Developing Ontologies based on Folksonomy: A Systematic Review

Document Type : Original Article

Authors

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

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

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

4 Assistant Professor, Department of Knowledge and Information Science, Allameh Tabataba’I University, Tehran, Iran.

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

Abstract

Objective: This paper aims to provide a comprehensive and systematic review of published studies on the topic of developing ontologies based on folksonomies as a means of information management in electronic environments.
Method: This study is conducted using the systematic review method. After designating the questions, input, and output criteria, 87 papers published during 2003-2019 were obtained through querying domestic and international research databases. After screening the results, 29 papers (27 papers in English and 2 papers in Farsi) were closely studied and systematically analyzed. The data was entered into Excel for further statistical analysis and visualization.
Findings: This study verifies the positive results of using folksonomized ontologies in central areas, including information management, and recovery and machine learning. The purpose of organizing knowledge is the quick transfer and retrieval of information. The inefficiency of traditional information organizing tools has always made it difficult to access information quickly and easily. The growth and development of information organization over time indicates a two-way relationship between knowledge growth and organizational evolution. This mutual influence between human knowledge and its organization has been accepted as a principle and its importance has increased over time. It seems that along with the increasing development of the world of knowledge, continuous review and improvement of organizational systems are inevitable. Moreover, this study shows the increasing popularity of folksonomized ontologies among users for determining semantic relationships between tags benefiting from more extensive sets of data.
Furthermore, this study demonstrates that the synthesis between ontologies and folksonomies can facilitate semantic web development by leveraging their differences. An important factor in emphasizing the use of the tacit meanings of folksonomy is the high potential of folksonomy in facilitating the creation of semantic relationships.
Conclusion: Although studies of the application of folksonomy in the development of ontologies have been conducted since 2003, semantic limitations in folksonomy have remained a challenging issue over last decades. However, the vast majority of user-generated folksonomies offer a promising future for semantic web development. Considering the significance of synthesizing folksonomies and ontologies in improving the semantic relationships in information systems and attending to users’ needs by obtaining useful data from tags, further studies are required to find effective solutions to the semantic ambiguities tags and to structure them. This study offers new perspectives to assist experts in the fields of information management and the semantic web.
 

