{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T02:25:40Z","timestamp":1743128740068,"version":"3.40.3"},"publisher-location":"Cham","reference-count":57,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030693763"},{"type":"electronic","value":"9783030693770"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-69377-0_17","type":"book-chapter","created":{"date-parts":[[2021,2,10]],"date-time":"2021-02-10T04:59:40Z","timestamp":1612933180000},"page":"204-219","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Modelling and Factorizing Large-Scale Knowledge Graph (DBPedia) for Fine-Grained Entity Type Inference"],"prefix":"10.1007","author":[{"given":"A. B. M.","family":"Moniruzzaman","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,2,10]]},"reference":[{"key":"17_CR1","unstructured":"\u201cDBPedia\u201d Public Semantic Knowledge Graph. http:\/\/wiki.dbpedia.org\/. Accessed 08 Aug 2017"},{"key":"17_CR2","unstructured":"\u201cPoblano Toolbox\u201d Poblano - Sandia software - Sandia National Laboratories. https:\/\/software.sandia.gov\/trac\/poblano\/. Accessed 29 Aug 2018"},{"key":"17_CR3","unstructured":"\u201cProbase\u201d Knowledge Base. https:\/\/www.microsoft.com\/en-us\/research\/project\/probase\/. Accessed 29 Sept 2018"},{"key":"17_CR4","unstructured":"\u201cRDF\u201d Resource Description Framework. https:\/\/www.w3.org\/RDF\/. Accessed 29 Sept 2018"},{"key":"17_CR5","unstructured":"\u201cSPRQL\u201d Query Language for RDF. https:\/\/www.w3.org\/TR\/rdf-sparql-query\/. Accessed 29 Sept 2018"},{"key":"17_CR6","unstructured":"\u201cTensor Toolbox\u201d MATLAB Tensor Toolbox. https:\/\/www.sandia.gov\/~tgkolda\/TensorToolbox\/index-2.6.html. Accessed 09 Aug 2018"},{"key":"17_CR7","unstructured":"\u201cYAGO\u201d semantic knowledge base. http:\/\/www.mpi-inf.mpg.de\/departments\/databases-and-information-systems\/research\/yago-naga\/yago\/. Accessed 09 Aug 2017"},{"key":"17_CR8","unstructured":"Acar, E., Kolda, T.G., Dunlavy, D.M.: All-at-once optimization for coupled matrix and tensor factorizations. arXiv preprint arXiv:1105.3422 (2011)"},{"key":"17_CR9","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/j.chemolab.2013.06.006","volume":"129","author":"E Acar","year":"2013","unstructured":"Acar, E., Rasmussen, M.A., Savorani, F., N\u00e6s, T., Bro, R.: Understanding data fusion within the framework of coupled matrix and tensor factorizations. Chemometr. Intell. Lab. Syst. 129, 53\u201363 (2013)","journal-title":"Chemometr. Intell. Lab. Syst."},{"key":"17_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"722","DOI":"10.1007\/978-3-540-76298-0_52","volume-title":"The Semantic Web","author":"S Auer","year":"2007","unstructured":"Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., Ives, Z.: DBpedia: a nucleus for a web of open data. In: Aberer, K., et al. (eds.) ASWC\/ISWC -2007. LNCS, vol. 4825, pp. 722\u2013735. Springer, Heidelberg (2007). https:\/\/doi.org\/10.1007\/978-3-540-76298-0_52"},{"key":"17_CR11","unstructured":"Azmy, M., Shi, P., Lin, J., Ilyas, I.: Farewell freebase: migrating the simplequestions dataset to DBpedia. In: Proceedings of the 27th International Conference on Computational Linguistics, pp. 2093\u20132103 (2018)"},{"key":"17_CR12","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, pp. 1533\u20131544 (2013)"},{"key":"17_CR13","doi-asserted-by":"crossref","unstructured":"Bollacker, K., Evans, C., Paritosh, P., Sturge, T., Taylor, J.: Freebase: a collaboratively created graph database for structuring human knowledge. In: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, pp. 1247\u20131250. ACM (2008)","DOI":"10.1145\/1376616.1376746"},{"key":"17_CR14","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, pp. 2787\u20132795 (2013)"},{"key":"17_CR15","doi-asserted-by":"crossref","unstructured":"Carlson, A., Betteridge, J., Kisiel, B., Settles, B., Hruschka Jr., E.R., Mitchell, T.M.: Toward an architecture for never-ending language learning. In: AAAI, vol. 5, p. 3 (2010)","DOI":"10.1609\/aaai.v24i1.7519"},{"issue":"3","key":"17_CR16","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1007\/BF02310791","volume":"35","author":"JD Carroll","year":"1970","unstructured":"Carroll, J.D., Chang, J.