{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,3]],"date-time":"2026-10-03T02:41:59Z","timestamp":1790995319228,"version":"4.1.0"},"publisher-location":"Cham","reference-count":44,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032251558","type":"print"},{"value":"9783032251565","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\/978-3-032-25156-5_11","type":"book-chapter","created":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T10:23:58Z","timestamp":1778063038000},"page":"198-216","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Link Prediction or\u00a0Perdition: The\u00a0Seeds of\u00a0Instability in\u00a0Knowledge Graph Embeddings"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-7111-3202","authenticated-orcid":false,"given":"Guillaume","family":"M\u00e9rou\u00e9","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0543-1232","authenticated-orcid":false,"given":"Fabien","family":"Gandon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2017-8426","authenticated-orcid":false,"given":"Pierre","family":"Monnin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,7]]},"reference":[{"key":"11_CR1","doi-asserted-by":"crossref","unstructured":"Berant, J., Chou, A., Frostig, R., Liang, P.: Semantic parsing on freebase from question-answer pairs. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, Seattle, Washington, USA, pp. 1533\u20131544. Association for Computational Linguistics (2013). https:\/\/aclanthology.org\/D13-1160\/","DOI":"10.18653\/v1\/D13-1160"},{"key":"11_CR2","doi-asserted-by":"publisher","unstructured":"Biswas, R., et al.: Knowledge graph embeddings: open challenges and opportunities. TGDK 1(1), 4:1\u20134:32 (2023). https:\/\/doi.org\/10.4230\/TGDK.1.1.4","DOI":"10.4230\/TGDK.1.1.4"},{"key":"11_CR3","first-page":"100036","volume":"2","author":"S Bonner","year":"2022","unstructured":"Bonner, S., et al.: Understanding the performance of knowledge graph embeddings in drug discovery. Artif. Intell. Life Sci. 2, 100036 (2022)","journal-title":"Artif. Intell. Life Sci."},{"key":"11_CR4","unstructured":"Bordes, A., Usunier, N., Garc\u00eda-Dur\u00e1n, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: 27th Annual Conference on Neural Information Processing Systems 2013. Advances in Neural Information Processing Systems, Proceedings of a meeting held 5\u20138 December 2013, Lake Tahoe, Nevada, United States, vol. 26, pp. 2787\u20132795 (2013). https:\/\/proceedings.neurips.cc\/paper\/2013\/hash\/1cecc7a77928ca8133fa24680a88d2f9-Abstract.html"},{"key":"11_CR5","doi-asserted-by":"publisher","unstructured":"Chen, J., et al.: Knowledge graphs for the life sciences: recent developments, challenges and opportunities. TGDK 1(1), 5:1\u20135:33 (2023). https:\/\/doi.org\/10.4230\/TGDK.1.1.5","DOI":"10.4230\/TGDK.1.1.5"},{"key":"11_CR6","doi-asserted-by":"publisher","unstructured":"Chen, S., Liu, X., Gao, J., Jiao, J., Zhang, R., Ji, Y.: HittER: hierarchical transformers for knowledge graph embeddings. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, Online and Punta Cana, Dominican Republic, pp. 10395\u201310407. Association for Computational Linguistics, (2021). https:\/\/doi.org\/10.18653\/v1\/2021.emnlp-main.812","DOI":"10.18653\/v1\/2021.emnlp-main.812"},{"key":"11_CR7","unstructured":"Das, R., et al.: Go for a walk and arrive at the answer: reasoning over paths in knowledge bases using reinforcement learning (2018). https:\/\/arxiv.org\/abs\/1711.05851"},{"key":"11_CR8","doi-asserted-by":"publisher","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, (AAAI-18), The 30th innovative Applications of Artificial Intelligence (IAAI-18), and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-18), New