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Main Subjects

References

Alipour Hafezi, M. (1395). E-book lending in digital libraries: A systematic review. Journal of Information Processing and Management, 32(1): 219-250. [In Persian]
Al-Khalifa, H.S. & Davis, H.C. (2006). Folks Annotation: A Semantic Metadata Tool for Annotating Learning Resources Using Folksonomies and Domain Ontologies. Innovations in Information Technology.
DOI:10.1109/INNOVATIONS.2006.301927
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. Instituto de Computa¸c˜ao-Unicamp, Avenida Albert Einstein, 1251, Cidade Universit´aria, Campinas, Brazil.
Angeletou, S.; Sabou, M.; Specia, L. & Motta, E. (2007). Bridging the Gap Between Folksonomies and the Semantic Web: An Experience Report. Knowledge Media Institute (KMi). Proc. Workshop Bridging the Gap between Semantic Web and Web 2.0.
Binzabiah, R. & Wade, S. (2012). Building an Ontology Based on Folksonomy: An attempt to represent knowledge embedded in filmed materials. Journal of Internet Technology and Secured Transactions (JITST(.
DOI: 10.20533/jitst.2046.3723.2012.0013
Chen, W.H.; Cai, Y.; Leung, H.F. & Li, Q. (2010). Generating ontologies with basic
level concepts from folksonomies. The Chinese University of Hong Kong, Hong Kong,
China Department of Computer Science, City University of Hong Kong, Hong Kong,
China.
Christiaens, S. (2006). Metadata mechanisms: from ontology to folksonomy and back. Semantics Technology and Applications Research Laboratory Vrije Universiteit Brussel.
Daud, A.; Li, J.; Zhou, L.; Zhang, L. Ding, Y. & Muhammad, F. (2010). Modeling Ontology of Folksonomy with Latent Semantics of Tags. International Conference on Web Intelligence and Intelligent Agent Technology.
Djuana, E. (2018). Gold-standard evaluation of a folksonomy-based ontology learning model, IOP Conf. Series: Journal of Physics: Conference Series, 971: 012045.
Ebrahimzadeh, S. & Hosseini Beheshti, M. (1395). Changes in knowledge concepts: A look at the necessity of the evolution of ontologies. Epistemological Studies, (7): 97-117. [In Persian]
Fang, Q.; Xu, C.; Hossain, M.S. & Ghoneim, A. (2016). Folksonomy-based Visual Ontology Construction and Its Applications. IEEE, Jitao Sang.
Fields, K. (2007). Ontologies, categories, folksonomies: an organised language of sound. Organised Sound, 12(2): 101-111.
García-Silva, A.; García-Castro, J.; García, A.; Corcho, O. & Gómez-Pérez, A. (2012). Building ontologies from 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.
Goy, A. & Magro, D. (2009). Exploiting Folksonomies and Ontology in an E- Business Application. Dipartimento di Informatica, Università di Torino C. Svizzera, 185, I-10149 Italy.
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. (2017). A Language-Independent Ontology Construction Method Using Tagged Images in Folksonomy. IEEE Access, 6: 2930-2942.
Han, X.; Shen, Z.; Miao, C. & Luo, X. (2008). Folksonomy-Based Ontological User Interest Profile Modeling and Its Application in Personalized Search. School of Computer Engineering, Nanyang Technological University, Nanyang Ave, Singapore 639798.
Jafari Powersi, H.; Hariri, N.; Alipour Hafezi, M.; Bab al-Hawaiji, F. & Khademi, M. (1399). Optimizing Semantic Information Retrieval by Labeling and Ontology. National Studies in Library and Information Organization, 31(1): 18-38. [In Persian]
Kafashan, M. & Fattahi, R. (1390). New systems of knowledge organization: semantic web, ontology and tools for organizing objective knowledge. Library and Information Science, 14(2): 45-70. [In Persian]
Kawakubo, H.; Akima, Y. & Yanai, K. (2010). Automatic Construction of A Folksonomy-based Visual Ontology. Multimedia, International Symposium: 330-335.
DOI:10.1109/ISM.2010.57
Kitchenham, B. & Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering.Keele University, University of Durham, School of Computer Science andMathematics, Department of Computer Science. Keele, Durham: EBSE technical report.
Limpens, F.; Gandon, F. & Buffa, M. (2008). Bridging Ontologies and Folksonomies to Leverage Knowledge Sharing on the Social Web: a Brief Survey. In: Proceedings of the 1st Workshop on Social Software Engineering and Applications (SoSEA): 13-18.
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.
Mac´ıas-Galindo, D.; Wong, W.; Cavedon, L. & Thangarajah, J. (2011). Using a Lexical Dictionary and a Folksonomy to Automatically Construct Domain Ontologies. Australasian Joint Conference on Artificial Intelligence:638–647. Springer.
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. (1395). Philosophical foundations of popular classification and its critique. Information and Communication Critique Book, 3(11): 242-262. [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.
Panke, S. & Gaiser, B. (2009). With My Head Up in the Clouds: Using Social Tagging
to Organize Knowledge. Journal of Business and Technical Communication, 23 (3):
318-349.
Passant, A. & France, E. (2007). Using Ontologies to Strengthen Folksonomies and Enrich Information Retrieval in Weblogs. In: Proceedings of International Conference on Weblogs and Social Media, Boulder, Colorado.
Pornaghi, R. (1386). Plagiarism among university students A systematic review and
meta-analysis study. Research project, Iran Institute of Information Science and Technology.
[In Persian]
Sharif, A. (2009). Combining ontology and folksonomy: An Integrated Approach to Knowledge Representation. Ferdowsi University of Mashhad, Iran (Islamic Republic).
Solskinnsbakk, G.; Gulla, J.A.; Haderlein, V.; Myrseth, P. & Cerrato, O. (2012). Quality of hierarchies in ontologies and folksonomies. Data & Knowledge Engineering, 74: 13-25.
Stapić, Z.E.G. & et al. (2012). Performing systematic literature review in software engineering. Central European Conference on Information and Intelligent Systems (pp. 441-493). Varaždin: Faculty of Organization and Informatics.
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).
Tomuro, N. & Shepitsen, A. (2009). Construction of Disambiguated Folksonomy Ontologies Using Wikipedia. DePaul University, College of Digital Media 243 S. Wabash, Chicago, IL USA.
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.
Trabelsi, C.; Jrad, A.B. & Yahia, S.B. (2010). Bridging folksonomies and domain ontologies: Getting out non-taxonomic relations. In: Data Mining Workshops (ICDMW), 2010 I.E. International Conference on: 369-379.
Van Damme, C. Coenen, T. & Vandijck, E. (2008). Turning a Corporate Folksonomy into a Lightweight Corporate Ontology. 11th International Business Information Systems Conference, BIS 2008, Innsbruck, Austria, May 2008. Heidelberg: Springer.
Van Damme, C.; Hepp, M. & Siorpae, K. (2007). FolksOntology: An Integrated Approach for Turning Folksonomies into Ontologies. Digital Enterprise Research Institute (DERI), University of Innsbruck, Innsbruck, Austria.
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.
Yari, SH. & Hosseini Beheshti, M. (1398). Ontologies and Social Tagging: Relationships
and Applications. Journal of Information Processing and Management, 35(1): 51-76.
[In Persian]
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Sciences and Techniques of Information Management
Volume 7, Issue 2
June 2021
Pages 21-54

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