{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T14:03:25Z","timestamp":1774879405382,"version":"3.50.1"},"publisher-location":"Singapore","reference-count":52,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819584130","type":"print"},{"value":"9789819584147","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[[2026]]},"DOI":"10.1007\/978-981-95-8414-7_26","type":"book-chapter","created":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T13:15:27Z","timestamp":1774876527000},"page":"467-485","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["LinkUIE: Entity Linking Adapter for\u00a0Universal Information Extraction"],"prefix":"10.1007","author":[{"given":"Wenqi","family":"Xiong","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuancheng","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weizhi","family":"Meng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bin","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dianxin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,3,31]]},"reference":[{"key":"26_CR1","doi-asserted-by":"publisher","unstructured":"Ayoola, T., Tyagi, S., Fisher, J., Christodoulopoulos, C., Pierleoni, A.: ReFinED: an efficient zero-shot-capable approach to end-to-end entity linking. In: Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics, pp. 209\u2013220. Association for Computational Linguistics, Seattle, Washington (2022). https:\/\/doi.org\/10.18653\/v1\/2022.naacl-industry.24","DOI":"10.18653\/v1\/2022.naacl-industry.24"},{"key":"26_CR2","doi-asserted-by":"publisher","unstructured":"Barba, E., Procopio, L., Navigli, R.: ExtEnD: Extractive entity disambiguation. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 2478\u20132488. Association for Computational Linguistics, Dublin, Ireland (2022). https:\/\/doi.org\/10.18653\/v1\/2022.acl-long.177","DOI":"10.18653\/v1\/2022.acl-long.177"},{"key":"26_CR3","first-page":"1877","volume":"33","author":"T Brown","year":"2020","unstructured":"Brown, T.: Language models are few-shot learners. Adv. Neural. Inf. Process. Syst. 33, 1877\u20131901 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"1","key":"26_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3596512","volume":"42","author":"S Bruch","year":"2023","unstructured":"Bruch, S., Gai, S., Ingber, A.: An analysis of fusion functions for hybrid retrieval. ACM Trans. Inf. Syst. 42(1), 1\u201335 (2023)","journal-title":"ACM Trans. Inf. Syst."},{"key":"26_CR5","doi-asserted-by":"publisher","unstructured":"Cui, L., Wu, Y., Liu, J., Yang, S., Zhang, Y.: Template-based named entity recognition using bart. In: Meeting of the Association for Computational Linguistics, pp. 1835\u20131845. Association for Computational Linguistics, Online (2021). https:\/\/doi.org\/10.18653\/v1\/2021.findings-acl.161","DOI":"10.18653\/v1\/2021.findings-acl.161"},{"key":"26_CR6","doi-asserted-by":"publisher","unstructured":"Dalton, J., Dietz, L., Allan, J.: Entity query feature expansion using knowledge base links. In: Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval, pp. 365\u2013374. Association for Computing Machinery, New York, NY, USA (2014). https:\/\/doi.org\/10.1145\/2600428.2609628","DOI":"10.1145\/2600428.2609628"},{"key":"26_CR7","unstructured":"De\u00a0Cao, N., Izacard, G., Riedel, S., Petroni, F.: Autoregressive entity retrieval. In: 9th International Conference on Learning Representations. OpenReview.net, Austria (2021). https:\/\/openreview.net\/forum?id=5k8F6UU39V"},{"key":"26_CR8","volume":"19","author":"K Detroja","year":"2023","unstructured":"Detroja, K., Bhensdadia, C., Bhatt, B.S.: A survey on relation extraction. Intell. Syst. Appl. 19, 200244 (2023)","journal-title":"Intell. Syst. Appl."