Folksonomy-Based Ontology: A Link Between Intelligence and Meaning in Psychology and Educational Sciences

Document Type : Original Article


1 PhD. Student, Department of Knowledge and Information Science, Hamadan Branch, Islamic Azad University, Hamadan, Iran.

2 Professor, Department of Knowledge and Information Science, University of Qom, Qom, Iran

3 Associate Professor, Department of General Linguistics, Iranian Research Institute for Information Science and Technology, Tehran, Iran.

4 Assistant Professor, Department of Knowledge and Information Science, Allameh Tabatabai University, Tehran, Iran.

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


Purpose: The aim of the present study is to reconcile the dual embodiment of ontology and folksonomy and establish a symbiotic relationship between the two technologies for the development of folksonomized ontology as a tool for organizing information in the fields of psychology and educational sciences in cyberspace.
Method: This study is of an applied nature and utilizes a combined research method. To gain conceptual knowledge and develop a semi-automated ontology based on natural language (common labels), the three-step method proposed by Alves and Santanche (extraction, enrichment, and synthesis) (2013) is utilized, along with Noy and McGuinness's approach (2001). The statistical population refers to the labels used by students of psychology and educational sciences in the digital library of Allameh Tabatabai University. The purposive sampling method is used to select and aggregate specific labels. Additionally, to evaluate the credibility and validity of the extracted concepts and relationships, they are reviewed and confirmed by 15 experts in the fields of psychology and educational sciences. The software used to create the ontology is Protégé version 5.5.
Findings: A total of 15,787 labels were extracted. After identifying the fundamental concepts and their interrelationships using the Eric database, a conceptual framework with an ontological approach was developed. After confirming and validating the conceptual structure, an ontology based on folksonomy is presented as the main research finding. It consists of 277 classes and subclasses, 110 relation types, 50 samples, and 9 synonyms taken from the Eric database, and is illustrated in several graphs. Based on the statistical test performed on the ontology derived from folksonomy with a significance level of 0.0005, the model has been proven to be relatively accurate, and all components have been confirmed by experts.
Conclusion: Folksonomies are "user-generated ontologies" within the Semantic Web. Leveraging the structural distinctions between folksonomies and ontologies holds the potential to advance the Semantic Web without requiring a deep understanding of complex classification systems.


