1. Home
  2. Issues
  3. Volume 35, Issue 3 (2024)
  4. Ontology and Fuzzy Theory Application in ...

Informatica


Ontology and Fuzzy Theory Application in Information Systems: A Bibliometric Analysis
Volume 35, Issue 3 (2024), pp. 557–576
Pub. online: 16 May 2024      Type: Research Article      Open accessOpen Access
Received
1 November 2023
Accepted
1 April 2024
Published
16 May 2024

Abstract

Ontologies are used to semantically enrich different types of information systems (IS), ensure a reasoning on their content and integrate heterogeneous IS at the semantical level. On the other hand, fuzzy theory is employed in IS for handling the uncertainty and fuzziness of their attributes, resulting in a fully fuzzy IS. As such, ontology- and fuzzy-based IS (i.e. ontology and fuzzy IS) are being developed. So, in this paper, we present a bibliometric analysis of the ontology and fuzzy IS concept to grasp its main ideas, and to increase its body of knowledge by providing a concept map for ontology and fuzzy IS. The main results obtained show that by adding ontologies and fuzzy theory to traditional ISs, they evolve into intelligent ISs capable of managing fuzzy and semantically rich (ontological) information and ensuring knowledge recognition in various fields of application. This bibliometric analysis would enable practitioners and researchers gain a comprehensive understanding of the ontology and fuzzy IS concept that they can eventually adopt for development of intelligent IS in their work.

References

 
Adel, E., El-Sappagh, S., Barakat, S., Elmogy, M. (2018). Distributed electronic health record based on semantic interoperability using fuzzy ontology: a survey. International Journal of Computers and Applications, 40(4), 223–241.
 
Adel, N., Crockett, K., Livesey, D., Carvalho, J. (2022). An interval type-2 fuzzy ontological similarity measure. IEEE Access, 10, 81506–81521.
 
Al Hakim, S., Sensuse, D.I., Budi, I., Prima, P., Safitri, N. (2020). Exploring an intelligent approach in knowledge mapping with ontology and text mining: systematic literature review. In: IICST2020: 5th International Workshop on Innovations in Information and Communication Science and Technology, pp. 33–40.
 
Alamri, B., Crowley, K., Richardson, I. (2022). Blockchain-based identity management systems in health IoT: a systematic review. IEEE Access, 10, 59612–59629.
 
Ali, F., Kwak, K.S., Kim, Y.G. (2016). Opinion mining based on fuzzy domain ontology and support vector machine: a proposal to automate online review classification. Applied Soft Computing, 47, 235–250.
 
Ali, F., Islam, S.R., Kwak, D., Khan, P., Ullah, N., Yoo, S.J., Kwak, K.S. (2018). Type-2 fuzzy ontology–aided recommendation systems for IoT–based healthcare. Computer Communications, 119, 138–155.
 
Amaral, G.B.F., Guizzardi, G. (2021). Foundational ontologies, ontology-driven conceptual modeling, and their multiple benefits to data mining. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 11(4), 1408.
 
Aria, M., Cuccurullo, C. (2017). bibliometrix: an R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975.
 
Bei, L. (2020). Study on the intelligent selection model of fuzzy semantic optimal solution in the process of translation using English corpus. Wireless Communications and Mobile Computing, 2000, 1–7.
 
Bobillo, F., Straccia, U. (2008). fuzzyDL: an expressive fuzzy description logic reasoner. In: IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence, pp. 923–930.
 
Bobillo, F., Straccia, U. (2011). Fuzzy ontology representation using OWL 2. International Journal of Approximate Reasoning, 52(7), 1073–1094.
 
Bobillo, F., Straccia, U. (2016). The fuzzy ontology reasoner fuzzyDL. Knowledge-Based Systems, 95, 12–34.
 
Bobillo, F., Delgado, M., Gómez-Romero, J., Straccia, U. (2009). Fuzzy description logics under Gödel semantics. International Journal of Approximate Reasoning, 50(3), 494–514.
 
