Folksonomy-based Ontology: a link between intelligence and meaning in the field of psychology and educational sciences

Document Type : Original Article


1 Ph.D. Student, Hamedan Branch, Islamic Azad University

2 PhD in Knowledge and Information Science; Department of Knowledge and Information Science; University of Qom; Iran

3 Ph.D. in Linguistic; AssistantProfessor; Iranian Research Institute for Information Science and Technology; Tehran, Iran

4 Ph.D. in Knowledge and Information Science; Assistant Professor Faculty of Psychology and Educational Sciences, Allameh Tabatabai University; Tehran, Iran

5 Assistant Professor, Department of Knowledge and Information Science, Hamedan Branch, Islamic Azad University, Hamedan, Iran.


Target: The aim of the present study is to solve the dual embodiment of ontology and folksonomy and to create a coexistence between the two technologies for the development of folksonomized ontology as a tool for organizing information in cyberspace.

Method: This study is of applied type and uses combined research method. To acquire conceptual knowledge and build a semi-automated ontology based on natural language, the three-step method of E3, developed by Alves, Santanche, Noy, and McGuinness, is employed. The statistical population is the labels provided by students of psychology and educational sciences at Allameh Tabatabai University.

Findings: The study resulted in the extraction of 94 concepts out of a total of 15,787 labels. Accordingly, after confirmation and validation of the conceptual structure, a Folksonomized Ontology is presented as the main research finding consisting of 277 classes and subclasses, 110 relation types, 50 samples, and 9 synonyms taken from the Eric database. The model is proved to be relatively accurate, and confirmed by experts.

Conclusion: Folksonomies are "user-generated ontologies" in the Semantic Web. Utilizing the structural differences between folksonomy and ontologies promises the evolution of Semantic Web with no need for basic knowledge and learning complex classification systems.


Main Subjects