Construct a Knowledge Graph for China Coronavirus (COVID-19) Patient Information Tracking

References

• Kong D, Yang J, Li L. Early identification of technological convergence in numerical control machine tool: a deep learning approach. Scientometrics. 2020;125:1983–2009. doi:10.1007/s11192-020-03696-y
   Web of Science ®Google Scholar
• Chen X, Liu J, Xu L, et al. Knowledge graph of COVID-19 patient activity-a case study of Zhengzhou city. J Wuhan Univ. 2020;45:816–824.
   Google Scholar
• Li Q, Guan X, Wu P, et al. Early transmission dynamics in Wuhan, China, of novel coronavirus–infected pneumonia. N Engl J Med. 2020;382:1199–1207. doi:10.1056/NEJMoa2001316
   PubMed Web of Science ®Google Scholar
• Chan J, Yuan S, Kok KH, et al. A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: a study of a family cluster. Lancet. 2020;395:514–523. doi:10.1016/S0140-6736(20)30154-9
   PubMed Web of Science ®Google Scholar
• World Health Organization. Statement on the second meeting of the International Health Regulations Emergency Committee regarding the outbreak of novel coronavirus (2019-nCoV). Available from: https://www.who.int/news-room/detail/30-01-2020-statement-on-the-secondmeeting-of-the-international-health-regulations-(2005)-emergency-committeeregarding-the-outbreak-of-novel-coronavirus-(2019-ncov). Accessed January 30, 2020.
   Google Scholar
• Confirmed coronavirus cases and deaths by country and territory. Available from: https://virusncov.com. Accessed May 12, 2020.
   Google Scholar
• Simonsen L, Gog JR, Olson D, et al. Infectious disease surveillance in the big data era: towards faster and locally relevant systems. J Infect Dis. 2016;214(suppl 4):380–385. doi:10.1093/infdis/jiw376
   Web of Science ®Google Scholar
• Houlihan CF, Whitworth JA. Outbreak science: recent progress in the detection and response to outbreaks of infectious diseases. Clin Med. 2019;19:140–144. doi:10.7861/clinmedicine.19-2-140
   PubMed Web of Science ®Google Scholar
• Abidi SS, Abidi SR. Intelligent health data analytics: a convergence of artificial intelligence and big data. Healthc Manage Forum. 2019;32:178–182. doi:10.1177/0840470419846134
   PubMedGoogle Scholar
• Dong X, Boulton ML, Carlson B, et al. Syndromic surveillance for influenza in Tianjin. J Public Health. 2017;39:274–281.
   Google Scholar
• Bansal S, Chowell G, Simonsen L, Vespignani A, Viboud C. Big data for infectious disease surveillance and modeling. J Infect Dis. 2016;214:375–379. doi:10.1093/infdis/jiw400
   PubMed Web of Science ®Google Scholar
• Salathé M. Digital pharmacovigilance and disease surveillance: combining traditional and big-data systems for better public health. J Infect Dis. 2016;214:399–403. doi:10.1093/infdis/jiw281
   Web of Science ®Google Scholar
• Sun Z, Yang J, Zhang J et al. Recurrent knowledge graph embedding for effective recommendation. Proceedings of the 12th ACM Conference on Recommender Systems. 2018:297–300.
   Google Scholar
• Wang X, He X, Cao Y et al. Kgat: knowledge graph attention network for recommendation. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2019:950–958.
   Google Scholar
• Pujara J, Miao H, Getoor L, et al. Knowledge Graph Identification. Vol. 8218. Berlin, Heidelberg: Springer; 2013:542–557.
