{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T20:34:32Z","timestamp":1780691672891,"version":"3.54.1"},"reference-count":25,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2019,12,1]],"date-time":"2019-12-01T00:00:00Z","timestamp":1575158400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2019,12,12]],"date-time":"2019-12-12T00:00:00Z","timestamp":1576108800000},"content-version":"vor","delay-in-days":11,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Smart Learn. Environ."],"published-print":{"date-parts":[[2019,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Learning basic concepts before complex ones is a natural form of learning. Automated systems and instructional designers evaluate and order concepts\u2019 complexity to successfully generate and recommend or adapt learning paths. This paper addresses the specific challenge of accurately and adequately identifying concept prerequisites using semantic web technologies for a basic understanding of a particular concept within the context of learning: given a target concept<jats:italic>c<\/jats:italic>, the goals are to (a) find candidate concepts that serve as possible prerequisite for<jats:italic>c<\/jats:italic>; and, (b) evaluate the prerequisite relation between the target and candidates concepts via a supervised learning model. Our four step approach consists of (i) an exploration of Knowledge Graphs in order to identify possible candidate concepts; (ii) the creation of a set of potential concepts; (iii) deployment of supervised learning model to evaluate a proposed list of prerequisite relationships regarding the target set; and, (iv) validation of our approaching using a ground truth of 80 concepts from different domains (with a precision varying between 76% and 96%).<\/jats:p>","DOI":"10.1186\/s40561-019-0104-3","type":"journal-article","created":{"date-parts":[[2019,12,12]],"date-time":"2019-12-12T18:02:35Z","timestamp":1576173755000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Exploring knowledge graphs for the identification of concept prerequisites"],"prefix":"10.1186","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8742-2094","authenticated-orcid":false,"given":"Rub\u00e9n","family":"Manrique","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bernardo","family":"Pereira","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Olga","family":"Mari\u00f1o","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2019,12,12]]},"reference":[{"key":"104_CR1","unstructured":"Adorni, G., Alzetta, C., Koceva, F., Passalacqua, S., Torre, I. (2019). Artificial Intelligence in Education. In: Isotani, S., Mill\u00e1n, E., Ogan, A., Hastings, P., McLaren, B., Luckin, R. (Eds.)Springer, Cham, (pp. 1\u201313)."},{"issue":"1","key":"104_CR2","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1145\/2505349","volume":"6","author":"S. Changuel","year":"2015","unstructured":"Changuel, S., Labroche, N., Bouchon-Meunier, B. (2015). Resources sequencing using automatic prerequisite\u2013outcome annotation. ACM Trans. Intell. Syst. Technol., 6(1), 6\u20131630. https:\/\/doi.org\/10.1145\/2505349.","journal-title":"ACM Trans. Intell. Syst. Technol."},{"issue":"1","key":"104_CR3","first-page":"321","volume":"16","author":"N.V. Chawla","year":"2002","unstructured":"Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P. (2002). Smote: Synthetic minority over-sampling technique. J. Artif. Int. Res., 16(1), 321\u2013357.","journal-title":"J. Artif. Int. Res."},{"key":"104_CR4","doi-asserted-by":"publisher","first-page":"785","DOI":"10.1145\/2939672.2939785","volume-title":"Proceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD \u201916","author":"T. Chen","year":"2016","unstructured":"Chen, T., & Guestrin, C. (2016). Xgboost: A scalable tree boosting system. In Proceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD \u201916. https:\/\/doi.org\/10.1145\/2939672.2939785. http:\/\/doi.acm.org\/10.1145\/2939672.2939785. ACM, New York, (pp. 785\u2013794)."},{"key":"104_CR5","first-page":"227","volume-title":"Text2Onto","year":"2005","unstructured":"Cimiano, P., & V\u00f6lker, J. (2005) In Montoyo, A., Mu\u0144oz, R., M\u00e9tais, E. (Eds.), Text2Onto, (pp. 227\u2013238). Berlin, Heidelberg: Springer."},{"key":"104_CR6","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1007\/978-3-642-30284-8_9","volume-title":"The Semantic Web: Research and Applications","author":"D. Damljanovic","year":"2012","unstructured":"Damljanovic, D., Stankovic, M., Laublet, P. (2012). Linked data-based concept recommendation: Comparison of different methods in open innovation scenario. In: Simperl, E., Cimiano, P., Polleres, A., Corcho, O., Presutti, V. (Eds.) In The Semantic Web: Research and Applications. Springer, Berlin, Heidelberg, (pp. 24\u201338)."