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Conceptualization in ontology based on Peirce’s triadic model of the sign: a qualitative content analysis

Khadije Moradi (Department of Information Science, Alzahra University, Tehran, Iran)

Information Discovery and Delivery

ISSN: 2398-6247

Article publication date: 6 June 2022

Issue publication date: 6 January 2023




This study aims to identify the effective components in object interpretation based on Pierce’s triadic sign model to determine how meaning is created and identify the components affecting conceptualization in ontology.


A qualitative approach was adopted, and 6320 comments posted on the Instagram pages of five groups forming the research population were analyzed by qualitative content analysis.


Concepts are dynamic and context-oriented. Over time, a person’s concepts and mental patterns about an object are altered or eliminated. Social, political and economic conditions, governance, technology, interactions, language, environment, culture and social habits contribute to object interpretation and meaning creation.

Practical implications

In designing ontologies, especially in a context-dependent field, attention should be paid to users’ mental preconceptions, mental patterns, context and personal characteristics. Instead of using words, researchers can use visual and multimedia signs. In addition to logic, statistics and computer science, personal and anthropological characteristics should be considered in the ontology design.


This study dealt with conceptualization, which is a principal component of ontology. Using Pierce’s triadic model sign and unstructured Instagram data, ontology was studied from a user and pragmatic viewpoint, independently of technical aspects.



The author would like to express his special thanks of gratitude to his Supervisors Dr Amir Ghaebi and Dr Masoumeh Karbala Aghaee Kamran who gave me the opportunity to do this research and for my guidance on the topic.


Moradi, K. (2023), "Conceptualization in ontology based on Peirce’s triadic model of the sign: a qualitative content analysis", Information Discovery and Delivery, Vol. 51 No. 1, pp. 56-65.



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