Orleans, Louisiana, USA, 2\u20137 February 2018, pp. 1811\u20131818. AAAI Press (2018). https:\/\/doi.org\/10.1609\/AAAI.V32I1.11573","DOI":"10.1609\/AAAI.V32I1.11573"},{"key":"11_CR9","unstructured":"Gad-Elrab, M.H., Ho, V.T., Levinkov, E., Tran, T., Stepanova, D.: Towards utilizing knowledge graph embedding models for conceptual clustering. In: Taylor, K.L., Gon\u00e7alves, R.S., L\u00e9cu\u00e9, F., Yan, J. (eds.) Proceedings of the ISWC 2020 Demos and Industry Tracks: From Novel Ideas to Industrial Practice co-located with 19th International Semantic Web Conference (ISWC 2020), CEUR Workshop Proceedings, Globally Online, 1\u20136 November 2020 (UTC), vol.\u00a02721, pp. 281\u2013286. CEUR-WS.org (2020). https:\/\/ceur-ws.org\/Vol-2721\/paper572.pdf"},{"key":"11_CR10","doi-asserted-by":"publisher","unstructured":"Gal\u00e1rraga, L., Teflioudi, C., Hose, K., Suchanek, F.M.: Fast rule mining in ontological knowledge bases with AMIE+. VLDB J. 24(6), 707\u2013730 (2015). https:\/\/doi.org\/10.1007\/S00778-015-0394-1","DOI":"10.1007\/S00778-015-0394-1"},{"issue":"4","key":"11_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3447772","volume":"54","author":"A Hogan","year":"2021","unstructured":"Hogan, A., et al.: Knowledge graphs. ACM Comput. Surv. 54(4), 1\u201337 (2021)","journal-title":"ACM Comput. Surv."},{"key":"11_CR12","doi-asserted-by":"publisher","unstructured":"Hubert, N., Paulheim, H., Brun, A., Monticolo, D.: Do similar entities have similar embeddings?. In: Mero\u00f1o Pe\u00f1uela, A., et al. (eds.) The Semantic Web, ESWC 2024. LNCS, vol. 14664, pp. 3\u201321. Springer, Cham (2024). https:\/\/doi.org\/10.1007\/978-3-031-60626-7_1","DOI":"10.1007\/978-3-031-60626-7_1"},{"key":"11_CR13","doi-asserted-by":"publisher","unstructured":"Jain, N., Kalo, JC., Balke, WT., Krestel, R.: Do embeddings actually capture knowledge graph semantics? In: Verborgh, R., et al. (eds.) The Semantic Web, ESWC 2021. LNCS, vol. 12731. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-77385-4_9","DOI":"10.1007\/978-3-030-77385-4_9"},{"issue":"2","key":"11_CR14","doi-asserted-by":"publisher","first-page":"494","DOI":"10.1109\/TNNLS.2021.3070843","volume":"33","author":"S Ji","year":"2022","unstructured":"Ji, S., Pan, S., Cambria, E., Marttinen, P., Yu, P.S.: A survey on knowledge graphs: representation, acquisition, and applications. IEEE Trans. Neural Netw. Learn. Syst. 33(2), 494\u2013514 (2022). https:\/\/doi.org\/10.1109\/TNNLS.2021.3070843","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"11_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"640","DOI":"10.1007\/978-3-319-25007-6_37","volume-title":"The Semantic Web - ISWC 2015","author":"D Krompa\u00df","year":"2015","unstructured":"Krompa\u00df, D., Baier, S., Tresp, V.: Type-constrained representation learning in knowledge graphs. In: Arenas, M., et al. (eds.) ISWC 2015. LNCS, vol. 9366, pp. 640\u2013655. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-25007-6_37"},{"key":"11_CR16","unstructured":"Lanthaler, M., Cyganiak, R., Wood, D.: RDF 1.1 concepts and abstract syntax. W3C recommendation, W3C, February 2014. https:\/\/www.w3.org\/TR\/2014\/REC-rdf11-concepts-20140225\/"},{"key":"11_CR17","doi-asserted-by":"publisher","unstructured":"Li, Q., Zhong, Y., Qin, Y.: MoCoKGC: momentum contrast entity encoding for knowledge graph completion. In: Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, Miami, Florida, USA, November 2024, pp. 14940\u201314952. Association for Computational Linguistics (2024). https:\/\/doi.org\/10.18653\/v1\/2024.emnlp-main.832, https:\/\/aclanthology.org\/2024.emnlp-main.832\/","DOI":"10.18653\/v1\/2024.emnlp-main.832"},{"key":"11_CR18","doi-asserted-by":"publisher","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, 