},{"key":"104_CR7","doi-asserted-by":"publisher","first-page":"611","DOI":"10.18653\/v1\/P18-1057","volume-title":"Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","author":"A. Fabbri","year":"2018","unstructured":"Fabbri, A., Li, I., Trairatvorakul, P., He, Y., Ting, W., Tung, R., Westerfield, C., Radev, D. (2018). Tutorialbank: A manually-collected corpus for prerequisite chains, survey extraction and resource recommendation. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). https:\/\/www.aclweb.org\/anthology\/P18-1057. https:\/\/doii.org\/10.18653\/v1\/P18-1057. Association for Computational Linguistics, Melbourne, (pp. 611\u201320)."},{"issue":"1","key":"104_CR8","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1109\/MS.2011.122","volume":"29","author":"P. Ferragina","year":"2012","unstructured":"Ferragina, P., & Scaiella, U. (2012). Fast and accurate annotation of short texts with wikipedia pages. IEEE Software, 29(1), 70\u201375. https:\/\/doi.org\/10.1109\/MS.2011.122.","journal-title":"IEEE Software"},{"issue":"5","key":"104_CR9","doi-asserted-by":"publisher","first-page":"595","DOI":"10.1016\/j.tele.2017.05.007","volume":"35","author":"F. Gasparetti","year":"2018","unstructured":"Gasparetti, F., Medio, C.D., Limongelli, C., Sciarrone, F., Temperini, M. (2018). Prerequisites between learning objects: Automatic extraction based on a machine learning approach. Telematics Informa., 35(5), 595\u2013610. http:\/\/www.sciencedirect.com\/science\/article\/pii\/S0736585316304890. https:\/\/doi.org\/10.1016\/j.tele.2017.05.007.","journal-title":"Telematics Informa."},{"key":"104_CR10","doi-asserted-by":"publisher","first-page":"866","DOI":"10.18653\/v1\/P16-1082","volume-title":"Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","author":"J. Gordon","year":"2016","unstructured":"Gordon, J., Zhu, L., Galstyan, A., Natarajan, P., Burns, G. (2016). Modeling Concept Dependencies in a Scientific Corpus. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). https:\/\/www.aclweb.org\/anthology\/P16-1082. https:\/\/doi.org\/10.18653\/v1\/P16-1082. Association for Computational Linguistics, Berlin, (pp. 866\u201375)."},{"key":"104_CR11","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1145\/2567948.2577353","volume-title":"Proceedings of the 23rd International Conference on World Wide Web","author":"P. Kapanipathi","year":"2014","unstructured":"Kapanipathi, P., Jain, P., Venkataramani, C., Sheth, A (2014). Hierarchical Interest Graph from Tweets. In Proceedings of the 23rd International Conference on World Wide Web. http:\/\/doi.acm.org\/10.1145\/2567948.2577353. https:\/\/doi.org\/10.1145\/2567948.2577353. ACM, New York, (pp. 311\u20132)."},{"issue":"1","key":"104_CR12","doi-asserted-by":"publisher","first-page":"159","DOI":"10.2307\/2529310","volume":"33","author":"J. Landis","year":"1977","unstructured":"Landis, J., & Koch, G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33(1), 159\u2013174. https:\/\/doi.org\/10.2307\/2529310.","journal-title":"Biometrics"},{"key":"104_CR13","first-page":"3","volume-title":"Concepts: Core Readings","author":"S. Laurence","year":"1999","unstructured":"Laurence, S., & Margolis, E. (1999). Concepts and cognitive science. In: Margolis, E., & Laurence, S. (Eds.) In Concepts: Core Readings. MIT Press, USA, (pp. 3\u201381)."},{"key":"104_CR14","volume-title":"Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing","author":"C. Liang","year":"2015","unstructured":"Liang, C., Wu, Z., Huang, W., Giles, C.L. (2015). Measuring prerequisite relations among concepts. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Lisbon, Portugal."},{"key":"104_CR15","doi-asserted-by":"crossref","unstructured":"Liang, C., Ye, J., Wu, Z., Pursel, B., Giles, C.L. (2017). Recovering concept prerequisite relations from university course dependencies. In In the 7th Symposium on Educational Advances in Artificial Intelligence, (pp. 4786\u20134791).","DOI":"10.1609\/aaai.v31i1.10550"},{"key":"104_CR16","doi-asserted-by":"crossref","unstructured":"Liang, C., Ye, J., Wang, S., Pursel, B., Giles, C.L. (2018). Investigating active learning for concept prerequisite learning. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, New Orleans, Louisiana, USA, February 2-7, 2018.","DOI":"10.1609\/aaai.v32i1.11396"},{"key":"104_CR17","doi-asserted-by":"publisher","unstructured":"Manrique, R., Sosa, J., Marino, O., Nunes, B.P., Cardozo, N. (2018). Investigating learning resources precedence relations via concept prerequisite learning. In 2018 IEEE\/WIC\/ACM International Conference on Web Intelligence (WI). https:\/\/doi.org\/10.1109\/WI.2018.00-89, (pp. 198\u2013205).","DOI":"10.1109\/WI.2018.00-89"},{"key":"104_CR18","first-page":"23","volume-title":"Diversified semantic query reformulation","year":"2017","unstructured":"Manrique, R., & Mari\u00f1o, O. (2017) In R\u00f3\u017bewski, P., & Lange, C. (Eds.), Diversified semantic query reformulation, (pp. 23\u201337). Cham: Springer."},{"key":"104_CR19","doi-asserted-by":"publisher","unstructured":"Manrique, R., Pereira, B., Marino, O., Cardozo, N., Wolfgand, S. (2019). Towards the Identification of Concept Prerequisites Via Knowledge Graphs. In 2019 IEEE 19th International Conference on Advanced Learning Technologies (ICALT). https:\/\/doi.org\/10.1109\/ICALT.2019.00101, (pp. 332\u20136).","DOI":"10.1109\/ICALT.2019.00101"},{"key":"104_CR20","first-page":"139","volume-title":"Interlinking Documents Based on Semantic Graphs with an Application","year":"2015","unstructured":"Nunes, B.P., Fetahu, B., Kawase, R., Dietze, S., Casanova, M.A., Maynard, D. (2015) In Tweedale, J.W., Jain, L.C., Watada, J., Howlett, R.J. (Eds.), Interlinking Documents Based on Semantic Graphs with an Application, (pp. 139\u2013155). Cham: Springer."},{"key":"104_CR21","doi-asserted-by":"publisher","unstructured":"Pan, L., Li, C., Li, J., Tang, J. (2017). Prerequisite relation learning for concepts in moocs. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/P17-1133, (pp. 1447\u201356).","DOI":"10.18653\/v1\/P17-1133"},{"issue":"3","key":"104_CR22","doi-asserted-by":"publisher","first-page":"489","DOI":"10.3233\/SW-160218","volume":"8","author":"H. Paulheim","year":"2017","unstructured":"Paulheim, H. (2017). Knowledge graph refinement: A survey of approaches and evaluation methods. Semantic Web, 8(3), 489\u2013508. https:\/\/doi.org\/10.3233\/SW-160218.","journal-title":"Semantic Web"},{"key":"104_CR23","first-page":"307","volume-title":"Proceedings of the Seventh Workshop on Building Educational Applications Using NLP","author":"P. Talukdar","year":"2012","unstructured":"Talukdar, P., & Cohen, W. (2012). Crowdsourced comprehension: Predicting prerequisite structure in wikipedia. In Proceedings of the Seventh Workshop on Building Educational Applications Using NLP. https:\/\/www.aclweb.org\/anthology\/W12-2037. Association for Computational Linguistics, Montr\u00e9al, (pp. 307\u201315)."},{"key":"104_CR24","first-page":"227","volume-title":"Pagerank on wikipedia: Towards general importance scores for entities","year":"2016","unstructured":"Thalhammer, A., & Rettinger, A. (2016) In Sack, H., Rizzo, G., Steinmetz, N., Mladeni\u0107, D., Auer, S., Lange, C. (Eds.), Pagerank on wikipedia: Towards general importance scores for entities, (pp. 227\u2013240). Cham: Springer."},{"key":"104_CR25","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1145\/2983323.2983725","volume-title":"Proceedings of CIKM \u201916","author":"S. Wang","year":"2016","unstructured":"Wang, S., Ororbia, A., Wu, Z., Williams, K., Liang, C., Pursel, B., Giles, C.L. (2016). Using prerequisites to extract concept maps from textbooks. In Proceedings of CIKM \u201916. https:\/\/doi.org\/10.1145\/2983323.2983725. ACM, New York, (pp. 317\u2013326)."}],"container-title":["Smart Learning Environments"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s40561-019-0104-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/s40561-019-0104-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s40561-019-0104-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,23]],"date-time":"2023-09-23T22:55:35Z","timestamp":1695509735000},"score":1,"resource":{"primary":{"URL":"https:\/\/slejournal.springeropen.com\/articles\/10.1186\/s40561-019-0104-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12]]},"references-count":25,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2019,12]]}},"alternative-id":["104"],"URL":"https:\/\/doi.org\/10.1186\/s40561-019-0104-3","relation":{},"ISSN":["2196-7091"],"issn-type":[{"value":"2196-7091","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,12]]},"assertion":[{"value":"28 August 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 November 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 December 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare that they have no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"21"}}