-J.: Analysis of individual differences in multidimensional scaling via an N-way generalization of \u201cEckart-Young\u201d decomposition. Psychometrika 35(3), 283\u2013319 (1970)","journal-title":"Psychometrika"},{"key":"17_CR17","first-page":"67","volume":"88","author":"TF Chan","year":"1987","unstructured":"Chan, T.F.: Rank revealing QR factorizations. Linear Algebra Appl. 88, 67\u201382 (1987)","journal-title":"Linear Algebra Appl."},{"key":"17_CR18","doi-asserted-by":"publisher","first-page":"20898","DOI":"10.1109\/ACCESS.2017.2759139","volume":"5","author":"L Chang","year":"2017","unstructured":"Chang, L., Zhu, M., Gu, T., Bin, C., Qian, J., Zhang, J.: Knowledge graph embedding by dynamic translation. IEEE Access 5, 20898\u201320907 (2017)","journal-title":"IEEE Access"},{"key":"17_CR19","doi-asserted-by":"publisher","DOI":"10.1002\/9780470747278","volume-title":"Nonnegative Matrix and Tensor Factorizations: Applications to Exploratory Multi-way Data Analysis and Blind Source Separation","author":"A Cichocki","year":"2009","unstructured":"Cichocki, A., Zdunek, R., Phan, A.H., Amari, S.: Nonnegative Matrix and Tensor Factorizations: Applications to Exploratory Multi-way Data Analysis and Blind Source Separation. Wiley, Hoboken (2009)"},{"issue":"4","key":"17_CR20","doi-asserted-by":"publisher","first-page":"1253","DOI":"10.1137\/S0895479896305696","volume":"21","author":"L De Lathauwer","year":"2000","unstructured":"De Lathauwer, L., De Moor, B., Vandewalle, J.: A multilinear singular value decomposition. SIAM J. Matrix Anal. Appl. 21(4), 1253\u20131278 (2000)","journal-title":"SIAM J. Matrix Anal. Appl."},{"issue":"3","key":"17_CR21","doi-asserted-by":"publisher","first-page":"1067","DOI":"10.1137\/070690730","volume":"30","author":"L De Lathauwer","year":"2008","unstructured":"De Lathauwer, L., Nion, D.: Decompositions of a higher-order tensor in block terms-part III: alternating least squares algorithms. SIAM J. Matrix Anal. Appl. 30(3), 1067\u20131083 (2008)","journal-title":"SIAM J. Matrix Anal. Appl."},{"key":"17_CR22","unstructured":"Diefenbach, D., Tanon, T., Singh, K., Maret, P.: Question answering benchmarks for Wikidata. In: ISWC 2017 (2017)"},{"key":"17_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1007\/11574620_14","volume-title":"The Semantic Web \u2013 ISWC 2005","author":"L Ding","year":"2005","unstructured":"Ding, L., Pan, R., Finin, T., Joshi, A., Peng, Y., Kolari, P.: Finding and ranking knowledge on the semantic web. In: Gil, Y., Motta, E., Benjamins, V.R., Musen, M.A. (eds.) ISWC 2005. LNCS, vol. 3729, pp. 156\u2013170. Springer, Heidelberg (2005). https:\/\/doi.org\/10.1007\/11574620_14"},{"issue":"2\u20133","key":"17_CR24","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1023\/A:1007413511361","volume":"29","author":"P Domingos","year":"1997","unstructured":"Domingos, P., Pazzani, M.: On the optimality of the simple bayesian classifier under zero-one loss. Mach. Learn. 29(2\u20133), 103\u2013130 (1997)","journal-title":"Mach. Learn."},{"key":"17_CR25","doi-asserted-by":"crossref","unstructured":"Dong, X., et al.: Knowledge vault: a web-scale approach to probabilistic knowledge fusion. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 601\u2013610. ACM (2014)","DOI":"10.1145\/2623330.2623623"},{"key":"17_CR26","unstructured":"Fabian, M.S., Gjergji, K., Gerhard, W., et al.: YAGO: a core of semantic knowledge unifying WordNet and Wikipedia. In: 16th International World Wide Web Conference, WWW, pp. 697\u2013706 (2007)"},{"key":"17_CR27","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1007\/978-3-642-04930-9_14","volume-title":"The Semantic Web - ISWC 2009","author":"T Franz","year":"2009","unstructured":"Franz, T., Schultz, A., Sizov, S., Staab, S.: TripleRank: ranking semantic web data by tensor decomposition. In: Bernstein, A., et al. (eds.) ISWC 2009. LNCS, vol. 5823, pp. 213\u2013228. Springer, Heidelberg (2009). https:\/\/doi.org\/10.1007\/978-3-642-04930-9_14"},{"issue":"5","key":"17_CR28","doi-asserted-by":"publisher","first-page":"403","DOI":"10.1007\/BF02163027","volume":"14","author":"GH Golub","year":"1970","unstructured":"Golub, G.H., Reinsch, C.: Singular value decomposition and least squares solutions. Numerische Math. 14(5), 403\u2013420 (1970)","journal-title":"Numerische Math."},{"issue":"4","key":"17_CR29","doi-asserted-by":"publisher","first-page":"848","DOI":"10.1137\/0917055","volume":"17","author":"M Gu","year":"1996","unstructured":"Gu, M., Eisenstat, S.C.: Efficient algorithms for computing a strong rank-revealing QR factorization. SIAM J. Sci. Comput. 17(4), 848\u2013869 (1996)","journal-title":"SIAM J. Sci. Comput."},{"key":"17_CR30","unstructured":"Harshman, R.A.: Foundations of the PARAFAC procedure: models and conditions for an \u201cexplanatory\u201d multimodal factor analysis (1970)"},{"key":"17_CR31","doi-asserted-by":"crossref","unstructured":"He, S., Liu, K., Ji, G., Zhao, J.: Learning to represent knowledge graphs with Gaussian embedding. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, pp. 623\u2013632. ACM (2015)","DOI":"10.1145\/2806416.2806502"},{"key":"17_CR32","doi-asserted-by":"crossref","unstructured":"Ji, G., He, S., Xu, L., Liu, K., Zhao, J.: Knowledge graph embedding via dynamic mapping matrix. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), vol. 1, pp. 687\u2013696 (2015)","DOI":"10.3115\/v1\/P15-1067"},{"key":"17_CR33","doi-asserted-by":"crossref","unstructured":"Kim, H., Park, H., Eld\u00e9n, L.: Non-negative tensor factorization based on alternating large-scale non-negativity-constrained least squares. In: Proceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2007, pp. 1147\u20131151. IEEE (2007)","DOI":"10.1109\/BIBE.2007.4375705"},{"issue":"3","key":"17_CR34","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1137\/07070111X","volume":"51","author":"TG Kolda","year":"2009","unstructured":"Kolda, T.G., Bader, B.W.: Tensor decompositions and applications. SIAM Rev. 51(3), 455\u2013500 (2009)","journal-title":"SIAM Rev."},{"issue":"6755","key":"17_CR35","doi-asserted-by":"publisher","first-page":"788","DOI":"10.1038\/44565","volume":"401","author":"DD Lee","year":"1999","unstructured":"Lee, D.D., Seung, H.S.: Learning the parts of objects by non-negative matrix factorization. Nature 401(6755), 788 (1999)","journal-title":"Nature"},{"issue":"2","key":"17_CR36","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":"17_CR37","doi-asserted-by":"crossref","unstructured":"Lin, Y., Liu, Z., Luan, H., Sun, M., Rao, S., Liu, S.: Modeling relation paths for representation learning of knowledge bases. arXiv preprint arXiv:1506.00379 (2015)","DOI":"10.18653\/v1\/D15-1082"},{"key":"17_CR38","doi-asserted-by":"crossref","unstructured":"Lin, Y., Liu, Z., Sun, M., Liu, Y., Zhu, X.: Learning entity and relation