{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T12:05:01Z","timestamp":1780401901106,"version":"3.54.1"},"publisher-location":"Cham","reference-count":55,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031945748","type":"print"},{"value":"9783031945755","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-94575-5_22","type":"book-chapter","created":{"date-parts":[[2025,5,31]],"date-time":"2025-05-31T02:15:22Z","timestamp":1748657722000},"page":"403-422","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Taxonomy Inference for\u00a0Tabular Data Using Large Language Models"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0981-5567","authenticated-orcid":false,"given":"Zhenyu","family":"Wu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4643-6750","authenticated-orcid":false,"given":"Jiaoyan","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2008-6617","authenticated-orcid":false,"given":"Norman W.","family":"Paton","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,6,1]]},"reference":[{"key":"22_CR1","unstructured":"Atil, B., Chittams, A., Fu, L., Ture, F., Xu, L., Baldwin, B.: LLM stability: a detailed analysis with some surprises. CoRR, abs\/2408.04667, 2024"},{"key":"22_CR2","unstructured":"Baazizi, M.A., Lahmar, H.B., Colazzo, D., Ghelli, G., Sartiani, C.: Schema inference for massive JSON datasets. In: Proceedings of the 20th International Conference on Extending Database Technology, EDBT 2017, Venice, Italy, 21\u201324 March 2017, pp. 222\u2013233. OpenProceedings.org, 2017"},{"key":"22_CR3","unstructured":"Barret, N., Manolescu, I., Upadhyay, P.: Computing generic abstractions from application datasets. In: Tanca, L., et\u00a0al., (eds.), Proceedings 27th International Conference on Extending Database Technology, EDBT 2024, pp. 94\u2013107. OpenProceedings.org, 2024"},{"key":"22_CR4","doi-asserted-by":"crossref","unstructured":"Brickley, D., Burgess, M., Noy, N.: Google dataset search: building a search engine for datasets in an open web ecosystem. In: The World Wide Web Conference, WWW \u201919, pp. 1365\u20131375, New York, NY, USA. Association for Computing Machinery, 2019","DOI":"10.1145\/3308558.3313685"},{"key":"22_CR5","unstructured":"Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. In: Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, 6\u201312 December 2020, virtual, 2020"},{"key":"22_CR6","doi-asserted-by":"crossref","unstructured":"Chen, B., Yi, F., Varro, D.: Prompting or fine-tuning? A comparative study of large language models for taxonomy construction. In: 2023 ACM\/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C), pp. 588\u2013596, Los Alamitos, CA, USA. IEEE Computer Society, October 2023","DOI":"10.1109\/MODELS-C59198.2023.00097"},{"issue":"5","key":"22_CR7","doi-asserted-by":"publisher","first-page":"2569","DOI":"10.1007\/s11280-023-01169-9","volume":"26","author":"J Chen","year":"2023","unstructured":"Chen, J., He, Y., Geng, Y., Jim\u00e9nez-Ruiz, E., Dong, H., Horrocks, I.: Contextual semantic embeddings for ontology subsumption prediction. World Wide Web (WWW) 26(5), 2569\u20132591 (2023)","journal-title":"World Wide Web (WWW)"},{"key":"22_CR8","unstructured":"Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13\u201318 July 2020, Virtual Event, pp. 1597\u20131607, 2020"},{"key":"22_CR9","doi-asserted-by":"crossref","unstructured":"Christodoulou, K., Paton, N.W., Fernandes, A.A.: Structure inference for linked data sources using clustering. Trans. Large Scale Data Knowl. Centered Syst. 19, 1\u201325 (2015)","DOI":"10.1007\/978-3-662-46562-2_1"},{"key":"22_CR10","unstructured":"DeepSeek-AI. Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning, 2025"},{"issue":"3","key":"22_CR11","doi-asserted-by":"publisher","first-page":"307","DOI":"10.14778\/3430915.3430921","volume":"14","author":"X Deng","year":"2020","unstructured":"Deng, X., Sun, H., Lees, A., You, W., Cong, Yu.: TURL: table understanding through representation learning. Proc. VLDB Endow. 14(3), 307\u2013319 (2020)","journal-title":"Proc. VLDB Endow."},{"key":"22_CR12","doi-asserted-by":"crossref","unstructured":"Dong, Y., Takeoka, K., Xiao, C., Oyamada, M.: Efficient joinable table discovery in data lakes: a high-dimensional similarity-based approach. In: 37th IEEE International Conference on Data Engineering, ICDE 2021, Chania, Greece, 19\u201322 April 2021, pp. 456\u2013467, 2021","DOI":"10.1109\/ICDE51399.2021.00046"},{"issue":"10","key":"22_CR13","doi-asserted-by":"publisher","first-page":"2458","DOI":"10.14778\/3603581.3603587","volume":"16","author":"Y Dong","year":"2023","unstructured":"Dong, Y., Xiao, C., Nozawa, T., Enomoto, M., Oyamada, M.: Deepjoin: joinable table discovery with pre-trained language models. Proc. VLDB Endow. 16(10), 2458\u20132470 (2023)","journal-title":"Proc. VLDB Endow."},{"key":"22_CR14","unstructured":"Edge, D., et al.: From local to global: a graph RAG approach to query-focused summarization. CoRR, abs\/2404.16130, 2024"},{"issue":"7","key":"22_CR15","doi-asserted-by":"publisher","first-page":"1726","DOI":"10.14778\/3587136.3587146","volume":"16","author":"G Fan","year":"2023","unstructured":"Fan, G., Wang, J., Li, Y., Zhang, D., Miller, R.J.: Semantics-aware dataset discovery from data lakes with contextualized column-based representation learning. Proc. VLDB Endow. 16(7), 1726\u20131739 (2023)","journal-title":"Proc. VLDB Endow."},{"issue":"1","key":"22_CR16","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1145\/3665252.3665263","volume":"53","author":"J Fan","year":"2024","unstructured":"Fan, J.: Unicorn: a unified multi-tasking matching model. SIGMOD Rec. 53(1), 44\u201353 (2024)","journal-title":"SIGMOD Rec."},{"issue":"3","key":"22_CR17","doi-asserted-by":"publisher","first-page":"793","DOI":"10.3233\/SW-233347","volume":"15","author":"J Flores","year":"2024","unstructured":"Flores, J., et al.: Incremental schema integration for data wrangling via knowledge graphs. Semantic Web 15(3), 793\u2013830 (2024)","journal-title":"Semantic Web"},{"key":"22_CR18","unstructured":"Funk, M., Hosemann, S., Jung, J.C., Lutz, C.: Towards ontology construction with language models. In: Joint proceedings of the 1st workshop on Knowledge Base Construction from Pre-Trained Language Models (KBC-LM) and the 2nd challenge on Language Models for Knowledge Base Construction (LM-KBC) co-located with the 22nd International Semantic Web Conference (ISWC 2023), Athens, Greece, 6 November 2023, volume 3577 of CEUR Workshop Proceedings. CEUR-WS.org, 2023"},{"key":"22_CR19","unstructured":"Australian Government. data.gov.au, n.d. Accessed 21 Aug 2025"},{"key":"22_CR20","unstructured":"UK\u00a0Government. data.gov.uk, n.d. Accessed 21 Aug 2025"},{"key":"22_CR21","unstructured":"U.S. Government. data.gov, n.d. Accessed 21 Aug 2025"},{"issue":"8","key":"22_CR22","doi-asserted-by":"publisher","first-page":"2104","DOI":"10.14778\/3659437.3659461","volume":"17","author":"M Kayali","year":"2024","unstructured":"Kayali, M., Lykov, A., Fountalis, I., Vasiloglou, N., Olteanu, D., Suciu, D.: Chorus: foundation models for unified data discovery and exploration. Proc. VLDB Endow. 17(8), 2104\u20132114 (2024)","journal-title":"Proc. VLDB Endow."},{"issue":"4","key":"22_CR23","doi-asserted-by":"publisher","first-page":"675","DOI":"10.1007\/s00778-021-00717-x","volume":"31","author":"K Kellou-Menouer","year":"2022","unstructured":"Kellou-Menouer, K., Kardoulakis, N., Troullinou, G., Kedad, Z., Plexousakis, D., Kondylakis, H.: A survey on semantic schema discovery. VLDB J. 31(4), 675\u2013710 (2022)","journal-title":"VLDB J."