Main Subjects

Al-Khalifa, H.S. & Davis, H.C. (2006). FolksAnnotation: A Semantic Metadata Tool for Annotating Learning Resources Using Folksonomies and Domain Ontologies. Innovations in Information Technology. IEEE Computer Society, Dubai, UAE.
Alruqimi, M. & Aknin, N. (2019). Bridging the Gap between the Social and Semantic Web: Extracting domain-specific ontology from folksonomy. Journal of King Saud University–Computer and Information Sciences, 31(1): 15-21.
Alves, H. & Santanche, A. (2013). Folksonomized Ontology and the 3E Steps Technique to Support Ontology Evolvement. Journal of WebSemantics, 18(1): 19-30. 
Besseny, A. (2020). Lost in spotify: folksonomy and wayfinding functions in spotify’s interface and companion apps. Popular Communication, 18(1): 1-17. 
Bruhn, C. & Syn, S.Y. (2018). Pragmatic thought as a philosophical foundation for collaborative tagging and the Semantic Web. Journal of Documentation, 74(3): 575-587.   
Chen, W., Cai, Y., Leung, H. & Li, Q. (2010). Generating ontologies with basic level concepts from folksonomies. International Conference on Computational Science, ICCS 2010. Procedia Computer Science, 1(1): 573-581.
Christiaens, S. (2006). Metadata mechanisms: from ontology to folksonomy and back. Semantics Technology and Applications Research Laboratory Vrije Universiteit Brussel. /10.1007/11915034_43
Daud, A., Li, J., Zhou, L., Zhang., L, Ding, Y. & Muhammad, F. (2010). Modeling ontology of folksonomy with latent semantics of tags. Web Intelligence and Intelligent Agent Technology. IEEE/WIC/ACM Int Conf on 1: 516-523.
Djuana, E. (2018). Gold-standard evaluation of a folksonomy-based ontology learning model, IOP Conf. Series: Journal of Physics: Conference Series, 971: 012045.
Djuana, E., Xu, Y. & Li, Y. (2012). Learning personalized tag ontology from user tagging information. In: Proceedings of the Tenth Australasian Data Mining Conference (Sydney, Australia, December 05-07, 2012). AusDM 2012. ACS, Sydney, Aus, 183-190.
Dong, H., Wang, W., Coenen, F. & Huang, K. (2020). Knowledge base enrichment by relation learning from social tagging data. Information Sciences, 526: 203-220.
Ebrahimzadeh, S. & Hosseini Beheshti, M. (2016). Change of the Concepts of Knowledge: a Glimpse at the Necessity of the Development of Ontologies. Knowledge Retrieval and Semantic Systems, 2(7): 97-11. [in persian]
Fang, Q., Xu, C., Sang, J., Shamim, H. & Ghoneim, A. (2016). Folksonomy-based visual ontology construction and its applications. IEEE Trans. Multim, 18(4): 702-713. 
García-Silva, A., García-Castro, J., García, A., Corcho, O. & Gómez-Pérez, A. (2015). Building ontologies Out of folksonomies and linked data: Data structures and Algorithms. International Journal on Artificial Intelligence Tools, 24(2).
Gasevic, G., Zouaq, A., Torniai, C., Jovanovic, J. & Hatala, M. (2011). An Approach to Folksonomy-Based Ontology Maintenance for Learning Environments. IEEE Transactions on Learning Technologies, 99: 1-14.
Goodarzi, N., Norouzi, Y., Hosseini Beheshti, M., Alipoor-Hafezi, M. & Bayat, B. (2021). Developing Ontologies based on Folksonomy: A Systematic Review. Sciences and Techniques of Information Management, 7(2): 21-54. [in persian]
Gruber, T. (2007). Ontology of folksonomy: A mash-up of apples and oranges. International Journal on Semantic Web & Information Systems, 3(2): 1-11.
Hamano, S., Ogawa, T. & Haseyama, A. (2018). A Language-Independent Ontology Construction Method Using Tagged Images in Folksonomy. IEEE Access, 6: 2930-2942.
Honarjooyan, Z. & Mirzabeigi, M. (2020). Semantics in Social Tagging Systems: A Systematic Review. Librarianship and Information Organization Studies, 31(123): 110-129. [in persian]
Hosseini Beheshti, M. & Ejei, F. (2015). Designing and Implementing Basic Sciences Ontology Based on Concepts and Relationships of Relevant Thesauri. Iranian Journal of Information Processing and Management, 30(3): 677-696. [in persian]
Hunter, J. & Gerber, A. (2010). Harvesting community annotations on 3D models of museum artefacts to enhance knowledge, discovery and re-use. Journal of Cultural Heritage, 11(1): 81-90.
Jafari Pavarsi, H., Hariri, N., Alipour Hafezi, M., Babalhavaeji, F. & Khademi, M. (2020). Optimizing Semantic Information Retrieval by Labeling and Ontology. Librarianship and Information Organization Studies, 31(1): 18-38. [in persian]
Kafashan, M. & Fattahi, R. (2011). Modern knowledge organization systems: semantic web, ontology and objective knowledge organization tools. Library and Information Sciences, 14(2): 45-70.