},{"key":"26_CR9","doi-asserted-by":"crossref","unstructured":"Ganea, O.E., Hofmann, T.: Deep joint entity disambiguation with local neural attention. In: Conference on Empirical Methods in Natural Language Processing, pp. 2619\u20132629. Assoc. Comput. Linguistics, Copenhagen, Denmark (2017). https:\/\/aclanthology.org\/D17-1277\/","DOI":"10.18653\/v1\/D17-1277"},{"key":"26_CR10","doi-asserted-by":"crossref","unstructured":"Gupta, N., Singh, S., Roth, D.: Entity linking via joint encoding of types, descriptions, and context. In: Proceedings of the 2017 conference on empirical methods in natural language processing, pp. 2681\u20132690. Association for Computational Linguistics, Copenhagen, Denmark (2017). https:\/\/aclanthology.org\/D17-1284\/","DOI":"10.18653\/v1\/D17-1284"},{"key":"26_CR11","unstructured":"Han, R., et al.: An empirical study on information extraction using large language models. arXiv preprint arXiv:2305.14450 (2023)"},{"key":"26_CR12","unstructured":"Hoffart, J., et al.: Robust disambiguation of named entities in text. In: Proceedings of the 2011 conference on empirical methods in natural language processing, pp. 782\u2013792. Association for Computational Linguistics, Edinburgh, Scotland, UK. (2011)"},{"issue":"2","key":"26_CR13","doi-asserted-by":"publisher","first-page":"40","DOI":"10.3390\/technologies11020040","volume":"11","author":"M Iman","year":"2023","unstructured":"Iman, M., Arabnia, H.R., Rasheed, K.: A review of deep transfer learning and recent advancements. Technologies 11(2), 40 (2023)","journal-title":"Technologies"},{"key":"26_CR14","unstructured":"Kandpal, N., Deng, H., Roberts, A., Wallace, E., Raffel, C.: Large language models struggle to learn long-tail knowledge. In: International Conference on Machine Learning, pp. 15696\u201315707. PMLR (2023)"},{"key":"26_CR15","doi-asserted-by":"crossref","unstructured":"Karpukhin, V., et al.: Dense passage retrieval for open-domain question answering. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, pp. 6769\u20136781 (2020)","DOI":"10.18653\/v1\/2020.emnlp-main.550"},{"key":"26_CR16","doi-asserted-by":"publisher","unstructured":"Kolitsas, N., Ganea, O.E., Hofmann, T.: End-to-end neural entity linking. In: Proceedings of the 22nd Conference on Computational Natural Language Learning, pp. 519\u2013529. Association for Computational Linguistics, Brussels, Belgium (2018). https:\/\/doi.org\/10.18653\/v1\/K18-1050","DOI":"10.18653\/v1\/K18-1050"},{"key":"26_CR17","doi-asserted-by":"publisher","unstructured":"Le, P., Titov, I.: Improving entity linking by modeling latent relations between mentions. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 1595\u20131604. Association for Computational Linguistics, Melbourne, Australia (2018). https:\/\/doi.org\/10.18653\/v1\/P18-1148","DOI":"10.18653\/v1\/P18-1148"},{"issue":"1","key":"26_CR18","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1109\/TKDE.2020.2981314","volume":"34","author":"J Li","year":"2020","unstructured":"Li, J., Sun, A., Han, J., Li, C.: A survey on deep learning for named entity recognition. IEEE Trans. Knowl. Data Eng. 34(1), 50\u201370 (2020)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"26_CR19","doi-asserted-by":"crossref","unstructured":"Li, X.V., Sanna\u00a0Passino, F.: Findkg: Dynamic knowledge graphs with large language models for detecting global trends in financial markets. In: Proceedings of the 5th ACM International Conference on AI in Finance, pp. 573\u2013581 (2024)","DOI":"10.1145\/3677052.3698603"},{"key":"26_CR20","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1162\/tacl_a_00141","volume":"3","author":"X Ling","year":"2015","unstructured":"Ling, X., Singh, S., Weld, D.S.: Design challenges for entity linking. Trans. Assoc. Comput. Linguistics 3, 315\u2013328 (2015)","journal-title":"Trans. Assoc. Comput. Linguistics"},{"key":"26_CR21","doi-asserted-by":"publisher","unstructured":"Liu, X., Liu, Y., Zhang, K., Wang, K., Liu, Q., Chen, E.: OneNet: a fine-tuning free framework for few-shot entity linking via large