{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,11,19]],"date-time":"2024-11-19T19:05:00Z","timestamp":1732043100515},"publisher-location":"Cham","reference-count":57,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031472428"},{"type":"electronic","value":"9783031472435"}],"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\/978-3-031-47243-5_2","type":"book-chapter","created":{"date-parts":[[2023,11,1]],"date-time":"2023-11-01T07:02:30Z","timestamp":1698822150000},"page":"18-37","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["MMpedia: A Large-Scale Multi-modal Knowledge Graph"],"prefix":"10.1007","author":[{"given":"Yinan","family":"Wu","sequence":"first","affiliation":[]},{"given":"Xiaowei","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Junwen","family":"Li","sequence":"additional","affiliation":[]},{"given":"Yue","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Haofen","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Wen","family":"Du","sequence":"additional","affiliation":[]},{"given":"Zhidong","family":"He","sequence":"additional","affiliation":[]},{"given":"Jingping","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Tong","family":"Ruan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,27]]},"reference":[{"key":"2_CR1","doi-asserted-by":"publisher","first-page":"69788","DOI":"10.1109\/ACCESS.2022.3187178","volume":"10","author":"S Aghaei","year":"2022","unstructured":"Aghaei, S., Raad, E., Fensel, A.: Question answering over knowledge graphs: a case study in tourism. IEEE Access 10, 69788\u201369801 (2022)","journal-title":"IEEE Access"},{"key":"2_CR2","doi-asserted-by":"crossref","unstructured":"Alberts, H., et al.: VisualSem: a high-quality knowledge graph for vision and language. arXiv preprint arXiv:2008.09150 (2020)","DOI":"10.18653\/v1\/2021.mrl-1.13"},{"key":"2_CR3","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. 26 (2013)"},{"key":"2_CR4","doi-asserted-by":"crossref","unstructured":"Calabrese, A., Bevilacqua, M., Navigli, R.: Fatality killed the cat or: BabelPic, a multimodal dataset for non-concrete concepts. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 4680\u20134686 (2020)","DOI":"10.18653\/v1\/2020.acl-main.425"},{"key":"2_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1007\/978-3-030-73197-7_12","volume-title":"Database Systems for Advanced Applications","author":"D Chen","year":"2021","unstructured":"Chen, D., Li, Z., Gu, B., Chen, Z.: Multimodal named entity recognition with image attributes and image knowledge. In: Jensen, C.S., et al. (eds.) DASFAA 2021. LNCS, vol. 12682, pp. 186\u2013201. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-73197-7_12"},{"issue":"12","key":"2_CR6","doi-asserted-by":"publisher","first-page":"9205","DOI":"10.1109\/JIOT.2021.3093065","volume":"9","author":"Q Chen","year":"2021","unstructured":"Chen, Q., Wang, W., Huang, K., Coenen, F.: Zero-shot text classification via knowledge graph embedding for social media data. IEEE Internet Things J. 9(12), 9205\u20139213 (2021)","journal-title":"IEEE Internet Things J."},{"key":"2_CR7","doi-asserted-by":"crossref","unstructured":"Chen, X., et al.: Hybrid transformer with multi-level fusion for multimodal knowledge graph completion. arXiv preprint arXiv:2205.02357 (2022)","DOI":"10.1145\/3477495.3531992"},{"key":"2_CR8","doi-asserted-by":"crossref","unstructured":"Chen, X., Shrivastava, A., Gupta, A.: NEIL: extracting visual knowledge from web data. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1409\u20131416 (2013)","DOI":"10.1109\/ICCV.2013.178"},{"key":"2_CR9","doi-asserted-by":"crossref","unstructured":"Cheng, M., et al.: ViSTA: vision and scene text aggregation for cross-modal retrieval. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5184\u20135193 (2022)","DOI":"10.1109\/CVPR52688.2022.00512"},{"key":"2_CR10","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1007\/978-3-319-91473-2_7","volume-title":"Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Foundations","author":"D Colla","year":"2018","unstructured":"Colla, D., Mensa, E., Radicioni, D.P., Lieto, A.: Tell me why: computational explanation of conceptual similarity judgments. In: Medina, J., et al. (eds.) IPMU 2018. CCIS, vol. 853, pp. 74\u201385. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-91473-2_7"},{"key":"2_CR11","doi-asserted-by":"crossref","unstructured":"Corbiere, C., Ben-Younes, H., Ram\u00e9, A., Ollion, C.: Leveraging weakly annotated data for fashion image retrieval and label prediction. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 2268\u20132274 (2017)","DOI":"10.1109\/ICCVW.2017.266"},{"key":"2_CR12","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)"},{"key":"2_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1007\/978-3-319-68204-4_8","volume-title":"The Semantic Web \u2013 ISWC 2017","author":"S Ferrada","year":"2017","unstructured":"Ferrada, S., Bustos, B., Hogan, A.: IMGpedia: a linked dataset with content-based analysis of Wikimedia images. In: d\u2019Amato, C., et al. (eds.) ISWC 2017. LNCS, vol. 10588, pp. 84\u201393. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-68204-4_8"},{"issue":"5","key":"2_CR14","doi-asserted-by":"publisher","first-page":"378","DOI":"10.1037\/h0031619","volume":"76","author":"JL Fleiss","year":"1971","unstructured":"Fleiss, J.L.: Measuring nominal scale agreement among many raters. Psychol. Bull. 76(5), 378 (1971)","journal-title":"Psychol. Bull."},{"key":"2_CR15","unstructured":"Gao, J., Zhao, H., Yu, C., Xu, R.: Exploring the feasibility of ChatGPT for event extraction. arXiv preprint arXiv:2303.03836 (2023)"},{"key":"2_CR16","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"2_CR17","doi-asserted-by":"crossref","unstructured":"Hendriksen, M., Vakulenko, S., Kuiper, E., de Rijke, M.: Scene-centric vs. object-centric image-text cross-modal retrieval: a reproducibility study. arXiv preprint arXiv:2301.05174 (2023)","DOI":"10.1007\/978-3-031-28241-6_5"},{"key":"2_CR18","doi-asserted-by":"publisher","first-page":"166","DOI":"10.1016\/j.neucom.2022.07.079","volume":"507","author":"H Kang","year":"2022","unstructured":"Kang, H., et al.: TSPNet: translation supervised prototype network via residual learning for multimodal social relation extraction. Neurocomputing 507, 166\u2013179 (2022)","journal-title":"Neurocomputing"},{"key":"2_CR19","unstructured":"Kim, W., Son, B., Kim, I.: ViLT: vision-and-language transformer without convolution or region supervision. In: International Conference on Machine Learning, pp. 5583\u20135594. PMLR (2021)"},{"issue":"1","key":"2_CR20","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1007\/s11263-016-0981-7","volume":"123","author":"R Krishna","year":"2017","unstructured":"Krishna, R., et al.: Visual genome: connecting language and vision using crowdsourced dense image annotations. Int. J. Comput. Vision 123(1), 32\u201373 (2017)","journal-title":"Int. J. Comput. Vision"},{"issue":"2","key":"2_CR21","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_CR22","doi-asserted-by":"crossref","unstructured":"Li, M., et al.: Gaia: a fine-grained multimedia knowledge extraction system. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp. 77\u201386 (2020)","DOI":"10.18653\/v1\/2020.acl-demos.11"},{"key":"2_CR23","doi-asserted-by":"crossref","unstructured":"Li, Y., Li, J., Jin, H., Peng, L.: Focusing attention across multiple images for multimodal event detection. In: ACM Multimedia Asia, pp. 1\u20136. Association for Computing Machinery (2021)","DOI":"10.1145\/3469877.3495642"},{"key":"2_CR24","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1007\/978-3-319-10602-1_48","volume-title":"Computer Vision \u2013 ECCV 2014","author":"T-Y Lin","year":"2014","unstructured":"Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740\u2013755. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10602-1_48"},{"key":"2_CR25","doi-asserted-by":"crossref","unstructured":"Liu, C., Mao, Z., Zhang, T., Xie, H., Wang, B., Zhang, Y.: Graph structured network for image-text matching. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10921\u201310930 (2020)","DOI":"10.1109\/CVPR42600.2020.01093"},{"key":"2_CR26","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"459","DOI":"10.1007\/978-3-030-21348-0_30","volume-title":"The Semantic Web","author":"Y Liu","year":"2019","unstructured":"Liu, Y., Li, H., Garcia-Duran, A., Niepert, M., Onoro-Rubio, D., Rosenblum, D.S.: MMKG: multi-modal knowledge graphs. In: Hitzler, P., et al. (eds.) ESWC 2019. LNCS, vol. 11503, pp. 459\u2013474. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-21348-0_30"},{"key":"2_CR27","unstructured":"Lu, J., Batra, D., Parikh, D., Lee, S.: ViLBERT: pretraining task-agnostic visiolinguistic representations for vision-and-language tasks. In: Advances in Neural Information Processing Systems, vol. 32 (2019)"},{"key":"2_CR28","doi-asserted-by":"crossref","unstructured":"Mafla, A., Rezende, R.S., Gomez, L., Larlus, D., Karatzas, D.: StacMR: scene-text aware cross-modal retrieval. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 2220\u20132230 (2021)","DOI":"10.1109\/WACV48630.2021.00227"},{"key":"2_CR29","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1016\/j.artint.2012.07.001","volume":"193","author":"R Navigli","year":"2012","unstructured":"Navigli, R., Ponzetto, S.P.: BabelNet: the automatic construction, evaluation and application of a wide-coverage multilingual semantic network. Artif. Intell. 193, 217\u2013250 (2012)","journal-title":"Artif. Intell."},{"key":"2_CR30","unstructured":"O\u00f1oro-Rubio, D., Niepert, M., Garc\u00eda-Dur\u00e1n, A., Gonz\u00e1lez, R., L\u00f3pez-Sastre, R.J.: Answering visual-relational queries in web-extracted knowledge graphs. arXiv preprint arXiv:1709.02314 (2017)"},{"key":"2_CR31","doi-asserted-by":"crossref","unstructured":"Peng, Y., Zhang, J.: LineaRE: simple but powerful knowledge graph embedding for link prediction. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 422\u2013431. IEEE (2020)","DOI":"10.1109\/ICDM50108.2020.00051"},{"key":"2_CR32","unstructured":"Radford, A., et al.: Learning transferable visual models from natural language supervision. In: International Conference on Machine Learning, pp. 8748\u20138763. PMLR (2021)"},{"key":"2_CR33","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779\u2013788 (2016)","DOI":"10.1109\/CVPR.2016.91"},{"key":"2_CR34","doi-asserted-by":"crossref","unstructured":"Reimers, N., Gurevych, I.: Sentence-BERT: sentence embeddings using Siamese BERT-networks. arXiv preprint arXiv:1908.10084 (2019)","DOI":"10.18653\/v1\/D19-1410"},{"issue":"3","key":"2_CR35","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky, O., et al.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vision 115(3), 211\u2013252 (2015)","journal-title":"Int. J. Comput. Vision"},{"key":"2_CR36","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"2_CR37","doi-asserted-by":"crossref","unstructured":"Suchanek, F.M., Kasneci, G., Weikum, G.: Yago: a core of semantic knowledge. In: Proceedings of the 16th International Conference on World Wide Web, pp. 697\u2013706 (2007)","DOI":"10.1145\/1242572.1242667"},{"key":"2_CR38","doi-asserted-by":"crossref","unstructured":"Sun, R., et al.: Multi-modal knowledge graphs for recommender systems. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp. 1405\u20131414 (2020)","DOI":"10.1145\/3340531.3411947"},{"key":"2_CR39","unstructured":"Sun, Z., Deng, Z.H., Nie, J.Y., Tang, J.: Rotate: knowledge graph embedding by relational rotation in complex space. arXiv preprint arXiv:1902.10197 (2019)"},{"issue":"2","key":"2_CR40","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1145\/2812802","volume":"59","author":"B Thomee","year":"2016","unstructured":"Thomee, B., et al.: YFCC100M: the new data in multimedia research. Commun. ACM 59(2), 64\u201373 (2016)","journal-title":"Commun. ACM"},{"key":"2_CR41","doi-asserted-by":"crossref","unstructured":"Tong, M., Wang, S., Cao, Y., Xu, B., Li, J., Hou, L., Chua, T.S.: Image enhanced event detection in news articles. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 9040\u20139047 (2020)","DOI":"10.1609\/aaai.v34i05.6437"},{"key":"2_CR42","unstructured":"Trouillon, T., Welbl, J., Riedel, S., Gaussier, \u00c9., Bouchard, G.: Complex embeddings for simple link prediction. In: International Conference on Machine Learning, pp. 2071\u20132080. PMLR (2016)"},{"key":"2_CR43","first-page":"200","volume":"34","author":"M Tsimpoukelli","year":"2021","unstructured":"Tsimpoukelli, M., Menick, J.L., Cabi, S., Eslami, S., Vinyals, O., Hill, F.: Multimodal few-shot learning with frozen language models. Adv. Neural. Inf. Process. Syst. 34, 200\u2013212 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"10","key":"2_CR44","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1145\/2629489","volume":"57","author":"D Vrande\u010di\u0107","year":"2014","unstructured":"Vrande\u010di\u0107, D., Kr\u00f6tzsch, M.: Wikidata: a free collaborative knowledgebase. Commun. ACM 57(10), 78\u201385 (2014)","journal-title":"Commun. ACM"},{"key":"2_CR45","doi-asserted-by":"publisher","first-page":"2515","DOI":"10.1109\/TMM.2021.3083109","volume":"24","author":"H Wang","year":"2021","unstructured":"Wang, H., et al.: Cross-modal food retrieval: learning a joint embedding of food images and recipes with semantic consistency and attention mechanism. IEEE Trans. Multimedia 24, 2515\u20132525 (2021)","journal-title":"IEEE Trans. Multimedia"},{"key":"2_CR46","doi-asserted-by":"publisher","DOI":"10.1016\/j.bdr.2020.100159","volume":"22","author":"M Wang","year":"2020","unstructured":"Wang, M., Wang, H., Qi, G., Zheng, Q.: Richpedia: a large-scale, comprehensive multi-modal knowledge graph. Big Data Res. 22, 100159 (2020)","journal-title":"Big Data Res."},{"key":"2_CR47","doi-asserted-by":"crossref","unstructured":"Wang, M., Wang, S., Yang, H., Zhang, Z., Chen, X., Qi, G.: Is visual context really helpful for knowledge graph? A representation learning perspective. In: Proceedings of the 29th ACM International Conference on Multimedia, pp. 2735\u20132743 (2021)","DOI":"10.1145\/3474085.3475470"},{"key":"2_CR48","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1007\/978-3-031-00129-1_24","volume-title":"Database Systems for Advanced Applications","author":"X Wang","year":"2022","unstructured":"Wang, X., et al.: PromptMNER: prompt-based entity-related visual clue extraction and integration for multimodal named entity recognition. In: Bhattacharya, A., et al. (eds.) DASFAA 2022. LNCS, vol. 13247, pp. 297\u2013305. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-00129-1_24"},{"key":"2_CR49","doi-asserted-by":"crossref","unstructured":"Wen, H., et al.: Resin: a dockerized schema-guided cross-document cross-lingual cross-media information extraction and event tracking system. In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Demonstrations, pp. 133\u2013143 (2021)","DOI":"10.18653\/v1\/2021.naacl-demos.16"},{"key":"2_CR50","unstructured":"Wu, C., Yin, S., Qi, W., Wang, X., Tang, Z., Duan, N.: Visual ChatGPT: talking, drawing and editing with visual foundation models. arXiv preprint arXiv:2303.04671 (2023)"},{"key":"2_CR51","doi-asserted-by":"crossref","unstructured":"Wu, Y., Zhan, P., Zhang, Y., Wang, L., Xu, Z.: Multimodal fusion with co-attention networks for fake news detection. In: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pp. 2560\u20132569 (2021)","DOI":"10.18653\/v1\/2021.findings-acl.226"},{"issue":"6","key":"2_CR52","doi-asserted-by":"publisher","first-page":"6196","DOI":"10.1007\/s10489-021-02647-1","volume":"52","author":"Y Yang","year":"2022","unstructured":"Yang, Y., Zhu, Y., Li, Y.: Personalized recommendation with knowledge graph via dual-autoencoder. Appl. Intell. 52(6), 6196\u20136207 (2022)","journal-title":"Appl. Intell."},{"key":"2_CR53","unstructured":"Yao, L., Mao, C., Luo, Y.: KG-BERT: BERT for knowledge graph completion. arXiv preprint arXiv:1909.03193 (2019)"},{"key":"2_CR54","unstructured":"Zhao, J., Huang, F., Lv, J., Duan, Y., Qin, Z., Li, G., Tian, G.: Do RNN and LSTM have long memory? In: International Conference on Machine Learning, pp. 11365\u201311375. PMLR (2020)"},{"key":"2_CR55","doi-asserted-by":"crossref","unstructured":"Zhao, Y., et al.: MoSE: modality split and ensemble for multimodal knowledge graph completion. arXiv preprint arXiv:2210.08821 (2022)","DOI":"10.18653\/v1\/2022.emnlp-main.719"},{"key":"2_CR56","doi-asserted-by":"crossref","unstructured":"Zheng, C., Wu, Z., Feng, J., Fu, Z., Cai, Y.: MNRE: a challenge multimodal dataset for neural relation extraction with visual evidence in social media posts. In: 2021 IEEE International Conference on Multimedia and Expo (ICME), pp. 1\u20136. IEEE (2021)","DOI":"10.1109\/ICME51207.2021.9428274"},{"key":"2_CR57","unstructured":"Zhu, X., et al.: Multi-modal knowledge graph construction and application: a survey. arXiv preprint arXiv:2202.05786 (2022)"}],"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\/978-3-031-47243-5_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,1]],"date-time":"2023-11-01T07:04:25Z","timestamp":1698822265000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-47243-5_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031472428","9783031472435"],"references-count":57,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-47243-5_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"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)"}}]}}