Borgo, S., Ferrario, R., Gangemi, A., Guarino, N., Masolo, C., Porello, D., Vieu, L. (2022). DOLCE: a descriptive ontology for linguistic and cognitive engineering. Applied Ontology, 17(1), 45–69.
 
Carlsson, C., Brunelli, M., Mezei, J. (2012). Decision making with a fuzzy ontology. Soft Computing, 16, 1143–1152.
 
Curé, O., Blin, G. (2015). Chapter three – RDF and the semantic web stack. RDF Database Systems. Triples Storage and SPARQL Query Processing, 2015, 41–80.
 
Daradkeh, Y.I., Tvoroshenko, I. (2020). Application of an improved formal model of the hybrid development of ontologies in complex information systems. Applied Sciences, 10(19), 6777.
 
Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., Lim, W.M. (2021). How to conduct a bibliometric analysis: an overview and guidelines. Journal of Business Research, 133, 285–296.
 
D’Aniello, G. (2023). Fuzzy logic for situation awareness: a systematic review. Journal of Ambient Intelligence and Humanized Computing, 14, 4419–4438.
 
Edwita, A., Sensuse, D.I., Noprisson, H. (2017). Critical success factors of information system development projects. In: International Conference on Information Technology Systems and Innovation, (ICITSI). Institute of Electrical and Electronics Engineers Inc., pp. 285–290.
 
Ferreira-Satler, M., Romero, F., Menendez-Dominguez, V., et al. (2012). Fuzzy ontologies-based user profiles applied to enhance e-learning activities. Soft Computing, 16, 1129–1141.
 
Fonseca, C.M., Porello, D., Guizzardi, G., Almeida, J.P.A., Guarino, N. (2019). Relations in ontology-driven conceptual modeling. In: Conceptual Modeling: 38th International Conference, ER 2019, Salvador, Brazil, November 4–7, 2019, pp. 28–42.
 
Gong, Z., Wang, Q. (2017). On the connection of fuzzy hypergraph with fuzzy information system. Journal of Intelligent and Fuzzy Systems, 33(3), 1665–1676.
 
Guarino, N. (1998). Formal ontology in information systems. In: FOIS’98 Conference, FOIS’98. IOS Press, Amsterdam, pp. 3–15.
 
Gupta, E. (2013). Information system. Entrepreneurship and SMEs Building Competencies, 97–102.
 
Huitzil, I., Bobillo, F., Gómez-Romero, J., Straccia, U. (2020). Fudge: fuzzy ontology building with consensuated fuzzy datatypes. Fuzzy Sets and Systems, 401, 91–112.
 
Jain, S., Seeja, K.R., Jindal, R. (2021). A fuzzy ontology framework in information retrieval using semantic query expansion. International Journal of Information Management Data Insights, 1(1), 100009.
 
Junior, H.J., Travassos, G.H. (2022). Consolidating a common perspective on Technical Debt and its Management through a Tertiary Study. Information and Software Technology, 149, 106964.
 
Kalibatienė, D., Miliauskaitė, J. (2021a). A hybrid systematic review approach on complexity issues in data-driven fuzzy inference systems development. Informatica, 32, 1–34.
 
Kalibatienė, D., Miliauskaitė, J. (2021b). A systematic mapping with bibliometric analysis on information systems using ontology and fuzzy logic. Applied Sciences, 11(7), 3003.
 
Kalibatiene, D., Vasilecas, O. (2011). Survey on ontology languages. In: Lecture Notes in Business Information Processing. Springer-Verlag, Berlin Heidelberg, pp. 124–141.
 
Kalibatienė, D., Vasilecas, O. (2012). Application of the ontology axioms for the development of OCL constraints from PAL constraints. Informatica, 23, 369–390.
 
Khalil, G.M., Gotway Crawford, C.A. (2015). A bibliometric analysis of US-based research on the behavioral risk factor surveillance system. American Journal of Preventive Medicine, 48(1), 50–57.
 
