medigraphic.com
ISSN 2307-2113 (Electronic)
Revista Cubana de Información en Ciencias de la Salud (ACIMED)

2021, Number 4

<< Back Next >>

Revista Cubana de Información en Ciencias de la Salud (ACIMED) 2021; 32 (4)

Knowledge graphs to manage epidemiological information about COVID-19

Delgado FT, Stuart CML, Delgado FM

Language: Spanish
References: 29
Page: 1-23
PDF size: 519.00 Kb.


ABSTRACT

Control of the spread of infectious diseases requires exhaustive epidemiological research, as has been validated by the performance of the Ministry of Public Health during several decades of combat against numerous diseases, such as dengue, cholera and various types of influenza, among others. However, the COVID-19 pandemic is testing the limits of the most rigorous epidemiological protocols in Cuba and worldwide, due to its high transmissibility and fast spread. In this context, the present study had the purpose of using knowledge graphs to support epidemiological research about COVID-19, with greater emphasis on exposure factors and contact tracing. To achieve this end, a study was conducted about the state of the art of knowledge graphs and their use in the health care sector, particularly in the combat against the novel coronavirus SARS-CoV-2. The research applied a methodological approach based on the development and use of knowledge graphs adjusted to the study field. Results are simulated in the context of the outbreak occurring in mid July 2020 in the municipality of Bauta, Artemisa province, using real data obtained from the Internet and combined with other simulated data.


REFERENCES

  1. Delgado-Fernández T. Taxonomía de transformación digital. Rev CubanaTransform Dig. 2020 [12/08/2020];1(1):4-23. Disponible en:https://rctd.uic.cu/rctd/article/view/62

  2. Noy N, Gao Y, Jain A, Narayanan A, Patterson A, Taylor J. Industry-scaleknowledge graphs: lessons and challenges. Queue. 2019;17(2):48-75. DOI:https://doi.org/10.1145/3331166

  3. Paulheim H. Knowledge graph refinement: A survey of approaches andevaluation methods. Sem Web. 2017 [12/08/2020];8(3):489-508. Disponible en:http://www.semantic-web-journal.net/system/files/swj1167.pdf

  4. Dirschl C, Kent J, Schram J, Reul Q. Enabling Digital Business Transformationthrough an enterprise Knowledge Graph. ESWC - Industry_Track; 2020[12/08/2020]. Disponible en: https://preprints.2020.eswcconferences.org/industry_track/paper_277.pdf

  5. Heist N, Hertling S, Ringler D, Paulheim H. Knowledge Graphs on the Web-anOverview. arXiv preprint arXiv:2003.00719; 2020 [12/08/2020]. Disponible en:https://arxiv.org/abs/2003.00719

  6. Ehrlinger L, Wöß W. Towards a Definition of Knowledge Graphs. SEMANTiCS;2016;48:1-4. Disponible en: https://www.semanticscholar.org/paper

  7. Saorín T. Grafos de conocimiento y bases de datos en grafo: conceptosfundamentales a partir de una" obra maestra" del Museo del Prado. Anuario ThinkEPI; 2019.

  8. Hogan A, Blomqvist E, Cochez M, d'Amato C, de Melo G, Gutiérrez C, Gayo JE,Kirrane S, Neumaier S, Polleres A, Navigli R. Knowledge graphs. arXiv Preprint;2020 [12/08/2020]. Disponible en: https://arxiv.org/abs/2003.02320

  9. Lehmann J, Isele R, Jakob M, Jentzsch A, Kontokostas D, Mendes PN, HellmannS, Morsey M, Van Kleef P, Auer S, Bizer C. DBpedia–a large-scale, multilingualknowledge base extracted from Wikipedia. Sem Web; 2015[12/08/2020];6(2):167-95. Disponible en:https://content.iospress.com/articles/semantic-web/sw134

  10. Kondreddi SK, Triantafillou P, Weikum G. Combining information extractionand human computing for crowdsourced knowledge acquisition. IEEE 30thInternational Conference on Data Engineering; 2014 [12/08/2020]. pp. 988-99.Disponible en: https://ieeexplore.ieee.org/abstract/document/6816717/

  11. Grainger T, AlJadda K, Korayem M, Smith A. The Semantic Knowledge Graph:A compact, auto-generated model for real-time traversal and ranking of anyrelationship within a domain. IEEE International Conference on Data Science andAdvanced Analytics (DSAA); 2016.

  12. Michel F, Gandon F, Ah-Kane V, Bobasheva A, Cabrio E, Corby O, Gazzotti R,et al. Covid-on-the-Web: Knowledge graph and services to advance COVID-19research. International Semantic Web Conference; 2020.

  13. Mohamed A, Abuoda G, Ghanem A, Kaoudi Z, Aboulnaga A. RDF Frames:Knowledge Graph Access for Machine Learning Tools. arXiv:2002.03614v1; 2020.

