https://doi.org/10.4081/gh.2022.1171
Does the Geohealth domain require a body of knowledge?
Published: 29 November 2022
Abstract Views: 1381
PDF: 531
HTML: 21
HTML: 21
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Authors
To answer the question if the Geohealth domain requires a body of knowledge (BoK), we need a general understanding of concepts associated with this field. Two years ago, the United Nation (UN) committee of experts on global geospatial information management identified “semantic- and ontology-linked data” as something that “will become essential to support the next generation of autonomous systems” (UN-GGIM, 2020). The term ontology is closely related to BoK. Unlike data models, ontologies are independent of application; they are generic, can be used in different ways and have clear advantages but they are challenging to create and even more difficult to maintain.
The brief description below summarizes what ontologies are, why they are needed to support linked data, what the role of the semantic web is, what is already going on within the Geohealth domain on ontologies and how a BoK can assist.
Abburu S, 2019. Ontology Driven Cross-Linked Domain Data Integration and Spatial Semantic Multi Criteria Query System for Geospatial Public Health: Concepts, Methodologies, Tools, and Applications. In I. Management Association (Eds.), Geospatial Intelligence: Concepts, Methodologies, Tools, and ApplicationsIGI Global. pp. 714-743.
DOI: https://doi.org/10.4018/978-1-5225-8054-6.ch032
Abdelghany A, Ramadan N, Hefni H, 2019. An agile methodology for ontology development. Int J Intell EngSyst 12:170-181.
DOI: https://doi.org/10.22266/ijies2019.0430.17
Bittner T, Donnelly M, Smith B, 2009. A spatio-temporal ontology for geographic information integration. Int J Geogr Inf Sci 23;765-98.
DOI: https://doi.org/10.1080/13658810701776767
de Boer M, Verhoosel JPC, 2020. Towards Data-driven Ontologies: a Filtering Approach using Keywords and Natural Language Constructs. Proceedings of the Twelfth Conference on Language Resources and Evaluation (11–16 May 2020).European Language Resources Association, Marseille, France. Accessed on 11.12.2022. Available from: https://aclanthology.org/2020.lrec-1.278
Dibase D, 2007. Is GIS a wamperter? Transactions in GIS 11:1-8.
DOI: https://doi.org/10.1111/j.1467-9671.2007.01029.x
Gao S, Anton F, Mioc D, Boley H, 2012. Non-spatial and geospatial semantic query of health information. Int Arc Photogramm, Remote Sens Spat Inf Sci XXXIX-B2:167-72.
DOI: https://doi.org/10.5194/isprsarchives-XXXIX-B2-167-2012
Gür N, Sanchez LD, Kauppinen T, 2012. GI systems for Public Health with an Ontology Based Approach. Proceedings of the AGILE'2012 International Conference on Geographic Information Science, Avignon, April, 24-27, 2012.In Multidisciplinary Research on Geographical Information in Europe and Beyond.Eds: Jérôme Gensel, Didier Josselin and Danny Vandenbroucke.Agile, Avignon, France. Accessed on 11.12.2022. Available from: https://agile-online.org/images/conferences/2012/documents/proceedings/papers/paper_guer_gi_systems_for_public_health_with_an_ontology_based_approach_2012.pdf
Krauskopf E, 2018. A bibiliometric analysis of the Journal of Infection and Public Health: 2008–2016. J Infect Public Health 11:224-9.
DOI: https://doi.org/10.1016/j.jiph.2017.12.011
Lemmens R, Lang S, Albrecht F, Augustijn E, Granell C, Augustijn E-W, Pathe C, Missoni-Steinbacher E-M, Monfort Muriach A, 2022. Integrating concepts of artificial intelligence in the eo4geo body of knowledge. Int. Arch. Photogramm. Remote Sens Spat Inf Sci XLIII-B4-2022:53-59.
DOI: https://doi.org/10.5194/isprs-archives-XLIII-B4-2022-53-2022
Mao X, Li Q, Zhang Z, Zhu Q, 2009. Application of Spatial Information Search Engine Based on Ontology in Public Health Emergence. Proceedings of the Third International Conference on Bioinformatics and Biomedical Engineering, Beijing, China 11-13 June 2009. IEEE Engineering in Medicine and Biology Society 2009. Accessed on 11.12.2022.Available from: https://ieeexplore.ieee.org/document/5162396
DOI: https://doi.org/10.1109/ICBBE.2009.5162396
Oliver GR, 2012. Foundations of the assumed business operations and strategy body of knowledge (bosbok) : An outline of shareable knowledge. Sydney University Press.
Ong E, He Y, 2016. Community-based Ontology Development, Annotation and Discussion with MediaWiki extension Ontokiwi and Ontokiwi-based Ontobedia. AMIA Jt Summits Transl Sci Proc 65-74.
Poggi A, Lembo D, Calvanese D, De Giacomo G, Lenzerini M, Rosati R, 2008. Linking data to ontologies. In Lecture Notes in Computer Science, Spaccapietra, S. (ed) vol 4900. Springer, Berlin, Heidelberg. J Data Semant X. Doi: 10.1007/978-3-540-77688-8_5
DOI: https://doi.org/10.1007/978-3-540-77688-8_5
Rezaei Z, Vahidnia MH, 2022. Effective medical center finding during COVID-19 pandemic using a spatial DSS centered on ontology engineering. GeoJournal, 11 pp. Doi: 10.1007/s10708-022-10777-3
DOI: https://doi.org/10.1007/s10708-022-10777-3
Roantree M, O’Donoghue J, O’Kelly N, Pierce M, Irving K, Van Boxtel M, Köhler S, 2016. Mapping longitudinal studies to risk factors in an ontology for dementia. Health Informatics J 22;414-426.
