Affiliation:
1. Department of Computer Applications, National Institute of Technology, Kurukshetra, Haryana 136119, India
Abstract
Background:
The acquisition and exchange of meaningful, integrated, and accurate
information are at the forefront of the combat against COVID-19; still, there are many countries
whose health systems are disrupted. Moreover, no one is adequately equipped for COVID-19
contingencies. Many organizations have established static information systems to manage the information.
This fact presents numerous issues, including delays, inconsistencies, and inaccuracies
in COVID-19 information collected for pandemic control and monitoring.
Objective:
This paper presents a semantic representation of COVID-19 data, a domain ontology
to facilitate measurement, clarification, linking, and sharing. We automatically generate a computer-
intelligible knowledge base from COVID-19 case information, which contains machineunderstandable
information. Furthermore, we have anticipated an ontology population algorithm
from tabular data that delivers interoperable, consistent, and accurate content with COVID-19 information.
Methods:
We utilized the tabula package to extract the tables from PDF files and user NLP libraries
to sort and rearrange tables. The proposed algorithm was then applied to all instances to
automatically add to the input ontology using the Owlready Python module. Moreover, to evaluate
the performance, SPARQL queries were used to retrieve answers to competency questions.
Results:
When there is an equivalence relationship, the suggested algorithm consistently finds the
right alignments and performs at its best or very close to it in terms of precision. Moreover, a
demonstration of algorithm performance and a case study on COVID-19 data to information
management and visualization of the populated data are also presented.
Conclusion:
This paper presents an ontology learning/matching tool for ontology and populating
instances automatically to ontology by emphasizing the importance of a unit's distinguishing features
by unit matching.
Publisher
Bentham Science Publishers Ltd.
Subject
General Materials Science
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