Kokar, M.M., Brady, D., Baclawski, K. (2009). Chapter 13 – role of ontologies in cognitive radios. In: Fette, B.A. (Ed.), Cognitive Radio Technology (second edition). Academic Press.
 
Lee, C.S., Wang, M.H. (2010). A fuzzy expert system for diabetes decision support application. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 41(1), 139–153.
 
Lee, C.S., Kao, Y.F., Kuo, Y.H., Wang, M.H. (2007). Automated ontology construction for unstructured text documents. Data and Knowledge Engineering, 60(3), 547–566.
 
Lee, C.S., Wang, M.H., Hagras, H. (2010). A type-2 fuzzy ontology and its application to personal diabetic-diet recommendation. IEEE Transactions on Fuzzy Systems, 18(2), 374–395.
 
Li, C., De Oliveira, J.V., Cerrada, M., Cabrera, D., Sánchez, R.V., Zurita, G. (2018). A systematic review of fuzzy formalisms for bearing fault diagnosis. IEEE Transactions on Fuzzy Systems, 27(7), 1362–1382.
 
Li, Z., Liu, X., Dai, J., Chen, J., Fujita, H. (2020). Measures of uncertainty based on Gaussian kernel for a fully fuzzy information system. Knowledge-Based Systems, 196, 105791.
 
Long, Q. (2015). Three-dimensional-flow model of agent-based computational experiment for complex supply network evolution. Expert Systems with Applications, 42(5), 2525–2537.
 
Lukasiewicz, T., Straccia, U. (2008). Managing uncertainty and vagueness in description logics for the semantic web. Journal of Web Semantics, 6(4), 291–308.
 
Ma, C., Molnár, B. (2020). Use of ontology learning in information system integration: a literature survey. In: Intelligent Information and Database Systems, ACIIDS 2020. Springer, Singapore, pp. 342–353.
 
Maksimov, N.V., Golitsina, O.L., Monankov, K.V., Lebedev, A.A., Bal, N.A., Kyurcheva, S.G. (2019). Semantic search tools based on ontological representations of documentary information. Automatic Documentation and Mathematical Linguistics, 53, 167–178.
 
Mardani, A., Nilashi, M., Zakuan, N., Loganathan, N., Soheilirad, S., Saman, M.Z.M., Ibrahim, O. (2017). A systematic review and meta-Analysis of SWARA and WASPAS methods: Theory and applications with recent fuzzy developments. Applied Soft Computing, 57, 265–292.
 
Martinez-Cruz, C., Porcel, C., Bernabé-Moreno, J., Herrera-Viedma, E. (2015). A model to represent users trust in recommender systems using ontologies and fuzzy linguistic modeling. Information Sciences, 311, 102–118.
 
Morente-Molinera, J.A., Kou, G., González-Crespo, R., Corchado, J.M., Herrera-Viedma, E. (2017). Solving multi-criteria group decision making problems under environments with a high number of alternatives using fuzzy ontologies and multi-granular linguistic modelling methods. Knowledge-Based Systems, 137, 54–64.
 
Morente-Molinera, J.A., Kou, G., Pang, C., Cabrerizo, F.J., Herrera-Viedma, E. (2019). An automatic procedure to create fuzzy ontologies from users’ opinions using sentiment analysis procedures and multi-granular fuzzy linguistic modelling methods. Information Sciences, 476, 222–238.
 
Niu, X., Sun, Z., Kong, X. (2022). A new type of dyad fuzzy β-covering rough set models base on fuzzy information system and its practical application. International Journal of Approximate Reasoning, 142, 13–30.
 
Okikiola, F.M., Ikotun, A.M., Adelokun, A.P., Ishola, P.E. (2020). A systematic review of health care ontology. Asian Journal of Research in Computer Science, 5(), 15–28.
 