embeddings for knowledge graph completion. In: AAAI, pp. 2181\u20132187 (2015)","DOI":"10.1609\/aaai.v29i1.9491"},{"key":"17_CR39","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-00671-6_1","volume-title":"The Semantic Web \u2013 ISWC 2018","author":"C Meilicke","year":"2018","unstructured":"Meilicke, C., Fink, M., Wang, Y., Ruffinelli, D., Gemulla, R., Stuckenschmidt, H.: Fine-grained evaluation of rule- and embedding-based systems for knowledge graph completion. In: Vrande\u010di\u0107, D., et al. (eds.) ISWC 2018. LNCS, vol. 11136, pp. 3\u201320. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00671-6_1"},{"issue":"02","key":"17_CR40","doi-asserted-by":"publisher","first-page":"1760011","DOI":"10.1142\/S0218213017600119","volume":"26","author":"A Melo","year":"2017","unstructured":"Melo, A., V\u00f6lker, J., Paulheim, H.: Type prediction in noisy rdf knowledge bases using hierarchical multilabel classification with graph and latent features. Int. J. Artif. Intell. Tools 26(02), 1760011 (2017)","journal-title":"Int. J. Artif. Intell. Tools"},{"key":"17_CR41","doi-asserted-by":"crossref","unstructured":"Moniruzzaman, A.B.M., Nayak, R., Tang, M., Balasubramaniam, T.: Fine-grained type inference in knowledge graphs via probabilistic and tensor factorization methods. In: The World Wide Web Conference, pp. 3093\u20133100. ACM (2019)","DOI":"10.1145\/3308558.3313597"},{"issue":"1","key":"17_CR42","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1109\/JPROC.2015.2483592","volume":"104","author":"M Nickel","year":"2016","unstructured":"Nickel, M., Murphy, K., Tresp, V., Gabrilovich, E.: A review of relational machine learning for knowledge graphs. Proc. IEEE 104(1), 11\u201333 (2016)","journal-title":"Proc. IEEE"},{"key":"17_CR43","unstructured":"Nickel, M., Tresp, V., Kriegel, H.-P.: A three-way model for collective learning on multi-relational data. In: Proceedings of the 28th International Conference on Machine Learning (ICML-11), pp. 809\u2013816 (2011)"},{"key":"17_CR44","doi-asserted-by":"crossref","unstructured":"Nickel, M., Tresp, V., Kriegel, H.-P.: Factorizing YAGO: scalable machine learning for linked data. In: Proceedings of the 21st international conference on World Wide Web, pp. 271\u2013280. ACM (2012)","DOI":"10.1145\/2187836.2187874"},{"issue":"3","key":"17_CR45","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)","journal-title":"Semant. Web"},{"key":"17_CR46","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"510","DOI":"10.1007\/978-3-642-41335-3_32","volume-title":"The Semantic Web \u2013 ISWC 2013","author":"Heiko Paulheim","year":"2013","unstructured":"Paulheim, Heiko, Bizer, Christian: Type inference on noisy RDF data. In: Alani, H., et al. (eds.) ISWC 2013. LNCS, vol. 8218, pp. 510\u2013525. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-41335-3_32"},{"issue":"2","key":"17_CR47","doi-asserted-by":"publisher","first-page":"63","DOI":"10.4018\/ijswis.2014040104","volume":"10","author":"H Paulheim","year":"2014","unstructured":"Paulheim, H., Bizer, C.: Improving the quality of linked data using statistical distributions. Int. J. Semant. Web Inf. Syst. (IJSWIS) 10(2), 63\u201386 (2014)","journal-title":"Int. J. Semant. Web Inf. Syst. (IJSWIS)"},{"key":"17_CR48","doi-asserted-by":"crossref","unstructured":"Porrini, R., Palmonari, M., Cruz, I.F.: Facet annotation using reference knowledge bases. In: Proceedings of the 2018 World Wide Web Conference on World Wide Web, pp. 1215\u20131224. International World Wide Web Conferences Steering Committee (2018)","DOI":"10.1145\/3178876.3186020"},{"key":"17_CR49","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. ACM (2007)","DOI":"10.1145\/1242572.1242667"},{"key":"17_CR50","doi-asserted-by":"crossref","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, pp. 57\u201366 (2015)","DOI":"10.18653\/v1\/W15-4007"},{"key":"17_CR51","doi-asserted-by":"crossref","unstructured":"Toutanova, K., Chen, D., Pantel, P., Poon, H., Choudhury, P., Gamon, M.: Representing text for joint embedding of text and knowledge bases. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 1499\u20131509 (2015)","DOI":"10.18653\/v1\/D15-1174"},{"issue":"12","key":"17_CR52","doi-asserted-by":"publisher","first-page":"2724","DOI":"10.1109\/TKDE.2017.2754499","volume":"29","author":"Q Wang","year":"2017","unstructured":"Wang, Q., Mao, Z., Wang, B., Guo, L.: Knowledge graph embedding: a survey of approaches and applications. IEEE Trans. Knowl. Data Eng. 29(12), 2724\u20132743 (2017)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"17_CR53","doi-asserted-by":"crossref","unstructured":"Wang, Z., Zhang, J., Feng, J., Chen, Z.: Knowledge graph and text jointly embedding. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1591\u20131601 (2014)","DOI":"10.3115\/v1\/D14-1167"},{"key":"17_CR54","doi-asserted-by":"crossref","unstructured":"Xiao, H., Huang, M., Zhu, X.: TransG: a generative model for knowledge graph embedding. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), vol. 1, pp. 2316\u20132325 (2016)","DOI":"10.18653\/v1\/P16-1219"},{"issue":"2","key":"17_CR55","first-page":"3","volume":"1","author":"H Zhang","year":"2004","unstructured":"Zhang, H.: The optimality of naive bayes. AA 1(2), 3 (2004)","journal-title":"AA"},{"key":"17_CR56","doi-asserted-by":"crossref","unstructured":"Zhang, J., Lu, C.-T., Cao, B., Chang, Y., Philip, S.Y.: Connecting emerging relationships from news via tensor factorization. In: 2017 IEEE International Conference on Big Data (Big Data), pp. 1223\u20131232. IEEE (2017)","DOI":"10.1109\/BigData.2017.8258048"},{"key":"17_CR57","doi-asserted-by":"crossref","unstructured":"Zupanc, K.: Davis, J.: Estimating rule quality for knowledge base completion with the relationship between coverage assumption. In: Proceedings of the Web Conference 2018, pp. 1\u20139 (2018)","DOI":"10.1145\/3178876.3186006"}],"container-title":["Lecture Notes in Computer Science","Databases Theory and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-69377-0_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,16]],"date-time":"2022-12-16T06:30:36Z","timestamp":1671172236000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-69377-0_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030693763","9783030693770"],"references-count":57,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-69377-0_17","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"10 February 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ADC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australasian Database Conference","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Dunedin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"New Zealand","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 January 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 February 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"32","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"adc2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/adc2021.github.io\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-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":"21","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":"16","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":"76% - 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":"2","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}