{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,12]],"date-time":"2025-05-12T15:48:18Z","timestamp":1747064898140,"version":"3.40.3"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031456817"},{"type":"electronic","value":"9783031456824"}],"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\/978-3-031-45682-4_13","type":"book-chapter","created":{"date-parts":[[2023,10,22]],"date-time":"2023-10-22T23:03:03Z","timestamp":1698015783000},"page":"173-185","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Knowledge Graph for\u00a0Retail Commerce"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-7317-7145","authenticated-orcid":false,"given":"Ronghao","family":"Pan","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3651-2660","authenticated-orcid":false,"given":"Jos\u00e9 Antonio","family":"Garc\u00eda-D\u00edaz","sequence":"additional","affiliation":[]},{"given":"Diego","family":"Rold\u00e1n","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2457-1791","authenticated-orcid":false,"given":"Rafael","family":"Valencia-Garc\u00eda","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,23]]},"reference":[{"key":"13_CR1","doi-asserted-by":"publisher","unstructured":"Beydoun, G., et al.: Cooperative modelling evaluated. Int. J. Cooperative Inf. Syst. 14(1), 45\u201371 (2005). https:\/\/doi.org\/10.1142\/S0218843005001080","DOI":"10.1142\/S0218843005001080"},{"key":"13_CR2","doi-asserted-by":"crossref","unstructured":"Bhartiya, A., Badola, K.: Mausam: Dis-Rex: a multilingual dataset for distantly supervised relation extraction (2021)","DOI":"10.18653\/v1\/2022.acl-short.95"},{"key":"13_CR3","doi-asserted-by":"publisher","unstructured":"Bounhas, I., Soudani, N., Slimani, Y.: Building a morpho-semantic knowledge graph for Arabic information retrieval. Inf. Process. Manage. 57(6), 102124 (2020). https:\/\/doi.org\/10.1016\/j.ipm.2019.102124, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0306457319302912","DOI":"10.1016\/j.ipm.2019.102124"},{"key":"13_CR4","doi-asserted-by":"publisher","unstructured":"Chen, Y., Wu, L., Zaki, M.J.: Toward subgraph-guided knowledge graph question generation with graph neural networks. IEEE Trans. Neural Netw. Learn. Syst., 1\u201312 (2023). https:\/\/doi.org\/10.1109\/TNNLS.2023.3264519","DOI":"10.1109\/TNNLS.2023.3264519"},{"key":"13_CR5","doi-asserted-by":"publisher","unstructured":"Garc\u00eda-S\u00e1nchez, F., Valencia-Garc\u00eda, R., Mart\u00ednez-B\u00e9jar, R.: An integrated approach for developing e-commerce applications. Expert Syst. Appl. 28(2), 223\u2013235 (2005). https:\/\/doi.org\/10.1016\/j.eswa.2004.10.004, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0957417404001137","DOI":"10.1016\/j.eswa.2004.10.004"},{"key":"13_CR6","doi-asserted-by":"publisher","unstructured":"Guia., J., Gon\u00c7alves Soares., V., Bernardino., J.: Graph databases: Neo4j analysis. In: Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 1: ICEIS, pp. 351\u2013356. INSTICC, SciTePress (2017). https:\/\/doi.org\/10.5220\/0006356003510356","DOI":"10.5220\/0006356003510356"},{"key":"13_CR7","doi-asserted-by":"publisher","unstructured":"Harnoune, A., Rhanoui, M., Mikram, M., Yousfi, S., Elkaimbillah, Z., El Asri, B.: BERT based clinical knowledge extraction for biomedical knowledge graph construction and analysis. Comput. Methods Program. Biomed. Update 1, 100042 (2021). https:\/\/doi.org\/10.1016\/j.cmpbup.2021.100042, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2666990021000410","DOI":"10.1016\/j.cmpbup.2021.100042"},{"key":"13_CR8","doi-asserted-by":"crossref","unstructured":"Huguet Cabot, P.L., Navigli, R.: REBEL: relation extraction by end-to-end language generation. In: Findings of the Association for Computational Linguistics: EMNLP 2021. Association for Computational Linguistics, Online and in the Barcel\u00f3 B\u00e1varo Convention Centre, Punta Cana, Dominican Republic (2021)","DOI":"10.18653\/v1\/2021.findings-emnlp.204"},{"key":"13_CR9","doi-asserted-by":"crossref","unstructured":"Huguet Cabot, P.L., Navigli, R.: REBEL: relation extraction by end-to-end language generation. In: Findings of the Association for Computational Linguistics: EMNLP 2021, pp. 2370\u20132381. Association for Computational Linguistics, Punta Cana, Dominican Republic (2021). https:\/\/aclanthology.org\/2021.findings-emnlp.204","DOI":"10.18653\/v1\/2021.findings-emnlp.204"},{"key":"13_CR10","doi-asserted-by":"publisher","unstructured":"Khan, S., Naseer, M., Hayat, M., Zamir, S.W., Khan, F.S., Shah, M.: Transformers in vision: a survey. ACM Comput. Surv. 54(10s), 1\u201341 (2022). https:\/\/doi.org\/10.1145\/3505244,https:\/\/doi.org\/10.1145%2F3505244","DOI":"10.1145\/3505244"},{"key":"13_CR11","doi-asserted-by":"publisher","unstructured":"K\u00f6ksal, A., \u00d6zg\u00fcr, A.: The RELX dataset and matching the multilingual blanks for cross-lingual relation classification. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 340\u2013350. Association for Computational Linguistics, Online (2020). https:\/\/doi.org\/10.18653\/v1\/2020.findings-emnlp.32, https:\/\/www.aclweb.org\/anthology\/2020.findings-emnlp.32","DOI":"10.18653\/v1\/2020.findings-emnlp.32"},{"issue":"1","key":"13_CR12","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1109\/JPROC.2015.2483592","volume":"104","author":"M Nickel","year":"2016","unstructured":"Nickel, M., Murphy, K., Tresp, V., Gabrilovich, E.: A review of relational machine learning for knowledge graphs. Proc. IEEE 104(1), 11\u201333 (2016). https:\/\/doi.org\/10.1109\/JPROC.2015.2483592","journal-title":"Proc. IEEE"},{"key":"13_CR13","unstructured":"Pawar, S., Palshikar, G.K., Bhattacharyya, P.: Relation extraction : a survey. CoRR abs\/1712.05191 (2017). https:\/\/arxiv.org\/abs\/1712.05191"},{"key":"13_CR14","doi-asserted-by":"publisher","unstructured":"Qu, M., Ren, X., Zhang, Y., Han, J.: Weakly-supervised relation extraction by pattern-enhanced embedding learning. In: Proceedings of the 2018 World Wide Web Conference, WWW \u201918, pp. 1257\u20131266. International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE (2018). https:\/\/doi.org\/10.1145\/3178876.3186024","DOI":"10.1145\/3178876.3186024"},{"key":"13_CR15","doi-asserted-by":"publisher","unstructured":"Ravikumar, K., Rastegar-Mojarad, M., Liu, H.: BELMiner: adapting a rule-based relation extraction system to extract biological expression language statements from bio-medical literature evidence sentences. Database 2017, baw156 (2017). https:\/\/doi.org\/10.1093\/database\/baw156","DOI":"10.1093\/database\/baw156"},{"key":"13_CR16","doi-asserted-by":"crossref","unstructured":"Rossiello, G., Chowdhury, M.F.M., Mihindukulasooriya, N., Cornec, O., Gliozzo, A.: KnowGL: knowledge generation and linking from text. In: Proceedings of the AAAI Conference on Artificial Intelligence (2023)","DOI":"10.1609\/aaai.v37i13.27084"},{"key":"13_CR17","doi-asserted-by":"publisher","unstructured":"Ruiz-S\u00e1nchez, J.M., Valencia-Garc\u00eda, R., Fern\u00e1ndez-Breis, J.T., Mart\u00ednez-B\u00e9jar, R., Compton, P.: An approach for incremental knowledge acquisition from text. Expert Syst. Appl. 25(1), 77\u201386 (2003). https:\/\/doi.org\/10.1016\/S0957-4174(03)00008-3, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0957417403000083","DOI":"10.1016\/S0957-4174(03)00008-3"},{"key":"13_CR18","doi-asserted-by":"publisher","unstructured":"Sun, R., et al.: Multi-modal knowledge graphs for recommender systems. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, CIKM \u201920, pp. 1405\u20131414. Association for Computing Machinery, New York (2020). https:\/\/doi.org\/10.1145\/3340531.3411947","DOI":"10.1145\/3340531.3411947"},{"key":"13_CR19","unstructured":"Tiedemann, J., Thottingal, S.: OPUS-MT - building open translation services for the world. In: Proceedings of the 22nd Annual Conference of the European Association for Machine Translation, pp. 479\u2013480. European Association for Machine Translation, Lisboa (2020). https:\/\/aclanthology.org\/2020.eamt-1.61"},{"key":"13_CR20","doi-asserted-by":"crossref","unstructured":"Tran, T.T., Le, P., Ananiadou, S.: Revisiting unsupervised relation extraction. CoRR abs\/2005.00087 (2020). https:\/\/arxiv.org\/abs\/2005.00087","DOI":"10.18653\/v1\/2020.acl-main.669"},{"key":"13_CR21","doi-asserted-by":"publisher","unstructured":"Valencia-Garc\u00eda, R., Ruiz-S\u00e1nchez, J.M., Vivancos-Vicente, P.J., Fern\u00e1ndez-Breis, J.T., Mart\u00ednez-B\u00e9jar, R.: An incremental approach for discovering medical knowledge from texts. Expert Syst. Appl. 26(3), 291\u2013299 (2004). https:\/\/doi.org\/10.1016\/j.eswa.2003.09.001, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S095741740300160X","DOI":"10.1016\/j.eswa.2003.09.001"},{"key":"13_CR22","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc. (2017)"},{"key":"13_CR23","doi-asserted-by":"publisher","unstructured":"Wolf, T., et al.: Transformers: state-of-the-art natural language processing. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pp. 38\u201345. Association for Computational Linguistics, Online (2020). https:\/\/doi.org\/10.18653\/v1\/2020.emnlp-demos.6, https:\/\/aclanthology.org\/2020.emnlp-demos.6","DOI":"10.18653\/v1\/2020.emnlp-demos.6"}],"container-title":["Communications in Computer and Information Science","Technologies and Innovation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-45682-4_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,22]],"date-time":"2023-10-22T23:04:37Z","timestamp":1698015877000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-45682-4_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031456817","9783031456824"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-45682-4_13","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"23 October 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CITI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Technologies and Innovation","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Guayaquil","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ecuador","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"citi2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/congresos.uagraria.edu.ec\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"OpenReview.net","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"51","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":"20","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":"0","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":"39% - 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":"3","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)"}}]}}