   Google Scholar
• Vo N, Guerreiro M, Yaparla A, et al. Class A scavenger receptors are used by Frog Virus 3 during its cellular entry. Viruses. 2019;11:2–11. doi:10.3390/v11020093
   Web of Science ®Google Scholar
• Uhlen M, Oksvold P, Fagerberg L, et al. Towards a knowledge-based Human Protein Atlas. Nat Biotechnol. 2010;28:1248–1250. doi:10.1038/nbt1210-1248
   PubMed Web of Science ®Google Scholar
• Ming Z, Yaru D. Ontology non-classification relationship extraction method in the construction of plant domain knowledge map. J Agric Machin. 2016;47:278–284.
   Google Scholar
• Jun C, Wanzeng L, Hao W, et al. BasicProblems and study directions of basic geographic knowledge service. Geomat Inf Sci Wuhan Univ. 2019;44:38–47.
   Google Scholar
• Clare D. Reading geography between the lines: extracting local place knowledge from text. Spat Inf Theory. 2013;81:320–337.
   Google Scholar
• Li Y, Feng L, Xiliang L. A bootstrapping based approach for open geo-entity relation extraction. Acta Geod Cartogr Sin. 2016;45:616–622.
   Google Scholar
• Hongwei D, Lingkui M, Changqing H, et al. A method for geo semantic spatial index on SPARQL query. Acta Geod Cartogr Sin. 2014;43:193–199.
   Google Scholar
• Gür N, Pedersen TB, Zimányi E, et al. A foundation for spatial data warehouses on the semantic web. Semantic Web. 2018;9:557–587. doi:10.3233/SW-170281
   Web of Science ®Google Scholar
• Feng L, Li Y, Peiyuan Q. On geographic knowledge graph. J Geo Inf Sci. 2017;19:723–734.
   Google Scholar
• Chen J, Deng S, Chen H. Crowd-GeoKG: Crowdsourced Geo-Knowledge Graph. In: CCKS 2017:Knowledge Graph and Semantic Computing,Language,Knowledge and Intelligence. Chengdu,China: Springer; 2017:165–172.
   Google Scholar
• Shu W, Xueying Z, Peng Y, et al. Geo-graphic knowledge graph (GeoKG): a formalized geographic knowledge representation. Int J Geo-Inf. 2019;8:184–185.
   Web of Science ®Google Scholar
• Qiang C, Jia C, Jian C, et al. Multi-level attention mechanism based distant supervision for relation extraction. J Chin Inf Process. 2018;32:96–106.
   Google Scholar
• Wan J, Chen B, Wang S, Xia M, Li D, Liu C. Fog computing for energy-aware load balancing and scheduling in smart factory. IEEE Trans Ind Inform. 2018;14:4548–4556. doi:10.1109/TII.2018.2818932
   Web of Science ®Google Scholar
• Zeng D, Liu K, Lai S, et al. Relation classification via convolutional deep neural network: the 25th International Conference on Computational Linguistics: Technical Papers, Dublin, Ireland, 2014. Dublin City University and Association for Computational Linguistics; 2014.
   Google Scholar
• Zhou P, Shi W, Tian J, et al. Attention-based bidirectional long short-term memory networks for relation classification. Proceedings of the 54th annual meeting of the association for computational linguistics, Berlin, Germany, 2016. Association for Computational Linguistics; 2016.
   Google Scholar
• Zeng D, Liu K, Chen Y, et al. Distant supervision for relation extraction via piecewise convolutional neural networks. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, Lisbon, Portugal, 2015. Association for Computational Linguistics; 2015.
   Google Scholar
• Jiang X, Wang Q, Li P, et al. Relation extraction with multi-instance multi-label convolutional neural networks. Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, Osaka, Japan, 2016. The COLING 2016 Organizing Committee; 2016.
   Google Scholar
• Feng J,Huang M,Zhao L, et al. Reinforcement learning for relation classification from noisy dat. Thirty-Second AAAI Conference on Artificial Intelligence, New Orleans, USA, 2018. PKP Publishing Services Network; 2018.
   Google Scholar
• Woźniak M, Graña M, et al. A survey of multiple classifier systems as hybrid systems. Information Fusion. 2014;16:3–17.
   Google Scholar