{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,25]],"date-time":"2025-11-25T06:57:35Z","timestamp":1764053855914,"version":"3.40.3"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031330223"},{"type":"electronic","value":"9783031330230"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-33023-0_13","type":"book-chapter","created":{"date-parts":[[2023,5,25]],"date-time":"2023-05-25T07:02:38Z","timestamp":1684998158000},"page":"148-160","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Constructing Low-Redundant and\u00a0High-Accuracy Knowledge Graphs for\u00a0Education"],"prefix":"10.1007","author":[{"given":"Wentao","family":"Li","sequence":"first","affiliation":[]},{"given":"Huachi","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Junnan","family":"Dong","sequence":"additional","affiliation":[]},{"given":"Qinggang","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Qing","family":"Li","sequence":"additional","affiliation":[]},{"given":"George","family":"Baciu","sequence":"additional","affiliation":[]},{"given":"Jiannong","family":"Cao","sequence":"additional","affiliation":[]},{"given":"Xiao","family":"Huang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,5,26]]},"reference":[{"key":"13_CR1","doi-asserted-by":"crossref","unstructured":"Abu-Salih, B.: Domain-specific knowledge graphs: a survey. J. Netw. Comput. Appl. 185 (2021)","DOI":"10.1016\/j.jnca.2021.103076"},{"key":"13_CR2","doi-asserted-by":"crossref","unstructured":"Agichtein, E., Gravano, L.: Snowball: extracting relations from large plain-text collections. In: Proceedings of the Fifth ACM Conference on Digital Libraries, pp. 85\u201394 (2000)","DOI":"10.1145\/375663.375774"},{"key":"13_CR3","doi-asserted-by":"crossref","unstructured":"Aliyu, I., Kana, A., Aliyu, S.: Development of knowledge graph for university courses management. Int. J. Educ. Manag. Eng. 2 (2020)","DOI":"10.5815\/ijeme.2020.02.01"},{"key":"13_CR4","doi-asserted-by":"crossref","unstructured":"Batista, D.S., Martins, B., Silva, M.J.: Semi-supervised bootstrapping of relationship extractors with distributional semantics. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 499\u2013504 (2015)","DOI":"10.18653\/v1\/D15-1056"},{"key":"13_CR5","doi-asserted-by":"crossref","unstructured":"Bosselut, A., Rashkin, H., Sap, M., Malaviya, C., Celikyilmaz, A., Choi, Y.: COMET: commonsense transformers for automatic knowledge graph construction. In: Annual Meeting of the Association for Computational Linguistics, pp. 4762\u20134779 (2019)","DOI":"10.18653\/v1\/P19-1470"},{"key":"13_CR6","doi-asserted-by":"publisher","first-page":"31553","DOI":"10.1109\/ACCESS.2018.2839607","volume":"6","author":"P Chen","year":"2018","unstructured":"Chen, P., Lu, Y., Zheng, V.W., Chen, X., Yang, B.: KnowEdu: a system to construct knowledge graph for education. IEEE Access 6, 31553\u201331563 (2018)","journal-title":"IEEE Access"},{"issue":"5","key":"13_CR7","doi-asserted-by":"publisher","first-page":"1200","DOI":"10.1007\/s11390-020-0328-2","volume":"36","author":"FR Dang","year":"2021","unstructured":"Dang, F.R., Tang, J.T., Pang, K.Y., Wang, T., Li, S.S., Li, X.: Constructing an educational knowledge graph with concepts linked to Wikipedia. J. Comput. Sci. Technol. 36(5), 1200\u20131211 (2021)","journal-title":"J. Comput. Sci. Technol."},{"key":"13_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1007\/978-3-030-62466-8_9","volume-title":"The Semantic Web \u2013 ISWC 2020","author":"D Dess\u00ec","year":"2020","unstructured":"Dess\u00ec, D., Osborne, F., Reforgiato Recupero, D., Buscaldi, D., Motta, E., Sack, H.: AI-KG: an automatically generated knowledge graph of artificial intelligence. In: Pan, J.Z., et al. (eds.) ISWC 2020. LNCS, vol. 12507, pp. 127\u2013143. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-62466-8_9"},{"issue":"6","key":"13_CR9","doi-asserted-by":"publisher","first-page":"707","DOI":"10.1007\/s00778-015-0394-1","volume":"24","author":"L Gal\u00e1rraga","year":"2015","unstructured":"Gal\u00e1rraga, L., Teflioudi, C., Hose, K., Suchanek, F.M.: Fast rule mining in ontological knowledge bases with AMIE. Int. J. Very Large Data Bases 24(6), 707\u2013730 (2015)","journal-title":"Int. J. Very Large Data Bases"},{"key":"13_CR10","doi-asserted-by":"crossref","unstructured":"Gal\u00e1rraga, L.A., Teflioudi, C., Hose, K., Suchanek, F.: AMIE: association rule mining under incomplete evidence in ontological knowledge bases. In: International World Wide Web Conference, pp. 413\u2013422 (2013)","DOI":"10.1145\/2488388.2488425"},{"key":"13_CR11","doi-asserted-by":"crossref","unstructured":"Li, N., Shen, Q., Song, R., Chi, Y., Xu, H.: MEduKG: a deep-learning-based approach for multi-modal educational knowledge graph construction. Information 13(2) (2022)","DOI":"10.3390\/info13020091"},{"key":"13_CR12","doi-asserted-by":"crossref","unstructured":"Li, X., et al.: Entity-relation extraction as multi-turn question answering. In: Annual Meeting of the Association for Computational Linguistics, pp. 1340\u20131350 (2019)","DOI":"10.18653\/v1\/P19-1129"},{"key":"13_CR13","doi-asserted-by":"crossref","unstructured":"Li, Y., Zhao, J., Yang, L., Zhang, Y.: Construction, visualization and application of knowledge graph of computer science major. In: International Conference on Big Data and Education, pp. 43\u201347 (2019)","DOI":"10.1145\/3322134.3322153"},{"key":"13_CR14","doi-asserted-by":"crossref","unstructured":"Liang, C., Ye, J., Wu, Z., Pursel, B., Giles, C.L.: Recovering concept prerequisite relations from university course dependencies. In: AAAI Conference on Artificial Intelligence (2017)","DOI":"10.1609\/aaai.v31i1.10550"},{"key":"13_CR15","unstructured":"Mahdisoltani, F., Biega, J., Suchanek, F.M.: YAGO3: a knowledge base from multilingual Wikipedias. In: Conference on Innovative Data Systems Research (2015)"},{"key":"13_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"408","DOI":"10.1007\/978-3-319-25007-6_24","volume-title":"The Semantic Web - ISWC 2015","author":"F Osborne","year":"2015","unstructured":"Osborne, F., Motta, E.: Klink-2: integrating multiple web sources to generate semantic topic networks. In: Arenas, M., et al. (eds.) ISWC 2015. LNCS, vol. 9366, pp. 408\u2013424. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-25007-6_24"},{"key":"13_CR17","doi-asserted-by":"crossref","unstructured":"Petroni, F., et al.: Language models as knowledge bases? In: Conference on Empirical Methods in Natural Language Processing, pp. 2463\u20132473 (2019)","DOI":"10.18653\/v1\/D19-1250"},{"key":"13_CR18","doi-asserted-by":"crossref","unstructured":"Qin, Y., Cao, H., Xue, L.: Research and application of knowledge graph in teaching: take the database course as an example. In: Journal of Physics: Conference Series, vol. 1607 (2020)","DOI":"10.1088\/1742-6596\/1607\/1\/012127"},{"key":"13_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1007\/978-3-030-00668-6_12","volume-title":"The Semantic Web \u2013 ISWC 2018","author":"AA Salatino","year":"2018","unstructured":"Salatino, A.A., Thanapalasingam, T., Mannocci, A., Osborne, F., Motta, E.: The computer science ontology: a large-scale taxonomy of research areas. In: Vrande\u010di\u0107, D., et al. (eds.) ISWC 2018. LNCS, vol. 11137, pp. 187\u2013205. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00668-6_12"},{"key":"13_CR20","doi-asserted-by":"publisher","first-page":"105618","DOI":"10.1016\/j.knosys.2020.105618","volume":"195","author":"D Shi","year":"2020","unstructured":"Shi, D., Wang, T., Xing, H., Xu, H.: A learning path recommendation model based on a multidimensional knowledge graph framework for e-learning. Knowl.-Based Syst. 195, 105618 (2020)","journal-title":"Knowl.-Based Syst."},{"key":"13_CR21","doi-asserted-by":"crossref","unstructured":"Stewart, M., Liu, W.: Seq2kg: an end-to-end neural model for domain agnostic knowledge graph (not text graph) construction from text. In: Proceedings of the International Conference on Principles of Knowledge Representation and Reasoning, vol. 17, pp. 748\u2013757 (2020)","DOI":"10.24963\/kr.2020\/77"},{"key":"13_CR22","doi-asserted-by":"crossref","unstructured":"Su, Y., Zhang, Y.: Automatic construction of subject knowledge graph based on educational big data. In: International Conference on Big Data and Education, pp. 30\u201336 (2020)","DOI":"10.1145\/3396452.3396458"},{"key":"13_CR23","doi-asserted-by":"crossref","unstructured":"Sun, H., Li, Y., Zhang, Y.: ConLearn: contextual-knowledge-aware concept prerequisite relation learning with graph neural network. In: SIAM International Conference on Data Mining, pp. 118\u2013126 (2022)","DOI":"10.1137\/1.9781611977172.14"},{"issue":"10","key":"13_CR24","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 57(10), 78\u201385 (2014)","journal-title":"Commun. ACM"},{"key":"13_CR25","doi-asserted-by":"crossref","unstructured":"Wadden, D., Wennberg, U., Luan, Y., Hajishirzi, H.: Entity, relation, and event extraction with contextualized span representations. arXiv preprint: arXiv:1909.03546 (2019)","DOI":"10.18653\/v1\/D19-1585"},{"key":"13_CR26","doi-asserted-by":"crossref","unstructured":"Wang, H., Zhao, M., Xie, X., Li, W., Guo, M.: Knowledge graph convolutional networks for recommender systems. In: International World Wide Web Conference, pp. 3307\u20133313 (2019)","DOI":"10.1145\/3308558.3313417"}],"container-title":["Lecture Notes in Computer Science","Learning Technologies and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-33023-0_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T16:04:47Z","timestamp":1693584287000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-33023-0_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031330223","9783031330230"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-33023-0_13","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"26 May 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICWL","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Web-Based Learning","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tenerife","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 November 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 November 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icwl2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.icwl-sete.eu\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"double blind","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"82","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"45","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"55% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"presented together with SETE 2022 - joint volume with ICWL and SETE 2022","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}