DOI: https://doi.org/10.1177/1460458214564092
Rospocher M, Tonelli S, Serafini L, Pianta E2012. Key-Concept Extraction for Ontology Engineering. Knowledge Engineering and Knowledge Management. EKAW 2012. Lecture Notes in Computer Science, vol 7603. Springer, Berlin, Heidelberg. Doi: 10.1007/978-3-642-33876-2_36
DOI: https://doi.org/10.1007/978-3-642-33876-2_36
Somodevilla MJ, Perez de Celis Herrero C, Hernández JA, Peneda Torres, I. H, 2013. Ontology Based Framework to Represent Relationships between Biomedical Spatial Data. The Twelfth Mexican International Conference on Artificial Intelligence, pp. 257-261.
DOI: https://doi.org/10.1109/MICAI.2013.37
UN-GGIM, 2020. p. 36. in Future trends in geospatial information management: the five to ten year vision. United Nations,Department of Economic and Social AffairsStatistics Division24-30 Nov. 2013. Accessed on 11.12.2022. Available from: https://ggim.un.org/
Utzinger J, Rinaldi L, Malone JB, Krauth SJ,Kristensen TK, Cringoli G, Bergquist R, 2011. Geospatial Health: the first five years. Geospat Health 6:137–54.
DOI: https://doi.org/10.4081/gh.2011.166
Zimeras S, 2012. Bayesian Ontologies in Spatial Integrating Medical Information Systems. pp. 220-232 in Quality Assurance in Healthcare Service Delivery, Nursing and Personalized Medicine: Technologies and Processes. A. Lazakidou & A. Daskalaki Eds. IGI Global.
DOI: https://doi.org/10.4018/978-1-61350-120-7.ch013
How to Cite
Augustijn-Beckers, E.-W., Amer, S., Lemmens, R., & Bergquist, R. (2022). Does the Geohealth domain require a body of knowledge?. Geospatial Health, 17(2). https://doi.org/10.4081/gh.2022.1171
Copyright (c) 2022 the Author(s)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
PAGEPress has chosen to apply the Creative Commons Attribution NonCommercial 4.0 International License (CC BY-NC 4.0) to all manuscripts to be published.
Similar Articles
- Maria Buxò-Pujolrás, José Miguel Martínez, Yutaka Yasui, Carme Borrell, Juan Carlos Martín, Montserrat Vergara, Marta Cervantes, Joan Benach, Monitoring the cluster of high-risk mortality areas in the southwest of Spain , Geospatial Health: Vol. 7 No. 1 (2012)
- Micaela Natalia Campero, Carlos Matías Scavuzzo, Veronica Andreo, María Sol Mileo, Micaela Belén Franzois, María Georgina Oberto, Carla Gonzalez Rodriguez, María Daniela Defagó, A geospatial analysis of cardiometabolic diseases and their risk factors considering environmental features in a midsized city in Argentina , Geospatial Health: Vol. 18 No. 2 (2023)
- Kamukama Ismail, Gilbert Maiga, Denis Ssebuggwawo, Peter Nabende, Ali Mansourian, Spatio-temporal trends and distribution patterns of typhoid disease in Uganda from 2012 to 2017 , Geospatial Health: Vol. 15 No. 2 (2020)
- Ulrik B. Pedersen, Dimitrios-Alexios Karagiannis-Voules, Nicholas Midzi, Tkafira Mduluza, Samson Mukaratirwa, Rasmus Fensholt, Birgitte J. Vennervald, Thomas K. Kristensen, Penelope Vounatsou, Anna-Sofie Stensgaard, Comparison of the spatial patterns of schistosomiasis in Zimbabwe at two points in time, spaced twenty-nine years apart: is climate variability of importance? , Geospatial Health: Vol. 12 No. 1 (2017)
- Emily Phaboutdy, Michael Ward, Investigation of landscape risk factors for the recent spread of varroa mite (Varroa destructor) in European honeybee (Apis mellifera) colonies in New South Wales, Australia , Geospatial Health: Vol. 19 No. 1 (2024)
- Isabelle Lebert, Séverine Bord, Christine Saint-Andrieux, Eva Cassar, Patrick Gasqui, Frédéric Beugnet, Karine Chalvet-Monfray, Sophie O. Vanwambeke, Gwenaël Vourc’h, Magalie René-Martellet, Habitat suitability map of Ixodes ricinus tick in France using multi-criteria analysis , Geospatial Health: Vol. 17 No. 1 (2022)
- Zhao Rong Huang, Miao Ge, Xin Rui Pang, Pu Song, Congxia Wang, The spatial distribution of interleukin-4 (IL-4) reference values in China based on a back propagation (BP) neural network , Geospatial Health: Vol. 18 No. 2 (2023)
- Bernada E. Sianga, Maurice C. Mbago, Amina S. Msengwa, The distribution of cardiovascular diseases in Tanzania: a spatio-temporal investigation , Geospatial Health: Vol. 19 No. 2 (2024)
- Kelly A. Patyk, Mary J. McCool-Eye, David D. South, Christopher L. Burdett, Susan A. Maroney, Andrew Fox, Grace Kuiper, Sheryl Magzamen, Modelling the domestic poultry population in the United States: A novel approach leveraging remote sensing and synthetic data methods , Geospatial Health: Vol. 15 No. 2 (2020)
- Michael T. Gebreslasie, Ides Bauwens, MALAREO: a user-driven project , Geospatial Health: Vol. 10 No. 2 (2015)
<< < 1 2 3 4 5 6 7 8 9 10 > >>
You may also start an advanced similarity search for this article.