[in persian]
Karizade, S. (2008) What is a Folksonomy. Ketabeh Mahe-Kolliat, Ettelat- Ertebatat & Danesh shenasi. No.130: 24-33. [in persian]
Khademian, M. & Kokabi, M. (2018). Folksonomies versus controlled vocabularies: Theoretical approaches. Iranian Journal of Information Processing and Management, 33(2): 945-962. [in persian]
Limpens, F., Buffa, M. & Gandon, F. (2008). Bridging Ontologies and Folksonomies to leverage knowledge sharing on the social web: a brief survey. Conference: Automated Software Engineering - Workshops, 2008. ASE Workshops 2008. 23rd IEEE/ACM International Conference on.
Lin, H., Davis, J. & Zhou, Y. (2009). An Integrated Approach to Extracting Ontological Structures from Folksonomies. School of Information Technologies, The University of Sydney, Australia. Information Systems and Machine Learning Lab (ISMLL) Samelsonplatz 1, University of Hildesheim, D-31141 Hildesheim, Germany.
Macías-Galindo, D., Wong, W., Cavedon, L. & Thangarajah, J. (2011). Using a Lexical Dictionary and a Folksonomy to Automatically Construct Domain Ontologies. In: Wang D. & Reynolds M. (eds.). AI 2011: Advances in Artificial Intelligence. Springer Berlin Heidelberg, 2011: 638-47.
Magnuson, L. (2009). Folksonomies: Meaning, Discourse, and Information Retrieval. Proceedings of the Annual Conference of the Canadian Association for Information Science. Available at: http://www.cais-acsi. ca/proceedings/2009/Magnuson_2009.pdf.
Majidi, A. (2016). Philosophical foundations of popular classification and its review. Ketabeh Mahe-Kolliat, Ettelat- Ertebatat & Danesh shenasi, 3(11): 243-262. [in persian]
Mao, M., Chen, S., Zhang, F., Han, J. & Xiao, Q. (2021). Hybrid ecommerce recommendation model incorporating product taxonomy and folksonomy. Knowledge-Based Systems, Vol. 214.
Mardani, A. (2009). Folksonomy: by users, for users. Librarianship and Information Organization Studies, 20(79): 239-260. [in persian]
Mika, P. (2007). Ontologies are us: A unified model of social networks and semantic, Web Semant. Journal of Web Semantics, 5(1): 5-15.
Norouzi, A., Mansori, E. & Hoseni, S. (2018). Folk taxonomy (folksonomy): organizing knowledge based on collective wisdom. Ettela-Shenasi, 17-18: 151-166. [in persian]
Noy, N.F. & McGuinness, D.L. (2001). Ontology Development 101: A Guide to Creating Your First Ontology. Stanford: Stanford Knowledge Systems Laboratory Technical Report and Stanford Medical Informatics Technical Report.
Potnis, D. (2011). Folksonomy-based User-centric Information Organization Systems. International Journal of Information Studies, 3: 31-43
Qassimi, S. & Abdelwahed, E.H. (2019). The role of collaborative tagging and ontologies in emerging semantic of web resources. Computing, 101(10): 1489–1511.
Saadat, R., Shabani, A., Asemi, A. & CheshmehSohrabi, M. (2019). Potential of Folksonomies for Enhancing Professional Knowledge Organization Systems: a Review of Conceptions and Literature. Librarianship and Information Organization Studies, 29(4): 7-26. [in persian]
Sayadi, N. (2019). Semantic web and ontology and their role in knowledge organization process. Librarian 2.0. URL= [in persian]
Soergel, D. (2008). Digital libraries and knowledge organization. Available at: http://www.dsoergel. com/New Publications/ Soergel Digital Libraries and Knowledge Organization.
Stojanovic, L., Maedche, A., Stojanovic, N. & Studer, R. (2003). Ontology evolution as reconfiguration-design problem solving. In: Proceedings of KCAP 2003, ACM: 162-171.
Tang, J., Leung, H-F., Luo, Q., Chen, D. & Gong, J. (2009). Towards Ontology Learning from Folksonomies. In: Proceedings of the 21st International Joint Conference on Artificial Intelligence (IJCAI).
Torniai, C., Jovanović, J., Bateman, S., Gašević, D. & Hatala, M. (2008). Leveraging Folksonomies for Ontology Evolution in E-learning Environments. The IEEE International Conference on Semantic Computing.
Van Damme, C., Hepp, M. & Siorpae, K. (2007). Folksontology: An integrated approach for turning folksonomies into ontologies. Bridging the Gap between Semantic Web and Web, 2(2007): 57-70
Wang, S., Wang, W., Zhuang, Y. & Fei, X. (2015). An ontology evolution method based on folksonomy. School of Computer Information Engineer, Changzhou Institute of Technology, Changzhou, Jiangsu, P.R. China. Journal of Applied Research and Technology, 13: 177-187.
Yari, Sh. & Hosseini Beheshti, M. (2019). Ontologies and Social Tagging: Relationships and Applications. Iranian Journal of Information Processing and Management, 35(1): 51-76. [in persian]