  14. Zou Y, Liu Y. The Implementation Knowledge Graph of Air Crash Data basedon Neo4j. IEEE 4th Information Technology, Networking, Electronic andAutomation Control Conference (ITNEC); 2020.

  15. Tejero A, Rodríguez-Doncel V, Pau I. Knowledge Graphs for InnovationEcosystems. arXiv preprint; 2020 [12/08/2020]. Disponible en:https://arxiv.org/abs/2001.08615

  16. Stuart-Cárdenas ML, Delgado-Fernández T, Delgado-Fernández M, Piedra Y.Datos empresariales enlazados: Revisión sistemática desde una perspectivaorganizacional. ALCANCE; 2020 [12/08/2020];9:23. Disponible en:http://scielo.sld.cu/scielo.php?script=sci_arttext&pid=S2411-99702020000200153

  17. Galkin M, Auer S, Vidal ME, Scerri S. Enterprise Knowledge Graphs: ASemantic Approach for Knowledge Management in the Next Generation ofEnterprise Information Systems. ICEIS; 2017 [18/04/2017]:88-98. Disponible en:https://www.scitepress.org/Papers/2017/63252/63252.pdf

  18. Bader SR, Grangel-González I, Nanjappa P, Vidal ME, Maleshkova M. AKnowledge Graph for Industry 4.0. European Semantic Web Conference;2020:465-80.

  19. Rotmensch M, Halpern Y, Tlimat A, Horng S, Sontag D. Learning a healthknowledge graph from electronic medical records. Scient Rep. 2017;7(1):5994.DOI: https://doi.org/10.1038/s41598-017-05778-z

  20. Gyrard A, Gaur M, Shekarpour S, Thirunarayan K, Sheth A. Personalizedhealth knowledge graph. Core Scholar Publications; 2018 [acceso: 28/07/2020].Disponible en: https://corescholar.libraries.wright.edu/

  21. Yu T, Li J, Yu Q, Tian Y, Shun X, Xu L, Zhu L, Gao H. Knowledge graph forTCM health preservation: design, construction, and applications. ArtificialIntelligence in Medicine. 2017;77:48-52. DOI:https://doi.org/10.1016/j.artmed.2017.04.001

  22. Domingo-Fernández D, Baksi S, Schultz B, Gadiya Y, Karki R, Raschka T, et al.COVID-19 Knowledge Graph: a computable, multi-modal, cause-and-effectknowledge model of COVID-19 pathophysiology. BioRxiv. 2020. DOI: https://doi.org/10.1101/2020.04.14.040667v1.full-text

  23. Wang Q, Li M, Wang X, Parulian N, Han G, Ma J, et al. COVID-19 LiteratureKnowledge Graph Construction and Drug Repurposing Report Generation. arXivPreprint; 2007 [acceso: 01/07/2020]. Disponible en:https://arxiv.org/abs/2007.00576

  24. Wise C, Ioannidis VN, Calvo MR, Song X, Price G, Kulkarni N, et al. COVID-19Knowledge Graph: Accelerating Information Retrieval and Discovery for ScientificLiterature. arXiv Preprint; 2007 [acceso: 24/07/2020]. Disponible en:https://arxiv.org/abs/2007.12731

  25. Chen C, Ebeid IA, Bu Y, Ding Y. Coronavirus Knowledge Graph: A Case Study.arXiv Preprint; 2007 [acceso: 04/07/2020]. Disponible en:https://arxiv.org/abs/2007.10287

  26. Ilievski F, Garijo D, Chalupsky H, Divvala NT, Yao Y, Rogers C, et al. KGTK: AToolkit for Large Knowledge Graph Manipulation and Analysis. arXiv Preprint;2006 [acceso: 29/05/2020]. Disponible en: https://arxiv.org/abs/2006.00088

  27. Chen WJ, Yang SY, Chang JC, Cheng WC, Lu TP, Wang YN, et al. Developmentof a semi-structured, multifaceted, computer-aided questionnaire for outbreakinvestigation: e-Outbreak Platform. Biomed J. 2020 [acceso: 20/06/2020].Disponible en:https://www.sciencedirect.com/science/article/pii/S2319417020300949

  28. Arruda N, Venceslau AD, da Cruz MM, Vidal VM, Pequeno VM. Publishing andConsuming Semantic Views for Construction of Knowledge Graphs. InICEIS; 2020[acceso: 20/06/2020];1:197-204. Disponible en:https://www.semanticscholar.org/paper/Publishing-and-Consuming-Semantic-Views-for-of-Arruda-Venceslau/3ddf6802b3eb40eac320ff34656ec23985166b40

  29. Claveau V, L’Homme MC. Discovering specific semantic relationships betweennouns and verbs in a specialized French corpus. 3rd International Workshop onComputational Terminology; 2004.




2020     |     www.medigraphic.com