{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T19:07:03Z","timestamp":1776884823361,"version":"3.51.2"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031472398","type":"print"},{"value":"9783031472404","type":"electronic"}],"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\/978-3-031-47240-4_29","type":"book-chapter","created":{"date-parts":[[2023,11,1]],"date-time":"2023-11-01T08:02:40Z","timestamp":1698825760000},"page":"541-560","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["ForecastTKGQuestions: A Benchmark for\u00a0Temporal Question Answering and\u00a0Forecasting over\u00a0Temporal Knowledge Graphs"],"prefix":"10.1007","author":[{"given":"Zifeng","family":"Ding","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zongyue","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruoxia","family":"Qi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingpei","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bailan","family":"He","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yunpu","family":"Ma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhao","family":"Meng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuo","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruotong","family":"Liao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhen","family":"Han","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Volker","family":"Tresp","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,10,27]]},"reference":[{"key":"29_CR1","unstructured":"Bordes, A., Usunier, N., Chopra, S., Weston, J.: Large-scale simple question answering with memory networks (2015). arxiv.org:1506.02075"},{"key":"29_CR2","doi-asserted-by":"publisher","unstructured":"Boschee, E., Lautenschlager, J., O\u2019Brien, S., Shellman, S., Starz, J., Ward, M.: ICEWS Coded Event Data (2015). https:\/\/doi.org\/10.7910\/DVN\/28075","DOI":"10.7910\/DVN\/28075"},{"key":"29_CR3","doi-asserted-by":"publisher","unstructured":"Cao, Y., Ji, X., Lv, X., Li, J., Wen, Y., Zhang, H.: Are missing links predictable? an inferential benchmark for knowledge graph completion. In: Zong, C., Xia, F., Li, W., Navigli, R. (eds.) Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ACL\/IJCNLP 2021, (Volume 1: Long Papers), Virtual Event, August 1\u20136, 2021, pp. 6855\u20136865. Association for Computational Linguistics (2021). https:\/\/doi.org\/10.18653\/v1\/2021.acl-long.534","DOI":"10.18653\/v1\/2021.acl-long.534"},{"key":"29_CR4","doi-asserted-by":"publisher","unstructured":"Chen, Z., Zhao, X., Liao, J., Li, X., Kanoulas, E.: Temporal knowledge graph question answering via subgraph reasoning. Knowl. Based Syst. 251, 109134 (2022). https:\/\/doi.org\/10.1016\/j.knosys.2022.109134","DOI":"10.1016\/j.knosys.2022.109134"},{"key":"29_CR5","doi-asserted-by":"publisher","unstructured":"Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. 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, NAACL-HLT 2019, Minneapolis, MN, USA, June 2\u20137, 2019, Volume 1 (Long and Short Papers), pp. 4171\u20134186. Association for Computational Linguistics (2019). https:\/\/doi.org\/10.18653\/v1\/n19-1423","DOI":"10.18653\/v1\/n19-1423"},{"key":"29_CR6","unstructured":"Ding, Z., Ma, Y., He, B., Han, Z., Tresp, V.: A simple but powerful graph encoder for temporal knowledge graph completion. In: NeurIPS 2022 Temporal Graph Learning Workshop (2022). https:\/\/openreview.net\/forum?id=DYG8RbgAIo"},{"key":"29_CR7","doi-asserted-by":"publisher","unstructured":"Gal\u00e1rraga, L.A., Teflioudi, C., Hose, K., Suchanek, F.M.: AMIE: association rule mining under incomplete evidence in ontological knowledge bases. In: Schwabe, D., Almeida, V.A.F., Glaser, H., Baeza-Yates, R., Moon, S.B. (eds.) 22nd International World Wide Web Conference, WWW \u201913, Rio de Janeiro, Brazil, May 13\u201317, 2013, pp. 413\u2013422. International World Wide Web Conferences