language model prompting. In: Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pp. 13634\u201313651. Association for Computational Linguistics, Miami, Florida, USA (2024). https:\/\/doi.org\/10.18653\/v1\/2024.emnlp-main.756","DOI":"10.18653\/v1\/2024.emnlp-main.756"},{"issue":"9","key":"26_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3596603","volume":"17","author":"Y Liu","year":"2023","unstructured":"Liu, Y., et al.: Techpat: technical phrase extraction for patent mining. ACM Trans. Knowl. Discov. Data 17(9), 1\u201331 (2023)","journal-title":"ACM Trans. Knowl. Discov. Data"},{"key":"26_CR23","doi-asserted-by":"crossref","unstructured":"Lou, J., et al.: Universal information extraction as unified semantic matching. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol.\u00a037, pp. 13318\u201313326 (2023)","DOI":"10.1609\/aaai.v37i11.26563"},{"key":"26_CR24","doi-asserted-by":"publisher","unstructured":"Lu, Y., et al.: Unified structure generation for universal information extraction. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, pp. 5755\u20135772. Association for Computational Linguistics, Dublin, Ireland (2022). https:\/\/doi.org\/10.18653\/v1\/2022.acl-long.395","DOI":"10.18653\/v1\/2022.acl-long.395"},{"key":"26_CR25","doi-asserted-by":"crossref","unstructured":"Luo, Y., et\u00a0al.: Oneke: a dockerized schema-guided llm agent-based knowledge extraction system. In: Companion Proceedings of the ACM on Web Conference 2025, pp. 2871\u20132874 (2025)","DOI":"10.1145\/3701716.3715189"},{"key":"26_CR26","doi-asserted-by":"crossref","unstructured":"Ma, Y., Crook, P.A., Sarikaya, R., Fosler-Lussier, E.: Knowledge graph inference for spoken dialog systems. In: 2015 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 5346\u20135350. IEEE (2015)","DOI":"10.1109\/ICASSP.2015.7178992"},{"key":"26_CR27","unstructured":"Meyer, L.P., et al.: Developing a scalable benchmark for assessing large language models in knowledge graph engineering. In: 19th International Conference on Semantic Systems. Leipzig, Germany (2023)"},{"key":"26_CR28","doi-asserted-by":"publisher","unstructured":"Orlando, R., Huguet\u00a0Cabot, P.L., Barba, E., Navigli, R.: ReLiK: retrieve and LinK, fast and accurate entity linking and relation extraction on an academic budget. In: Findings of the Association for Computational Linguistics: ACL 2024, pp. 14114\u201314132. Association for Computational Linguistics, Bangkok, Thailand (2024). https:\/\/doi.org\/10.18653\/v1\/2024.findings-acl.839","DOI":"10.18653\/v1\/2024.findings-acl.839"},{"issue":"3","key":"26_CR29","doi-asserted-by":"publisher","first-page":"1582","DOI":"10.1109\/TCYB.2022.3223918","volume":"54","author":"B Pu","year":"2022","unstructured":"Pu, B., Liu, J., Kang, Y., Chen, J., Philip, S.Y.: Mvstt: A multiview spatial-temporal transformer network for traffic-flow forecasting. IEEE Trans. Cybern. 54(3), 1582\u20131595 (2022)","journal-title":"IEEE Trans. Cybern."},{"key":"26_CR30","doi-asserted-by":"crossref","unstructured":"Robertson, S.E., Walker, S., Hancock-Beaulieu, M., Gull, A., Lau, M.: Okapi at trec. In: Text retrieval conference, pp. 21\u201330 (1992)","DOI":"10.6028\/NIST.SP.500-215.adhoc-city"},{"key":"26_CR31","doi-asserted-by":"publisher","unstructured":"Tedeschi, S., Conia, S., Cecconi, F., Navigli, R.: Named entity recognition for entity linking: What works and what\u2019s next. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 2584\u20132596. Association for Computational Linguistics, Punta Cana, Dominican Republic (2021). https:\/\/doi.org\/10.18653\/v1\/2021.findings-emnlp.220","DOI":"10.18653\/v1\/2021.findings-emnlp.220"},{"key":"26_CR32","doi-asserted-by":"publisher","unstructured":"Van\u00a0Hulst, J.M., Hasibi, F., Dercksen, K., Balog, K., De\u00a0Vries, A.P.: Rel: An entity linker standing on the shoulders of giants. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 2197\u20132200. Association for Computing Machinery, New York, NY, USA (2020). https:\/\/doi.org\/10.1145\/3397271.3401416","DOI":"10.1145\/3397271.3401416"},{"key":"26_CR33","doi-asserted-by":"crossref","unstructured":"Wadhwa, S., Amir, S., Wallace, B.C.: Revisiting relation extraction in the era of large language models. In: Proceedings of the conference. Association for Computational Linguistics. Meeting. vol.\u00a02023, p. 15566 (2023)","DOI":"10.18653\/v1\/2023.acl-long.868"},{"key":"26_CR34","doi-asserted-by":"publisher","unstructured":"Wang, K., et al.: Class-dynamic and hierarchy-constrained network for entity linking. In: International Conference on Database Systems for Advanced Applications. vol. 13944, pp. 622\u2013638. Springer, Tianjin, China (2023). https:\/\/doi.org\/10.1007\/978-3-031-30672-3_42","DOI":"10.1007\/978-3-031-30672-3_42"},{"key":"26_CR35","doi-asserted-by":"publisher","unstructured":"Wang, S., et al.: Benchmarking diverse-modal entity linking with generative models. In: The 61st Annual Meeting of the Association for Computational Linguistics, pp. 7841\u20137857. Association for Computational Linguistics, Toronto, Canada (2023). https:\/\/doi.org\/10.18653\/v1\/2023.findings-acl.497","DOI":"10.18653\/v1\/2023.findings-acl.497"},{"key":"26_CR36","unstructured":"Wang, X., et\u00a0al.: Instructuie: Multi-task instruction tuning for unified information extraction. arXiv preprint arXiv:2304.08085 (2023)"},{"key":"26_CR37","doi-asserted-by":"publisher","unstructured":"Wang, Y., Yu, B., Zhang, Y., Liu, T., Zhu, H., Sun, L.: Tplinker: single-stage joint extraction of entities and relations through token pair linking. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 1572\u20131582. International Committee on Computational Linguistics, Barcelona, Spain (Online) (2020). https:\/\/doi.org\/10.18653\/v1\/2020.coling-main.138","DOI":"10.18653\/v1\/2020.coling-main.138"},{"key":"26_CR38","doi-asserted-by":"publisher","unstructured":"Wang, Y., Yu, B., Zhu, H., Liu, T., Yu, N., Sun, L.: Discontinuous named entity recognition as maximal clique discovery. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, pp. 764\u2013774. Association for Computational Linguistics, Online (2021). https:\/\/doi.org\/10.18653\/v1\/2021.acl-long.63","DOI":"10.18653\/v1\/2021.acl-long.63"},{"key":"26_CR39","doi-asserted-by":"publisher","unstructured":"Wu, L., Petroni, F., Josifoski, M., Riedel, S., Zettlemoyer, L.: Scalable zero-shot entity linking with dense entity retrieval. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 6397\u20136407. Association for Computational Linguistics, Online (2020). https:\/\/doi.org\/10.18653\/v1\/2020.emnlp-main.519","DOI":"10.18653\/v1\/2020.emnlp-main.519"},{"key":"26_CR40","unstructured":"Xiao, X., et al.: Yayi-uie: A chat-enhanced instruction tuning framework for universal information extraction. arXiv preprint arXiv:2312.15548 (2023)"},{"key":"26_CR41","doi-asserted-by":"publisher","unstructured":"Xiao, Z., Gong, M., Wu, J., Zhang, X., Shou, L., Jiang, D.: Instructed language models with retrievers are powerful entity linkers. In: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pp. 2267\u20132282. Association for Computational Linguistics, Singapore (2023). https:\/\/doi.org\/10.18653\/v1\/2023.emnlp-main.139","DOI":"10.18653\/v1\/2023.emnlp-main.139"},{"issue":"6","key":"26_CR42","doi-asserted-by":"publisher","DOI":"10.1007\/s11704-024-40555-y","volume":"18","author":"D Xu","year":"2024","unstructured":"Xu, D., et al.: Large language models for generative information extraction: a survey. Front. Comp. Sci. 18(6), 186357 (2024)","journal-title":"Front. Comp. Sci."},{"key":"26_CR43","doi-asserted-by":"publisher","unstructured":"Xu, Z., Chen, Y., Hu, B.: Improving biomedical entity linking with cross-entity interaction. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol.\u00a037, pp. 13869\u201313877. AAAI Press (2023). https:\/\/doi.org\/10.1609\/aaai.v37i11.26624","DOI":"10.1609\/aaai.v37i11.26624"},{"key":"26_CR44","doi-asserted-by":"publisher","unstructured":"Yan, H., Sun, Y., Li, X., Zhou, Y., Huang, X.J., Qiu, X.: Utc-ie: a unified token-pair classification architecture for information extraction. In: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics, pp. 4096\u20134122. Association for Computational Linguistics, Toronto, Canada (2023). https:\/\/doi.org\/10.18653\/v1\/2023.acl-long.226","DOI":"10.18653\/v1\/2023.acl-long.226"},{"key":"26_CR45","unstructured":"Yu, B., et al.: Towards generalized open information extraction. arXiv preprint arXiv:2211.15987 (2022)"},{"issue":"1","key":"26_CR46","first-page":"377","volume":"35","author":"K Zhang","year":"2021","unstructured":"Zhang, K., et al.: Eatn: an efficient adaptive transfer network for aspect-level sentiment analysis. IEEE Trans. Knowl. Data Eng. 35(1), 377\u2013389 (2021)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"26_CR47","unstructured":"Zhang, N., et al.: Knowlm technical report (2023). http:\/\/knowlm.zjukg.cn\/"},{"key":"26_CR48","doi-asserted-by":"publisher","unstructured":"Zhang, Q., et al.: A survey for efficient open domain question answering. In: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 14447\u201314465. Association for Computational Linguistics, Toronto, Canada (2023). https:\/\/doi.org\/10.18653\/v1\/2023.acl-long.808","DOI":"10.18653\/v1\/2023.acl-long.808"},{"key":"26_CR49","unstructured":"Zhang, W., Hua, W., Stratos, K.: Entqa: entity linking as question answering. In: 10th International Conference on Learning Representations, ICLR 2022. OpenReview.net, Online (2022)"},{"key":"26_CR50","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Zhao, Y., Gao, H., Hu, M.: Linkner: linking local named entity recognition models to large language models using uncertainty. In: Proceedings of the ACM Web Conference 2024, pp. 4047\u20134058 (2024)","DOI":"10.1145\/3589334.3645414"},{"issue":"4","key":"26_CR51","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3618295","volume":"56","author":"L Zhong","year":"2023","unstructured":"Zhong, L., Wu, J., Li, Q., Peng, H., Wu, X.: A comprehensive survey on automatic knowledge graph construction. ACM Comput. Surv. 56(4), 1\u201362 (2023)","journal-title":"ACM Comput. Surv."},{"issue":"5","key":"26_CR52","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1007\/s11280-024-01297-w","volume":"27","author":"Y Zhu","year":"2024","unstructured":"Zhu, Y., et al.: LLMs for knowledge graph construction and reasoning: recent capabilities and future opportunities. World Wide Web 27(5), 58 (2024)","journal-title":"World Wide Web"}],"container-title":["Lecture Notes in Computer Science","Algorithms and Architectures for Parallel Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-8414-7_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T13:15:31Z","timestamp":1774876531000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-8414-7_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819584130","9789819584147"],"references-count":52,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-8414-7_26","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"31 March 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICA3PP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Algorithms and Architectures for Parallel Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Zhengzhou","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","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":"30 October 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 November 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ica3pp2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ieee-cybermatics.org\/2025\/ica3pp\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}