25\u201330 January 2015, Austin, Texas, USA, pp. 2181\u20132187. AAAI Press (2015). https:\/\/doi.org\/10.1609\/AAAI.V29I1.9491","DOI":"10.1609\/AAAI.V29I1.9491"},{"key":"11_CR19","unstructured":"Liu, H., Wu, Y., Yang, Y.: Analogical inference for multi-relational embeddings. In: Proceedings of the 34th International Conference on Machine Learning, ICML 2017. Proceedings of Machine Learning Research, Sydney, NSW, Australia, 6\u201311 August 2017, vol.\u00a070, pp. 2168\u20132178. PMLR (2017). http:\/\/proceedings.mlr.press\/v70\/liu17d.html"},{"key":"11_CR20","unstructured":"Madhyastha, P., Jain, R.: On model stability as a function of random seed (2019). https:\/\/arxiv.org\/abs\/1909.10447"},{"key":"11_CR21","unstructured":"Marx, C.T., du\u00a0Pin\u00a0Calmon, F., Ustun, B.: Predictive multiplicity in classification (2020). https:\/\/arxiv.org\/abs\/1909.06677"},{"key":"11_CR22","doi-asserted-by":"publisher","unstructured":"Meilicke, C., Chekol, M.W., Betz, P., Fink, M., Stuckenschmidt, H.: Anytime bottom-up rule learning for large-scale knowledge graph completion. VLDB J. 33(1), 131\u2013161 (2024). https:\/\/doi.org\/10.1007\/S00778-023-00800-5","DOI":"10.1007\/S00778-023-00800-5"},{"issue":"3","key":"11_CR23","doi-asserted-by":"publisher","first-page":"379","DOI":"10.3233\/SW-210452","volume":"13","author":"P Monnin","year":"2022","unstructured":"Monnin, P., Ra\u00efssi, C., Napoli, A., Coulet, A.: Discovering alignment relations with graph convolutional networks: a biomedical case study. Semant. Web 13(3), 379\u2013398 (2022). https:\/\/doi.org\/10.3233\/SW-210452","journal-title":"Semant. Web"},{"key":"11_CR24","doi-asserted-by":"publisher","unstructured":"Nguyen, D.Q., Nguyen, T.D., Nguyen, D.Q., Phung, D.Q.: A novel embedding model for knowledge base completion based on convolutional neural network. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT, Volume 2 (Short Papers), New Orleans, Louisiana, USA, 1\u20136 June 2018, pp. 327\u2013333. Association for Computational Linguistics (2018). https:\/\/doi.org\/10.18653\/V1\/N18-2053","DOI":"10.18653\/V1\/N18-2053"},{"issue":"8","key":"11_CR25","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1145\/3331166","volume":"62","author":"NF Noy","year":"2019","unstructured":"Noy, N.F., Gao, Y., Jain, A., Narayanan, A., Patterson, A., Taylor, J.: Industry-scale knowledge graphs: lessons and challenges. Commun. ACM 62(8), 36\u201343 (2019). https:\/\/doi.org\/10.1145\/3331166","journal-title":"Commun. ACM"},{"issue":"3","key":"11_CR26","doi-asserted-by":"publisher","first-page":"489","DOI":"10.3233\/SW-160218","volume":"8","author":"H Paulheim","year":"2017","unstructured":"Paulheim, H.: Knowledge graph refinement: a survey of approaches and evaluation methods. Semant. Web 8(3), 489\u2013508 (2017). https:\/\/doi.org\/10.3233\/SW-160218","journal-title":"Semant. Web"},{"key":"11_CR27","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"498","DOI":"10.1007\/978-3-319-46523-4_30","volume-title":"The Semantic Web \u2013 ISWC 2016","author":"P Ristoski","year":"2016","unstructured":"Ristoski, P., Paulheim, H.: RDF2Vec: RDF graph embeddings for data mining. In: Groth, P., et al. (eds.) ISWC 2016. LNCS, vol. 9981, pp. 498\u2013514. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46523-4_30"},{"key":"11_CR28","unstructured":"Ruffinelli, D., Broscheit, S., Gemulla, R.: You CAN teach an old dog new tricks! On training knowledge graph embeddings. In: 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, 26\u201330 April 2020. OpenReview.net (2020). https:\/\/openreview.net\/forum?id=BkxSmlBFvr"},{"key":"11_CR29","doi-asserted-by":"publisher","unstructured":"Safavi, T., Koutra, D.: CoDEx: a comprehensive knowledge graph completion benchmark. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Online, November 2020, pp. 8328\u20138350. Association for Computational Linguistics (2020). https:\/\/doi.org\/10.18653\/v1\/2020.emnlp-main.669","DOI":"10.18653\/v1\/2020.emnlp-main.669"},{"key":"11_CR30","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":"11_CR31","doi-asserted-by":"publisher","unstructured":"Schumacher, T., Wolf, H., Ritzert, M., Lemmerich, F., Grohe, M., Strohmaier, M.: The effects of randomness on the stability of node embeddings. In: Kamp, M., et al. (eds.) Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2021. Communications in Computer and Information Science, vol. 1524, p. 197215. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-93736-2_16","DOI":"10.1007\/978-3-030-93736-2_16"},{"key":"11_CR32","unstructured":"Shchur, O., Mumme, M., Bojchevski, A., G\u00fcnnemann, S.: Pitfalls of graph neural network evaluation (2019). https:\/\/arxiv.org\/abs\/1811.05868"},{"key":"11_CR33","doi-asserted-by":"crossref","unstructured":"Sun, Z., et al.: A benchmarking study of embedding-based entity alignment for knowledge graphs. Proc. VLDB Endow. 13(11), 2326\u20132340 (2020). http:\/\/www.vldb.org\/pvldb\/vol13\/p2326-sun.pdf","DOI":"10.14778\/3407790.3407828"},{"key":"11_CR34","unstructured":"Sun, Z., Deng, Z., Nie, J., Tang, J.: Rotate: knowledge graph embedding by relational rotation in complex space. In: 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, 6\u20139 May 2019. OpenReview.net (2019). https:\/\/openreview.net\/forum?id=HkgEQnRqYQ"},{"key":"11_CR35","doi-asserted-by":"publisher","unstructured":"Toutanova, K., Chen, D.: Observed versus latent features for knowledge base and text inference. In: Proceedings of the 3rd Workshop on Continuous Vector Space Models and their Compositionality, CVSC 2015, Beijing, China, 26\u201331 July 2015, pp. 57\u201366. Association for Computational Linguistics (2015). https:\/\/doi.org\/10.18653\/V1\/W15-4007","DOI":"10.18653\/V1\/W15-4007"},{"key":"11_CR36","unstructured":"Trouillon, T., Welbl, J., Riedel, S., Gaussier, \u00c9., Bouchard, G.: Complex embeddings for simple link prediction. In: Proceedings of the 33nd International Conference on Machine Learning, ICML 2016. JMLR Workshop and Conference Proceedings, New York City, NY, USA, 19\u201324 June 2016, vol.\u00a048, pp. 2071\u20132080. JMLR.org (2016). http:\/\/proceedings.mlr.press\/v48\/trouillon16.html"},{"key":"11_CR37","doi-asserted-by":"publisher","unstructured":"Wang, H., et al.: RippleNet: propagating user preferences on the knowledge graph for recommender systems. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, CIKM \u201918, New York, NY, USA, pp. 417\u2013426. Association for Computing Machinery (2018). https:\/\/doi.org\/10.1145\/3269206.3271739","DOI":"10.1145\/3269206.3271739"},{"key":"11_CR38","doi-asserted-by":"publisher","unstructured":"Xiong, B., et al.: Geometric relational embeddings: a survey. CoRR abs\/2304.11949 (2023). https:\/\/doi.org\/10.48550\/ARXIV.2304.11949","DOI":"10.48550\/ARXIV.2304.11949"},{"key":"11_CR39","unstructured":"Yang, B., Yih, W., He, X., Gao, J., Deng, L.: Embedding entities and relations for learning and inference in knowledge bases. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, Conference Track Proceedings, 7\u20139 May 2015 (2015). http:\/\/arxiv.org\/abs\/1412.6575"},{"key":"11_CR40","unstructured":"Yang, B., Yih, W., He, X., Gao, J., Deng, L.: Embedding entities and relations for learning and inference in knowledge bases. In: 3rd International Conference on Learning Representations, ICLR 2015, Conference Track Proceedings, San Diego, CA, USA, 7\u20139 May 2015 (2015). http:\/\/arxiv.org\/abs\/1412.6575"},{"key":"11_CR41","doi-asserted-by":"publisher","unstructured":"Yasunaga, M., Ren, H., Bosselut, A., Liang, P., Leskovec, J.: QA-GNN: reasoning with language models and knowledge graphs for question answering. In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Online, June 2021, pp. 535\u2013546. Association for Computational Linguistics (2021). https:\/\/doi.org\/10.18653\/v1\/2021.naacl-main.45, https:\/\/aclanthology.org\/2021.naacl-main.45\/","DOI":"10.18653\/v1\/2021.naacl-main.45"},{"key":"11_CR42","doi-asserted-by":"publisher","unstructured":"Zhang, W., et al.: Iteratively learning embeddings and rules for knowledge graph reasoning. In: The World Wide Web Conference, WWW 2019, San Francisco, CA, USA, 13\u201317 May 2019, pp. 2366\u20132377. ACM (2019). https:\/\/doi.org\/10.1145\/3308558.3313612","DOI":"10.1145\/3308558.3313612"},{"key":"11_CR43","doi-asserted-by":"publisher","unstructured":"Zhu, Y., et al.: Predictive multiplicity of knowledge graph embeddings in link prediction. In: Findings of the Association for Computational Linguistics, EMNLP 2024, pp. 334\u2013354. Association for Computational Linguistics, November 2024, Miami, Florida, USA (2024). https:\/\/doi.org\/10.18653\/v1\/2024.findings-emnlp.19, https:\/\/aclanthology.org\/2024.findings-emnlp.19\/","DOI":"10.18653\/v1\/2024.findings-emnlp.19"},{"key":"11_CR44","unstructured":"Zhu, Z., Zhang, Z., Xhonneux, L.P., Tang, J.: Neural bellman-ford networks: a general graph neural network framework for link prediction. Adv. Neural Inf. Process. Syst. 34 (2021)"}],"container-title":["Lecture Notes in Computer Science","The Semantic Web"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-25156-5_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T10:24:18Z","timestamp":1778063058000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-25156-5_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032251558","9783032251565"],"references-count":44,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-25156-5_11","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":"7 May 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"To ensure the reproducibility of our findings, all code and datasets (WN18RR, FB15k-237, CoDEx-s, kinship and nations) are provided in our GitHub repository (see Footnote 2), allowing to reproduce training, evaluation, and similarity computations. Hyperparameter configurations of grid search and selected models are documented in the README. We also precisely report the hardware (GPUs) and seeds employed in each experiment in the paper.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Supplemental Material Statement"}},{"value":"In the preparation of this paper, ChatGPT was used as a writing assistant for grammar and spelling checks. In addition, Windsurf Editor, powered by Claude 3.5, was used as a coding assistant for implementation details. All scientific contributions, analyses, and experimental results presented in this paper are the authors\u2019 own.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Use of Generative AI"}},{"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":"Dubrovnik","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Croatia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 May 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 May 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"esws2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2026.eswc-conferences.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}