Parida, A.K., Bisoi, R., Dash, P.K., Mishra, S. (2017). Times series forecasting using Chebyshev functions based locally recurrent neuro-fuzzy information system. International Journal of Computational Intelligence Systems, 10, 375–393.
 
Porcel, C., Ching-López, A., Lefranc, G., Loia, V., Herrera-Viedma, E. (2018). Sharing notes: an academic social network based on a personalized fuzzy linguistic recommender system. Engineering Applications of Artificial Intelligence, 75, 1–10.
 
Qasim, I., Alam, M., Khan, S., Khan, A., Malik, K., Saleem, M., Bukhari, S. (2020). A comprehensive review of type-2 fuzzy ontology. Artificial Intelligence Review, 53, 1187–1206.
 
Rahayu, N.W., Ferdiana, R., Kusumawardani, S.S. (2022). A systematic review of ontology use in E-Learning recommender system. Computers and Education: Artificial Intelligence, 3, 100047.
 
Saba, D., Sahli, Y., Hadidi, A. (2021). An ontology based energy management for smart home. Sustainable Computing: Informatics and Systems, 31, 100591.
 
Sattar, A., Surin, E.S.M., Ahmad, M.N., Ahmad, M., Mahmood, A.K. (2020). Comparative analysis of methodologies for domain ontology development: a systematic review. International Journal of Advanced Computer Science and Applications, 11(5), 99–108.
 
Stoilos, G., Stamou, G. (2014). Reasoning with fuzzy extensions of OWL and OWL 2. Knowledge and Information Systems, 40(1), 205–242.
 
Tabakov, M., Chlopowiec, A., Chlopowiec, A., Dlubak, A. (2021). Classification with fuzzification optimization combining fuzzy information systems and type-2 fuzzy inference. Applied Sciences, 11(8), 3484.
 
Travé Allepuz, E., Medina Gordo, S., del Fresno Bernal, P., Vicens Tarré, J., Mauri Martí, A. (2021). Towards an ontology-driven information system for archaeological pottery studies: the greyware experience. Applied Sciences, 11(17), 7989.
 
Tvoroshenko, I., Ahmad, M., Ayaz, M., Syed, K., Lyashenko, V., Alharbi Adel, R. (2020). Modification of models intensive development ontologies by fuzzy logic. International Journal of Emerging Trends in Engineering Research, 8, 939–944.
 
van Eck, N.J., Waltman, L. (2019). Manual for VOS Viewer Version 1.6.10. CWTS, Universiteit Leiden, Leiden, Holland.
 
van Eck, N., Waltman, L., Noyons, E., Buter, R. (2010). Automatic term identification for bibliometric mapping. Science, 82, 581–596.
 
Verdonck, M., Gailly, F., Pergl, R., Guizzardi, G., Martins, B., Pastor, O. (2019). Comparing traditional conceptual modeling with ontology-driven conceptual modeling: an empirical study. Information Systems, 81, 92–103.
 
Wand, Y., Weber, R. (1993). On the ontological expressiveness of information systems analysis and design grammars. Information Systems Journal, 3, 217–237.
 
Wang, J. (2022). Encyclopedia of Data Science and Machine Learning. IGI Global.
 
Wikström, R., Mezei, J. (2014). Intrusion detection with type-2 fuzzy ontologies and similarity measures. In: Intelligence, Intelligent Methods for Cyber Warfare, Studies in Computational Intelligence, 563, 151–172.
 
Yepes-Nuñez, J.J., Urrútia, G., Romero-García, M., Alonso-Fernández, S. (2021). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Revista Española de Cardiología, 74 (9), 790–799.
 
Yu, Y.X., Gong, H.P., Liu, H.C., Mou, X. (2023). Knowledge representation and reasoning using fuzzy Petri nets: a literature review and bibliometric analysis. Artificial Intelligence Review, 56 6241–6265.
 