},{"key":"22_CR24","doi-asserted-by":"publisher","unstructured":"Kellou-Menouer, K., Kedad, Z.: Schema discovery in RDF data sources. In: Johannesson, P., Lee, M.L., Liddle, S.W., Opdahl, A.L., L\u00f3pez, \u00d3.P. (eds.) ER 2015. LNCS, vol. 9381, pp. 481\u2013495. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-25264-3_36","DOI":"10.1007\/978-3-319-25264-3_36"},{"issue":"4","key":"22_CR25","doi-asserted-by":"publisher","first-page":"932","DOI":"10.14778\/3574245.3574274","volume":"16","author":"A Khatiwada","year":"2022","unstructured":"Khatiwada, A., Shraga, R., Gatterbauer, W., Miller, R.J.: Integrating data lake tables. Proc. VLDB Endow. 16(4), 932\u2013945 (2022)","journal-title":"Proc. VLDB Endow."},{"key":"22_CR26","unstructured":"Langlais, P., Gao, T.L.: Rate: a reproducible automatic taxonomy evaluation by filling the gap. In: Proceedings of the 15th International Conference on Computational Semantics, pp. 173\u2013182, 2023"},{"issue":"1","key":"22_CR27","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1145\/3665252.3665269","volume":"53","author":"P Li","year":"2024","unstructured":"Li, P., He, Y., Yan, C., Wang, Y., Chaudhuri, S.: Auto-tables: relationalize tables without using examples. SIGMOD Rec. 53(1), 76\u201385 (2024)","journal-title":"SIGMOD Rec."},{"key":"22_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2020.103607","volume":"112","author":"H Liu","year":"2020","unstructured":"Liu, H., Perl, Y., Geller, J.: Concept placement using BERT trained by transforming and summarizing biomedical ontology structure. J. Biomed. Inform. 112, 103607 (2020)","journal-title":"J. Biomed. Inform."},{"key":"22_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.websem.2022.100761","volume":"76","author":"J Liu","year":"2023","unstructured":"Liu, J., Chabot, Y., Troncy, R., Huynh, V.-P., Labb\u00e9, T., Monnin, P.: From tabular data to knowledge graphs: a survey of semantic table interpretation tasks and methods. J. Web Semant. 76, 100761 (2023)","journal-title":"J. Web Semant."},{"key":"22_CR30","doi-asserted-by":"crossref","unstructured":"Liu, Q., Lin, H., Xiao, X., Han, X., Sun, L., Wu, H.: Fine-grained entity typing via label reasoning. In: Moens, M.F., Huang, X.J., Specia, L., Yih, W.T. (eds.), Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 4611\u20134622, Online and Punta Cana, Dominican Republic, November 2021. Association for Computational Linguistics, November 2021","DOI":"10.18653\/v1\/2021.emnlp-main.378"},{"key":"22_CR31","unstructured":"Liu, Y., et al.: Roberta: a robustly optimized BERT pretraining approach. CoRR, abs\/1907.11692, 2019"},{"key":"22_CR32","unstructured":"Lo, A., Jiang, A.Q., Li, W., Jamnik, M.: End-to-end ontology learning with large language models. In: Globersons, A. et al. (eds.), Advances in Neural Information Processing Systems, vol. 38: Annual Conference on Neural Information Processing Systems 2024, NeurIPS 2024, Vancouver, BC, Canada, 10\u201315 December 2024"},{"key":"22_CR33","unstructured":"Ma, Y., Cambria, E., Gao, S.: Label embedding for zero-shot fine-grained named entity typing. In: Matsumoto, Y., Prasad, R. (eds.), Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pp. 171\u2013180, Osaka, Japan. The COLING 2016 Organizing Committee, December 2016"},{"key":"22_CR34","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1007\/978-3-319-11964-9_18","volume-title":"The Semantic Web \u2013 ISWC 2014","author":"R Meusel","year":"2014","unstructured":"Meusel, R., Petrovski, P., Bizer, C.: The WebDataCommons microdata, RDFa and microformat dataset series. In: Mika, P., et al. (eds.) ISWC 2014. LNCS, vol. 8796, pp. 277\u2013292. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-11964-9_18"},{"key":"22_CR35","doi-asserted-by":"crossref","unstructured":"Min, D., et al.: Exploring the impact of table-to-text methods on augmenting llm-based question answering with domain hybrid data. In: Yang, Y., Davani, A., Sil, A., Kumar, A. (eds.), Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Track, NAACL 2024, Mexico City, Mexico, 16\u201321 June 2024, pp. 464\u2013482. Association for Computational Linguistics, 2024","DOI":"10.18653\/v1\/2024.naacl-industry.41"},{"issue":"7","key":"22_CR36","doi-asserted-by":"publisher","first-page":"813","DOI":"10.14778\/3192965.3192973","volume":"11","author":"F Nargesian","year":"2018","unstructured":"Nargesian, F., Zhu, E., Pu, K.Q., Miller, R.J.: Table union search on open data. Proc. VLDB Endow. 11(7), 813\u2013825 (2018)","journal-title":"Proc. VLDB Endow."},{"key":"22_CR37","doi-asserted-by":"crossref","unstructured":"Obeidat, R., Fern, X., Shahbazi, H., Tadepalli, P.: Description-based zero-shot fine-grained entity typing. In: Burstein, J., Doran, C., Solorio, T. (eds.), Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 807\u2013814, Minneapolis, Minnesota, June 2019. Association for Computational Linguistics","DOI":"10.18653\/v1\/N19-1087"},{"key":"22_CR38","doi-asserted-by":"crossref","unstructured":"Paton, N.W., Chen, J., Wu, Z.: Dataset discovery and exploration: a survey. ACM Comput. Surv. 56(4), 102:1\u2013102:37 (2024)","DOI":"10.1145\/3626521"},{"key":"22_CR39","doi-asserted-by":"crossref","unstructured":"Reimers, N., Gurevych, I.: Sentence-bert: Sentence embeddings using siamese bert-networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, EMNLP-IJCNLP 2019, Hong Kong, China, 3\u20137 November 2019, pp. 3980\u20133990. Association for Computational Linguistics, 2019","DOI":"10.18653\/v1\/D19-1410"},{"key":"22_CR40","unstructured":"Ritze, D., Bizer, C.: Matching web tables to dbpedia - a feature utility study. In: International Conference on Extending Database Technology, 2017"},{"key":"22_CR41","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/0377-0427(87)90125-7","volume":"20","author":"PJ Rousseeuw","year":"1987","unstructured":"Rousseeuw, P.J.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53\u201365 (1987)","journal-title":"J. Comput. Appl. Math."},{"key":"22_CR42","doi-asserted-by":"crossref","unstructured":"Shi, J., et al.: Subsumption prediction for e-commerce taxonomies. In: The Semantic Web - 20th International Conference, ESWC 2023, Hersonissos, Crete, Greece, May 28 - 1 June 2023, Proceedings, vol.13870 of LNCS, pp. 244\u2013261. Springer, 2023","DOI":"10.1007\/978-3-031-33455-9_15"},{"key":"22_CR43","unstructured":"Stewart, I., Horawalavithana, S., Kennedy, B., Munikoti, S., Pazdernik, K.: Surprisingly fragile: Assessing and addressing prompt instability in multimodal foundation models, 2024. CoRR, abs\/2408.14595"},{"key":"22_CR44","unstructured":"Touvron, H., et al.: Llama: Open and efficient foundation language models. CoRR, abs\/2302.13971, 2023"},{"issue":"9","key":"22_CR45","doi-asserted-by":"publisher","first-page":"528","DOI":"10.14778\/2002938.2002939","volume":"4","author":"P Venetis","year":"2011","unstructured":"Venetis, P., et al.: Recovering semantics of tables on the web. Proc. VLDB Endow. 4(9), 528\u2013538 (2011)","journal-title":"Proc. VLDB Endow."},{"key":"22_CR46","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1007\/978-3-642-21034-1_9","volume-title":"The Semantic Web: Research and Applications","author":"J V\u00f6lker","year":"2011","unstructured":"V\u00f6lker, J., Niepert, M.: Statistical schema induction. In: Antoniou, G., et al. (eds.) ESWC 2011. LNCS, vol. 6643, pp. 124\u2013138. Springer, Heidelberg (2011). https:\/\/doi.org\/10.1007\/978-3-642-21034-1_9"},{"key":"22_CR47","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1007\/978-3-642-34002-4_11","volume-title":"Conceptual Modeling","author":"J Wang","year":"2012","unstructured":"Wang, J., Wang, H., Wang, Z., Zhu, K.Q.: Understanding tables on the web. In: Atzeni, P., Cheung, D., Ram, S. (eds.) ER 2012. LNCS, vol. 7532, pp. 141\u2013155. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-34002-4_11"},{"key":"22_CR48","unstructured":"Yang, A., et al.: Qwen2 technical report. CoRR, abs\/2407.10671, 2024"},{"issue":"1","key":"22_CR49","doi-asserted-by":"publisher","first-page":"634","DOI":"10.14778\/1687627.1687699","volume":"2","author":"X Yang","year":"2009","unstructured":"Yang, X., Procopiuc, C.M., Srivastava, D.: Summarizing relational databases. Proc. VLDB Endow. 2(1), 634\u2013645 (2009)","journal-title":"Proc. VLDB Endow."},{"key":"22_CR50","unstructured":"Yosef, M.A., Bauer, S., Hoffart, J., Spaniol, M., Weikum, G.: Hyena: hierarchical type classification for entity names. In: International Conference on Computational Linguistics, 2012"},{"key":"22_CR51","unstructured":"Yu, C., Jagadish, H.V.: Schema summarization. In: Proceedings of the 32nd International Conference on Very Large Data Bases, Seoul, Korea, 12\u201315 September 2006, pp. 319\u2013330, 2006"},{"key":"22_CR52","doi-asserted-by":"crossref","unstructured":"Yuan, Z., Downey, D.: Otyper: a neural architecture for open named entity typing. AAAI\u201918\/IAAI\u201918\/EAAI\u201918. AAAI Press, 2018","DOI":"10.1609\/aaai.v32i1.12070"},{"key":"22_CR53","doi-asserted-by":"crossref","unstructured":"Zeng, Q., et al.: Chain-of-layer: iteratively prompting large language models for taxonomy induction from limited examples. In: Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, CIKM 2024, Boise, ID, USA, 21\u201325 October 2024, pp. 3093\u20133102. ACM, 2024","DOI":"10.1145\/3627673.3679608"},{"key":"22_CR54","doi-asserted-by":"crossref","unstructured":"Zhang, T., Xia, C., Lu, C.T., Yu, P.: MZET: memory augmented zero-shot fine-grained named entity typing. In: Scott, D., Bel, N., Zong, C. (eds.), Proceedings of the 28th International Conference on Computational Linguistics, pp. 77\u201387, Barcelona, Spain (Online). International Committee on Computational Linguistics, December 2020","DOI":"10.18653\/v1\/2020.coling-main.7"},{"issue":"6","key":"22_CR55","doi-asserted-by":"publisher","first-page":"921","DOI":"10.3233\/SW-160242","volume":"8","author":"Z Zhang","year":"2017","unstructured":"Zhang, Z.: Effective and efficient semantic table interpretation using tableminer$$ ^{\\text{+ }}$$. Semantic Web 8(6), 921\u2013957 (2017)","journal-title":"Semantic Web"}],"container-title":["Lecture Notes in Computer Science","The Semantic Web"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-94575-5_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,31]],"date-time":"2025-05-31T02:15:44Z","timestamp":1748657744000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-94575-5_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031945748","9783031945755"],"references-count":55,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-94575-5_22","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"1 June 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ESWC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Semantic Web Conference","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portoroz","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Slovenia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 June 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 June 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"esws2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2025.eswc-conferences.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}