","key":"1102_CR33","doi-asserted-by":"publisher","first-page":"1323","DOI":"10.1007\/s10270-013-0392-y","volume":"14","author":"JJ L\u00f3pez-Fern\u00e1ndez","year":"2015","unstructured":"L\u00f3pez-Fern\u00e1ndez, J.J., Cuadrado, J.S., Guerra, E., de Lara, J.: Example-driven meta-model development. Softw. Syst. Model. 14(4), 1323\u20131347 (2015)","journal-title":"Softw. Syst. Model."},{"key":"1102_CR34","doi-asserted-by":"crossref","unstructured":"Kuschke, T., M\u00e4der, P., Rempel, P.: Recommending Auto-completions for Software Modeling Activities. In: Moreira, A., Sch\u00e4tz, B., Gray, J., Vallecillo, A., and Clarke, P. (eds.) Model-Driven Engineering Languages and Systems, Lecture Notes in Computer Science, pp. 170\u2013186, Springer, Berlin, Heidelberg (2013)","DOI":"10.1007\/978-3-642-41533-3_11"},{"key":"1102_CR35","doi-asserted-by":"crossref","unstructured":"Stephan, M.: Towards a cognizant virtual software modeling assistant using model clones. In: 2019 IEEE\/ACM 41st International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER), pp. 21\u201324, May 2019","DOI":"10.1109\/ICSE-NIER.2019.00014"},{"key":"1102_CR36","doi-asserted-by":"crossref","unstructured":"Saini, R., Mussbacher, G., Guo, J.\u00a0L.\u00a0C., Kienzle, J.: Domobot: a bot for automated and interactive domain modelling. In: Proceedings of the 23rd ACM\/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings, MODELS \u201920, Association for Computing Machinery, New York, NY, USA, (2020)","DOI":"10.1145\/3417990.3421385"},{"key":"1102_CR37","doi-asserted-by":"crossref","unstructured":"Li, X., Su, X., Wang, M.: Social network-based recommendation: a graph random walk kernel approach. In: Proceedings of the 12th ACM\/IEEE-CS Joint Conference on Digital Libraries, JCDL \u201912, pp. 409\u2013410, Association for Computing Machinery, New York, NY, USA (2012)","DOI":"10.1145\/2232817.2232915"},{"key":"1102_CR38","doi-asserted-by":"crossref","unstructured":"Ostuni, V.\u00a0C., Noia, T.\u00a0D., Mirizzi, R., Sciascio, E.\u00a0D.: A linked data recommender system using a neighborhood-based graph kernel. In International conference on electronic commerce and web technologies, pp. 89\u2013100. Springer, Heidelberg (2014)","DOI":"10.1007\/978-3-319-10491-1_10"},{"key":"1102_CR39","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/j.neunet.2012.03.001","volume":"31","author":"F Fouss","year":"2012","unstructured":"Fouss, F., Francoisse, K., Yen, L., Pirotte, A., Saerens, M.: An experimental investigation of kernels on graphs for collaborative recommendation and semisupervised classification. Neural Netw. 31, 53\u201372 (2012)","journal-title":"Neural Netw."},{"issue":"2","key":"1102_CR40","doi-asserted-by":"publisher","first-page":"880","DOI":"10.1016\/j.dss.2012.09.019","volume":"54","author":"X Li","year":"2013","unstructured":"Li, X., Chen, H.: Recommendation as link prediction in bipartite graphs: a graph kernel-based machine learning approach. Decis. Support Syst. 54(2), 880\u2013890 (2013)","journal-title":"Decis. Support Syst."},{"key":"1102_CR41","doi-asserted-by":"crossref","unstructured":"Xu, W., Xu, Z., Zhao, B.: A graph kernel based item similarity measure for top-n recommendation. In: Web Information Systems and Applications: 16th International Conference, WISA 2019, Qingdao, China, September 20-22, 2019, Proceedings, pp. 684\u2013689, Springer, Berlin, Heidelberg (2019)","DOI":"10.1007\/978-3-030-30952-7_69"}],"container-title":["Software and Systems Modeling"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10270-023-01102-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10270-023-01102-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10270-023-01102-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T17:55:47Z","timestamp":1729187747000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10270-023-01102-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,4]]},"references-count":41,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2023,10]]}},"alternative-id":["1102"],"URL":"https:\/\/doi.org\/10.1007\/s10270-023-01102-8","relation":{},"ISSN":["1619-1366","1619-1374"],"issn-type":[{"value":"1619-1366","type":"print"},{"value":"1619-1374","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,4]]},"assertion":[{"value":"1 April 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 February 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 March 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 April 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}