Steering Committee \/ ACM (2013). https:\/\/doi.org\/10.1145\/2488388.2488425","DOI":"10.1145\/2488388.2488425"},{"key":"29_CR8","unstructured":"Han, Z., Chen, P., Ma, Y., Tresp, V.: Explainable subgraph reasoning for forecasting on temporal knowledge graphs. In: 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3\u20137, 2021. OpenReview.net (2021). https:\/\/openreview.net\/forum?id=pGIHq1m7PU"},{"key":"29_CR9","doi-asserted-by":"publisher","unstructured":"Han, Z., Ding, Z., Ma, Y., Gu, Y., Tresp, V.: Learning neural ordinary equations for forecasting future links on temporal knowledge graphs. In: Moens, M., Huang, X., Specia, L., Yih, S.W. (eds.) Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021, Virtual Event \/ Punta Cana, Dominican Republic, 7\u201311 November, 2021, pp. 8352\u20138364. Association for Computational Linguistics (2021). https:\/\/doi.org\/10.18653\/v1\/2021.emnlp-main.658","DOI":"10.18653\/v1\/2021.emnlp-main.658"},{"key":"29_CR10","doi-asserted-by":"publisher","unstructured":"Ji, H., Ke, P., Huang, S., Wei, F., Zhu, X., Huang, M.: Language generation with multi-hop reasoning on commonsense knowledge graph. In: Webber, B., Cohn, T., He, Y., Liu, Y. (eds.) Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020, Online, November 16\u201320, 2020, pp. 725\u2013736. Association for Computational Linguistics (2020). https:\/\/doi.org\/10.18653\/v1\/2020.emnlp-main.54","DOI":"10.18653\/v1\/2020.emnlp-main.54"},{"key":"29_CR11","doi-asserted-by":"publisher","unstructured":"Jia, Z., Abujabal, A., Roy, R.S., Str\u00f6tgen, J., Weikum, G.: Tempquestions: A benchmark for temporal question answering. In: Champin, P., Gandon, F., Lalmas, M., Ipeirotis, P.G. (eds.) Companion of the The Web Conference 2018 on The Web Conference 2018, WWW 2018, Lyon, France, April 23\u201327, 2018, pp. 1057\u20131062. ACM (2018). https:\/\/doi.org\/10.1145\/3184558.3191536","DOI":"10.1145\/3184558.3191536"},{"key":"29_CR12","doi-asserted-by":"publisher","unstructured":"Jia, Z., Pramanik, S., Roy, R.S., Weikum, G.: Complex temporal question answering on knowledge graphs. In: Demartini, G., Zuccon, G., Culpepper, J.S., Huang, Z., Tong, H. (eds.) CIKM \u201921: The 30th ACM International Conference on Information and Knowledge Management, Virtual Event, Queensland, Australia, November 1\u20135, 2021, pp. 792\u2013802. ACM (2021). https:\/\/doi.org\/10.1145\/3459637.3482416","DOI":"10.1145\/3459637.3482416"},{"key":"29_CR13","doi-asserted-by":"publisher","unstructured":"Jin, W., et al.: Forecastqa: A question answering challenge for event forecasting with temporal text data. In: Zong, C., Xia, F., Li, W., Navigli, R. (eds.) Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ACL\/IJCNLP 2021, (Volume 1: Long Papers), Virtual Event, August 1\u20136, 2021, pp. 4636\u20134650. Association for Computational Linguistics (2021). https:\/\/doi.org\/10.18653\/v1\/2021.acl-long.357","DOI":"10.18653\/v1\/2021.acl-long.357"},{"key":"29_CR14","doi-asserted-by":"publisher","unstructured":"Jin, W., Qu, M., Jin, X., Ren, X.: Recurrent event network: Autoregressive structure inferenceover temporal knowledge graphs. In: Webber, B., Cohn, T., He, Y., Liu, Y. (eds.) Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020, Online, November 16\u201320, 2020, pp. 6669\u20136683. Association for Computational Linguistics (2020). https:\/\/doi.org\/10.18653\/v1\/2020.emnlp-main.541","DOI":"10.18653\/v1\/2020.emnlp-main.541"},{"key":"29_CR15","doi-asserted-by":"publisher","unstructured":"Jung, J., Jung, J., Kang, U.: Learning to walk across time for interpretable temporal knowledge graph completion. In: Zhu, F., Ooi, B.C., Miao, C. (eds.) KDD \u201921: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Virtual Event, Singapore, August 14\u201318, 2021, pp. 786\u2013795. ACM (2021). https:\/\/doi.org\/10.1145\/3447548.3467292","DOI":"10.1145\/3447548.3467292"},{"key":"29_CR16","unstructured":"Lacroix, T., Obozinski, G., Usunier, N.: Tensor decompositions for temporal knowledge base completion. In: 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26\u201330, 2020. OpenReview.net (2020), https:\/\/openreview.net\/forum?id=rke2P1BFwS"},{"key":"29_CR17","doi-asserted-by":"publisher","unstructured":"Liu, Y., et al.: Roberta: A robustly optimized BERT pretraining approach (2019). https:\/\/doi.org\/10.48550\/ARXIV.1907.11692","DOI":"10.48550\/ARXIV.1907.11692"},{"key":"29_CR18","doi-asserted-by":"crossref","unstructured":"Liu, Y., Ma, Y., Hildebrandt, M., Joblin, M., Tresp, V.: Tlogic: Temporal logical rules for explainable link forecasting on temporal knowledge graphs. In: Thirty-Sixth AAAI Conference on Artificial Intelligence, AAAI 2022, Thirty-Fourth Conference on Innovative Applications of Artificial Intelligence, IAAI 2022, The Twelveth Symposium on Educational Advances in Artificial Intelligence, EAAI 2022 Virtual Event, February 22 - March 1, 2022, pp. 4120\u20134127. AAAI Press (2022). https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/20330","DOI":"10.1609\/aaai.v36i4.20330"},{"key":"29_CR19","doi-asserted-by":"crossref","unstructured":"Mavromatis, C., et al.: Tempoqr: Temporal question reasoning over knowledge graphs. In: Thirty-Sixth AAAI Conference on Artificial Intelligence, AAAI 2022, Thirty-Fourth Conference on Innovative Applications of Artificial Intelligence, IAAI 2022, The Twelveth Symposium on Educational Advances in Artificial Intelligence, EAAI 2022 Virtual Event, February 22 - March 1, 2022, pp. 5825\u20135833. AAAI Press (2022). https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/20526","DOI":"10.1609\/aaai.v36i5.20526"},{"key":"29_CR20","doi-asserted-by":"crossref","unstructured":"Meilicke, C., Chekol, M.W., Fink, M., Stuckenschmidt, H.: Reinforced anytime bottom up rule learning for knowledge graph completion (2020). arxiv.org:2004.04412","DOI":"10.24963\/ijcai.2019\/435"},{"key":"29_CR21","doi-asserted-by":"publisher","unstructured":"Saxena, A., Chakrabarti, S., Talukdar, P.P.: Question answering over temporal knowledge graphs. In: Zong, C., Xia, F., Li, W., Navigli, R. (eds.) Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ACL\/IJCNLP 2021, (Volume 1: Long Papers), Virtual Event, August 1\u20136, 2021, pp. 6663\u20136676. Association for Computational Linguistics (2021). https:\/\/doi.org\/10.18653\/v1\/2021.acl-long.520","DOI":"10.18653\/v1\/2021.acl-long.520"},{"key":"29_CR22","doi-asserted-by":"publisher","unstructured":"Saxena, A., Tripathi, A., Talukdar, P.: Improving multi-hop question answering over knowledge graphs using knowledge base embeddings. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 4498\u20134507. Association for Computational Linguistics, Online (2020). https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.412, https:\/\/aclanthology.org\/2020.acl-main.412","DOI":"10.18653\/v1\/2020.acl-main.412"},{"key":"29_CR23","doi-asserted-by":"crossref","unstructured":"Shang, C., Wang, G., Qi, P., Huang, J.: Improving time sensitivity for question answering over temporal knowledge graphs. In: Muresan, S., Nakov, P., Villavicencio, A. (eds.) Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2022, Dublin, Ireland, May 22\u201327, 2022, pp. 8017\u20138026. Association for Computational Linguistics (2022). https:\/\/aclanthology.org\/2022.acl-long.552","DOI":"10.18653\/v1\/2022.acl-long.552"},{"key":"29_CR24","doi-asserted-by":"publisher","unstructured":"Talmor, A., Berant, J.: The web as a knowledge-base for answering complex questions. In: Walker, M.A., Ji, H., Stent, A. (eds.) Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2018, New Orleans, Louisiana, USA, June 1\u20136, 2018, Volume 1 (Long Papers), pp. 641\u2013651. Association for Computational Linguistics (2018). https:\/\/doi.org\/10.18653\/v1\/n18-1059","DOI":"10.18653\/v1\/n18-1059"},{"key":"29_CR25","unstructured":"Trivedi, R., Dai, H., Wang, Y., Song, L.: Know-evolve: Deep temporal reasoning for dynamic knowledge graphs. In: Precup, D., Teh, Y.W. (eds.) Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6\u201311 August 2017. Proceedings of Machine Learning Research, vol. 70, pp. 3462\u20133471. PMLR (2017). http:\/\/proceedings.mlr.press\/v70\/trivedi17a.html"},{"key":"29_CR26","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 33nd International Conference on Machine Learning, ICML 2016, New York City, NY, USA, June 19\u201324, 2016. JMLR Workshop and Conference Proceedings, vol. 48, pp. 2071\u20132080. JMLR.org (2016), http:\/\/proceedings.mlr.press\/v48\/trouillon16.html"},{"key":"29_CR27","doi-asserted-by":"publisher","unstructured":"Vrandecic, D., Kr\u00f6tzsch, M.: Wikidata: a free collaborative knowledgebase. Commun. ACM 57(10), 78\u201385 (2014). https:\/\/doi.org\/10.1145\/2629489","DOI":"10.1145\/2629489"},{"key":"29_CR28","doi-asserted-by":"publisher","unstructured":"Yih, W., Chang, M., He, X., Gao, J.: Semantic parsing via staged query graph generation: Question answering with knowledge base. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, ACL 2015, July 26\u201331, 2015, Beijing, China, Volume 1: Long Papers, pp. 1321\u20131331. The Association for Computer Linguistics (2015). https:\/\/doi.org\/10.3115\/v1\/p15-1128","DOI":"10.3115\/v1\/p15-1128"},{"key":"29_CR29","unstructured":"Zhang, Y., Dai, H., Kozareva, Z., Smola, A.J., Song, L.: Variational reasoning for question answering with knowledge graph. In: McIlraith, S.A., Weinberger, K.Q. (eds.) 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, February 2\u20137, 2018, pp. 6069\u20136076. AAAI Press (2018). https:\/\/www.aaai.org\/ocs\/index.php\/AAAI\/AAAI18\/paper\/view\/16983"},{"key":"29_CR30","doi-asserted-by":"crossref","unstructured":"Zhu, C., Chen, M., Fan, C., Cheng, G., Zhang, Y.: Learning from history: Modeling temporal knowledge graphs with sequential copy-generation networks. In: Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, Thirty-Third Conference on Innovative Applications of Artificial Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances in Artificial Intelligence, EAAI 2021, Virtual Event, February 2\u20139, 2021, pp. 4732\u20134740. AAAI Press (2021). https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/16604","DOI":"10.1609\/aaai.v35i5.16604"}],"container-title":["Lecture Notes in Computer Science","The Semantic Web \u2013 ISWC 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-47240-4_29","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,1]],"date-time":"2023-11-01T08:09:37Z","timestamp":1698826177000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-47240-4_29"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031472398","9783031472404"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-47240-4_29","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"27 October 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ISWC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Semantic Web Conference","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Athens","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","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":"6 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 November 2023","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":"semweb2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iswc2023.semanticweb.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easy Chair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"248","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":"58","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":"23% - 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":"1","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}