ZareRavasan, A., Krčál, M. (2021). A systematic literature review on 30 Years of empirical research on information systems business value. Journal of Global Information Management (JGIM), 29 (6), 1–37.
 
Zhang, F., Cheng, J., Ma, Z. (2016). A survey on fuzzy ontologies for the Semantic Web. The Knowledge Engineering Review, 31(3), 278–321.
 
Zhang, F., Ma, Z., Chen, X. (2015). Formalizing fuzzy object-oriented database models using fuzzy ontologies. Journal of Intelligent and Fuzzy Systems, 29(4), 1407–1420.
 
Zhang, G., Li, Z., Wu, W.Z., Liu, X., Xie, N. (2018). Information structures and uncertainty measures in a fully fuzzy information system. International Journal of Approximate Reasoning, 101, 119–149.
 
Zhang, X., Li, J., Mi, J. (2022). Dynamic updating approximations approach to multi-granulation interval-valued hesitant fuzzy information systems with time-evolving attributes. Knowledge-Based Systems, 238, 107809.

Biographies

Kalibatienė Diana
[email protected]

D. Kalibatienė Kalibatiene received his PhD degree in technological sciences from Vilnius Gediminas Technical University (VilniusTECH) in 2009. Currently, she is a full-time professor at the Information Systems Department, VilniusTECH, Lithuania. She has published over 100 research papers in international journals and conference proceedings. Her research fields include ontology-based information systems development; rule-based dynamic business process modelling and simulation; multi-criteria decision-making methods application in different fields; fuzzy theory application in quality planning and prediction. She actively participates in Erasmus+ teaching and has delivered lectures at Universidade Nova De Lisboa (Lisboa, Portugal), International University Travnik (Travnik, Bosnia and Herzegovina), Palermo University (Sicily, Italy), University of La Laguna (Tenerife), and University of Rousse (Bulgaria). She supervises one PhD student and one PhD student has already defended her thesis “Fuzzy Inference and Machine Learning-Based Prediction with a Small Dataset for Oil Spills in the Geological Environment”. She is a Coordinator of the Erasmus+ KA220-HED Project “Embracing rapid application development (RAD) skills opportunity as a catalyst for employability and innovation” (RAD-Skills) (2022-10-01–2024-09-30). She is a member and chair of bachelor’s and master’s degree study programmes in VilniusTECH. In 2021, she was an invited editor of the Applied Sciences journal special issue “Ontology-Based Information Systems Establishment and Recent Development”. From 2022, she is an Editor-in-Chief of the newly established journal New Trends in Computer Sciences and a Steering Committee member of the Baltic DB&IS conference.

Miliauskaitė Jolanta
[email protected]

J. Miliauskaitė received her PhD degree in technological sciences from Vilnius University in 2015. Currently, she is a researcher and associate professor at Vilnius University (Lithuania) Institute of Data Science and Digital Technologies Department of Cyber-Social Systems Engineering Group. She is an author and a co-author of scientific papers, a majority of which were published in Web of Science journals. Her research interests include enterprise business services, service-oriented enterprise systems, web service composition, quality of service modelling and evaluation of service-oriented enterprise systems, fuzzy theory application in quality planning and prediction.

Slotkienė Asta
[email protected]

A. Slotkienė received her PhD degree in technological sciences from Kaunas Technical University in 2009. She is an associate professor at Cyber-Social Systems Engineering Group, Institute of Data Science and Digital Technologies, Vilnius University. She is an author and a co-author of several scientific papers, most of which are published in Web of Science journals. Her research interests include software quality assurance, software process improvement based on machine learning, and quality of service modelling and evaluation of service-oriented enterprise systems.


Full article Related articles PDF XML
Full article Related articles PDF XML

Copyright
© 2024 Vilnius University
Open access article under the CC BY license.

Keywords
ontology fuzzy theory information system bibliometric analysis

Metrics
since January 2020
266

Article info
views

 137

Full article
views
141

PDF
downloads

 32

XML
downloads


Share


RSS