TY - GEN
T1 - OntoCOVID
T2 - 15th International Conference on Information Technology and Applications 2021
AU - Ali, Shaukat
AU - Khusro, Shah
AU - Anwar, Sajid
AU - Ullah, Abrar
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2022/4/21
Y1 - 2022/4/21
N2 - Several COVID19 statistical datasets are provided to support stakeholders for better planning and decision making in healthcare. However, the datasets are in heterogeneous proprietary formats that create data silos and compatibility issues and make data discovery and reuse difficult. Further, the data integration for analysis is difficult and is performed by the domain experts manually which is time consuming and error prone. Therefore, an explicit, flexible, and widely acceptable methodology is needed to represent, store, query, and visualize COVID19 statistical data in the datasets. In this paper, we have presented the design and development of OntoCOVID ontology for representing, organizing, sharing, and reusing COVID19 statistical data in the datasets. The OntoCOVID is a lightweight ontology providing definitions of classes, properties, and axioms to semantically represent and relate information in the COVID19 statistical datasets. The OntoCOVID is evaluated to demonstrate its completeness and information retrieval for different use-case scenarios. The results obtained are promising and advocate for the improved ontological design and applications of the OntoCOVID.
AB - Several COVID19 statistical datasets are provided to support stakeholders for better planning and decision making in healthcare. However, the datasets are in heterogeneous proprietary formats that create data silos and compatibility issues and make data discovery and reuse difficult. Further, the data integration for analysis is difficult and is performed by the domain experts manually which is time consuming and error prone. Therefore, an explicit, flexible, and widely acceptable methodology is needed to represent, store, query, and visualize COVID19 statistical data in the datasets. In this paper, we have presented the design and development of OntoCOVID ontology for representing, organizing, sharing, and reusing COVID19 statistical data in the datasets. The OntoCOVID is a lightweight ontology providing definitions of classes, properties, and axioms to semantically represent and relate information in the COVID19 statistical datasets. The OntoCOVID is evaluated to demonstrate its completeness and information retrieval for different use-case scenarios. The results obtained are promising and advocate for the improved ontological design and applications of the OntoCOVID.
KW - COVID19
KW - Dataset
KW - Ontology
KW - OWL
KW - Semantic web
KW - SPARQL
UR - http://www.scopus.com/inward/record.url?scp=85129301311&partnerID=8YFLogxK
U2 - 10.1007/978-981-16-7618-5_16
DO - 10.1007/978-981-16-7618-5_16
M3 - Conference contribution
AN - SCOPUS:85129301311
SN - 9789811676178
T3 - Lecture Notes in Networks and Systems
SP - 183
EP - 194
BT - Proceedings of International Conference on Information Technology and Applications. ICITA 2021
A2 - Ullah, Abrar
A2 - Gill, Steve
A2 - Rocha, Álvaro
A2 - Anwar, Sajid
PB - Springer
Y2 - 13 November 2021 through 14 November 2021
ER -