{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T18:40:00Z","timestamp":1742928000199,"version":"3.40.3"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031703775"},{"type":"electronic","value":"9783031703782"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-70378-2_2","type":"book-chapter","created":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T09:02:05Z","timestamp":1725181325000},"page":"21-37","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Guiding Catalogue Enrichment with\u00a0User Queries"],"prefix":"10.1007","author":[{"given":"Yupei","family":"Du","sequence":"first","affiliation":[]},{"given":"Jacek","family":"Golebiowski","sequence":"additional","affiliation":[]},{"given":"Philipp","family":"Schmidt","sequence":"additional","affiliation":[]},{"given":"Ziawasch","family":"Abedjan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,8,22]]},"reference":[{"key":"2_CR1","unstructured":"Arias, M., Fern\u00e1ndez, J.D., Mart\u00ednez-Prieto, M.A., de\u00a0la Fuente, P.: An empirical study of real-world sparql queries. arXiv preprint arXiv:1103.5043 (2011)"},{"key":"2_CR2","unstructured":"Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: Advances in Neural Information Processing Systems, vol.\u00a026. Curran Associates, Inc. (2013)"},{"key":"2_CR3","unstructured":"Brickley, D.: Rdf vocabulary description language 1.0: Rdf schema (2004). http:\/\/www.w3.org\/TR\/rdf-schema\/"},{"key":"2_CR4","doi-asserted-by":"publisher","first-page":"192435","DOI":"10.1109\/ACCESS.2020.3030076","volume":"8","author":"Z Chen","year":"2020","unstructured":"Chen, Z., Wang, Y., Zhao, B., Cheng, J., Zhao, X., Duan, Z.: Knowledge graph completion: a review. IEEE Access 8, 192435\u2013192456 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.3030076","journal-title":"IEEE Access"},{"key":"2_CR5","doi-asserted-by":"crossref","unstructured":"Dettmers, T., Minervini, P., Stenetorp, P., Riedel, S.: Convolutional 2d knowledge graph embeddings. In: Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence and Thirtieth Innovative Applications of Artificial Intelligence Conference and Eighth AAAI Symposium on Educational Advances in Artificial Intelligence. AAAI 2018\/IAAI 2018\/EAAI 2018 (2018)","DOI":"10.1609\/aaai.v32i1.11573"},{"key":"2_CR6","doi-asserted-by":"crossref","unstructured":"Dong, X.L., et al.: Autoknow: self-driving knowledge collection for products of thousands of types. In: KDD 2020 (2020). https:\/\/www.amazon.science\/publications\/autoknow-self-driving-knowledge-collection-for-products-of-thousands-of-types","DOI":"10.1145\/3394486.3403323"},{"key":"2_CR7","doi-asserted-by":"publisher","unstructured":"Gal\u00e1rraga, L., Razniewski, S., Amarilli, A., Suchanek, F.M.: Predicting completeness in knowledge bases. In: Proceedings of the ACM International Conference on Web Search and Data Mining (WSDM), pp. 375\u2013383 (2017). https:\/\/doi.org\/10.1145\/3018661.3018739","DOI":"10.1145\/3018661.3018739"},{"key":"2_CR8","doi-asserted-by":"publisher","unstructured":"Gal\u00e1rraga, L.A., Teflioudi, C., Hose, K., Suchanek, F.: Amie: association rule mining under incomplete evidence in ontological knowledge bases. In: Proceedings of the International Conference on World Wide Web (WWW), pp. 413\u2013422. Association for Computing Machinery, New York (2013). https:\/\/doi.org\/10.1145\/2488388.2488425","DOI":"10.1145\/2488388.2488425"},{"key":"2_CR9","doi-asserted-by":"publisher","unstructured":"Jiang, X., Wang, Q., Wang, B.: Adaptive convolution for multi-relational learning. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol. 1 (Long and Short Papers), pp. 978\u2013987. Association for Computational Linguistics, Minneapolis (2019). https:\/\/doi.org\/10.18653\/v1\/N19-1103. https:\/\/aclanthology.org\/N19-1103","DOI":"10.18653\/v1\/N19-1103"},{"key":"2_CR10","unstructured":"Kazemi, S.M., Poole, D.: Simple embedding for link prediction in knowledge graphs. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol.\u00a031. Curran Associates, Inc. (2018)"},{"issue":"2","key":"2_CR11","doi-asserted-by":"publisher","first-page":"167","DOI":"10.3233\/SW-140134","volume":"6","author":"J Lehmann","year":"2015","unstructured":"Lehmann, J., et al.: Dbpedia-a large-scale, multilingual knowledge base extracted from wikipedia. Semant. web 6(2), 167\u2013195 (2015)","journal-title":"Semant. web"},{"key":"2_CR12","doi-asserted-by":"crossref","unstructured":"Lin, Y., Liu, Z., Sun, M., Liu, Y., Zhu, X.: Learning entity and relation embeddings for knowledge graph completion. In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, pp. 2181\u20132187 (2015)","DOI":"10.1609\/aaai.v29i1.9491"},{"key":"2_CR13","doi-asserted-by":"publisher","unstructured":"Lo, K., Wang, L.L., Neumann, M., Kinney, R., Weld, D.: S2ORC: the semantic scholar open research corpus. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 4969\u20134983. Association for Computational Linguistics, Online (2020). https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.447. https:\/\/www.aclweb.org\/anthology\/2020.acl-main.447","DOI":"10.18653\/v1\/2020.acl-main.447"},{"key":"2_CR14","doi-asserted-by":"crossref","unstructured":"Meilicke, C., Chekol, M.W., Fink, M., Stuckenschmidt, H.: Reinforced anytime bottom up rule learning for knowledge graph completion. arXiv preprint arXiv:2004.04412 (2020)","DOI":"10.24963\/ijcai.2019\/435"},{"key":"2_CR15","doi-asserted-by":"crossref","unstructured":"Meilicke, C., Chekol, M.W., Ruffinelli, D., Stuckenschmidt, H.: Anytime bottom-up rule learning for knowledge graph completion. In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), IJCAI 2019, pp. 3137\u20133143 (2019)","DOI":"10.24963\/ijcai.2019\/435"},{"key":"2_CR16","unstructured":"Mnih, A., Teh, Y.W.: A fast and simple algorithm for training neural probabilistic language models. In: Proceedings of the 29th International Conference on Machine Learning, ICML 2012, Edinburgh, Scotland, UK, 26 June\u20131 July 2012. icml.cc\/Omnipress (2012). http:\/\/icml.cc\/2012\/papers\/855.pdf"},{"key":"2_CR17","unstructured":"Nickel, M., Tresp, V., Kriegel, H.P.: A three-way model for collective learning on multi-relational data. In: Proceedings of the International Conference on International Conference on Machine Learning (ICML), pp. 809\u2013816. Omnipress, Madison (2011)"},{"key":"2_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1007\/978-3-030-49461-2_34","volume-title":"The Semantic Web","author":"T Pellissier Tanon","year":"2020","unstructured":"Pellissier Tanon, T., Weikum, G., Suchanek, F.: YAGO 4: a reason-able knowledge base. In: Harth, A., et al. (eds.) ESWC 2020. LNCS, vol. 12123, pp. 583\u2013596. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-49461-2_34"},{"key":"2_CR19","doi-asserted-by":"crossref","unstructured":"Peng, C., Xia, F., Naseriparsa, M., Osborne, F.: Knowledge graphs: opportunities and challenges. Artif. Intell. Rev. 1\u201332 (2023)","DOI":"10.1007\/s10462-023-10465-9"},{"key":"2_CR20","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"593","DOI":"10.1007\/978-3-319-93417-4_38","volume-title":"The Semantic Web","author":"M Schlichtkrull","year":"2018","unstructured":"Schlichtkrull, M., Kipf, T.N., Bloem, P., van\u00a0den Berg, R., Titov, I., Welling, M.: Modeling relational data with graph convolutional networks. In: Gangemi, A., et al. (eds.) ESWC 2018. LNCS, vol. 10843, pp. 593\u2013607. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-93417-4_38"},{"key":"2_CR21","doi-asserted-by":"publisher","unstructured":"Shang, C., Tang, Y., Huang, J., Bi, J., He, X., Zhou, B.: End-to-end structure-aware convolutional networks for knowledge base completion. In: Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence and Thirty-First Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational Advances in Artificial Intelligence. AAAI 2019\/IAAI 2019\/EAAI 2019, AAAI Press (2019). https:\/\/doi.org\/10.1609\/aaai.v33i01.33013060","DOI":"10.1609\/aaai.v33i01.33013060"},{"key":"2_CR22","doi-asserted-by":"crossref","unstructured":"Sosa, D.N., Derry, A., Guo, M., Wei, E., Brinton, C., Altman, R.B.: A literature-based knowledge graph embedding method for identifying drug repurposing opportunities in rare diseases. In: Pacific Symposium on Biocomputing 2020, pp. 463\u2013474. World Scientific (2019)","DOI":"10.1142\/9789811215636_0041"},{"key":"2_CR23","unstructured":"Sun, Z., Deng, Z.H., Nie, J.Y., Tang, J.: Rotate: Knowledge graph embedding by relational rotation in complex space. In: International Conference on Learning Representations (2019). https:\/\/openreview.net\/forum?id=HkgEQnRqYQ"},{"key":"2_CR24","doi-asserted-by":"publisher","unstructured":"Sun, Z., et al.: Research commentary on recommendations with side information: a survey and research directions. Electron. Commer. Res. Appl. 37, 100879 (2019). https:\/\/doi.org\/10.1016\/j.elerap.2019.100879. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1567422319300560","DOI":"10.1016\/j.elerap.2019.100879"},{"key":"2_CR25","unstructured":"Trouillon, T., Welbl, J., Riedel, S., Gaussier, \u00c9., Bouchard, G.: Complex embeddings for simple link prediction. In: Balcan, M., Weinberger, K.Q. (eds.) Proceedings of the International Conference on Machine Learning (ICML), vol.\u00a048, pp. 2071\u20132080. JMLR.org (2016). http:\/\/proceedings.mlr.press\/v48\/trouillon16.html"},{"key":"2_CR26","doi-asserted-by":"publisher","unstructured":"Wang, M., Qiu, L., Wang, X.: A survey on knowledge graph embeddings for link prediction. Symmetry 13(3) (2021). https:\/\/doi.org\/10.3390\/sym13030485","DOI":"10.3390\/sym13030485"},{"key":"2_CR27","doi-asserted-by":"publisher","unstructured":"Wang, Z., Zhang, J., Feng, J., Chen, Z.: Knowledge graph embedding by translating on hyperplanes. In: Proceedings of the AAAI Conference on Artificial Intelligence 28(1) (2014). https:\/\/doi.org\/10.1609\/aaai.v28i1.8870. https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/8870","DOI":"10.1609\/aaai.v28i1.8870"},{"key":"2_CR28","doi-asserted-by":"crossref","unstructured":"Wishart, D.S., et al.: Drugbank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res. 34(suppl_1), D668\u2013D672 (2006)","DOI":"10.1093\/nar\/gkj067"},{"key":"2_CR29","doi-asserted-by":"publisher","unstructured":"Yang, Z., Ding, M., Zhou, C., Yang, H., Zhou, J., Tang, J.: Understanding negative sampling in graph representation learning. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2020, pp. 1666\u20131676 (2020). https:\/\/doi.org\/10.1145\/3394486.3403218","DOI":"10.1145\/3394486.3403218"},{"issue":"8","key":"2_CR30","doi-asserted-by":"publisher","first-page":"396","DOI":"10.3390\/info13080396","volume":"13","author":"M Zamini","year":"2022","unstructured":"Zamini, M., Reza, H., Rabiei, M.: A review of knowledge graph completion. Information 13(8), 396 (2022)","journal-title":"Information"},{"key":"2_CR31","doi-asserted-by":"publisher","unstructured":"Zhou, S., et al.: Interactive recommender system via knowledge graph-enhanced reinforcement learning. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2020, pp. 179\u2013188. Association for Computing Machinery, New York (2020). https:\/\/doi.org\/10.1145\/3397271.3401174","DOI":"10.1145\/3397271.3401174"}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-70378-2_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T09:02:27Z","timestamp":1725181347000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-70378-2_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031703775","9783031703782"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-70378-2_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"22 August 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vilnius","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lithuania","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 September 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2024.ecmlpkdd.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}