Using Rough Set Theory for Reasoning on Vague Ontologies

Full Text (PDF, 262KB), PP.21-36

Views: 0 Downloads: 0

Author(s)

Mustapha Bourahla 1,*

1. Computer Science Department, University of M’Sila, M’Sila, 28000, Algeria

* Corresponding author.

DOI: https://doi.org/10.5815/ijisa.2022.04.03

Received: 24 Dec. 2021 / Revised: 1 Feb. 2022 / Accepted: 21 Mar. 2022 / Published: 8 Aug. 2022

Index Terms

Vagueness, Rough Sets, Fuzzy Sets, Description Logics, Fuzzy Description Logics, Ontology Web Language, Automatic Reasoning

Abstract

Web ontologies can contain vague concepts, which means the knowledge about them is imprecise and then query answering will not possible due to the open world assumption. A concept description can be very exact (crisp concept) or exact (fuzzy concept) if its knowledge is complete, otherwise it is inexact (vague concept) if its knowledge is incomplete. In this paper, we propose a method based on the rough set theory for reasoning on vague ontologies. With this method, the detection of vague concepts will insert into the original ontology new rough vague concepts where their description is defined on approximation spaces to be used by extended Tableau algorithm for automatic reasoning. A prototype of Tableau's extended algorithm is developed and tested on examples where encouraging results are given by this method to demonstrate that unlike other methods, it is possible to answer queries even in the presence of incomplete information.

Cite This Paper

Mustapha Bourahla, "Using Rough Set Theory for Reasoning on Vague Ontologies", International Journal of Intelligent Systems and Applications(IJISA), Vol.14, No.4, pp.21-36, 2022. DOI:10.5815/ijisa.2022.04.03

Reference

[1]Baader, F., Horrocks, I., Lutz, C. & Sattler, U., (2017). An Introduction to Description Logic, Cambridge University Press, ISBN 978-0-521-69542-8, 1-255
[2]Hitzler, P., (2021). A review of the semantic web field, Communications of the ACM, Volume 64, Issue 2, 76–83, https://doi.org/10.1145/3397512
[3]Hitzler, P., Krotzsch, M., Parsia, B., Patel-Schneider, P. F., (2009). OWL 2 Web Ontology Language: Primer, S. Rudolph (Eds.).
[4]Krotzsch, M., (2012). OWL 2 profiles: An introduction to light weight ontology languages, in: T. Eiter, T. Krennwallner (Eds.), Proceedings of the 8th Reasoning Web Summer School, Vol. 7487 of Lecture Notes in Computer Science, Springer, 112-183.
[5]Britz, K., Casini, G., Meyer T., Moodley, K., & Sattler, U., Varzinczak I., (2021). Principles of klm-style defeasible description logics, ACM Trans. Comput. Log., 22(1),1-46.
[6]Huitzil, I., Bobillo, F., Gómez-Romero, J., Straccia, U., (2020). Fudge: Fuzzy ontology building with consensuated fuzzy datatypes, Fuzzy Sets Syst, 401, 91-112.
[7]Bourahla, M. (2018). Description and reasoning for vague ontologies using logic programming, IET Softw. 12(5), 397-409.
[8]Kern-Isberner, G. & Lukasiewicz, T., (2017). Many Facets of Reasoning Under Uncertainty, Inconsistency, Vagueness, and Preferences: A Brief Survey, Künstliche Intell. 31(1), 9-13.
[9]Lukasiewicz, T. & Straccia, U., (2007). Managing uncertainty and vagueness in description logics for the semantic web, J. Web Sem, 6 (4), 291-308.
[10]Di Noia, T., Mongiello, M., Nocera, F., Straccia, U., (2019). A fuzzy ontology-based approach for tool-supported decision making in architectural design, Knowl. Inf. Syst. 58(1), 83-112.
[11]Zadeh, L. A., (1965). Fuzzy sets, Information and Control 8 (3), 338-353.
[12]Zadeh, L. A., (1975). The concept of a linguistic variable and its application to approximate reasoning, Inf. Sci. 8 (3), 199-249.
[13]Bobillo, F. & Straccia, U., (2011). Fuzzy ontology representation using OWL 2, International Journal of Approximate Reasoning 52, 1073-1094.
[14]Bobillo, F.& Straccia, U., (2018). Reasoning within Fuzzy OWL 2 EL revisited. Fuzzy Sets Syst, 351, 1-40.
[15]Straccia, U., (2013). Foundations of Fuzzy Logic and Semantic Web Languages, CRC Studies in Informatics Series, Chapman & Hall.
[16]Akama S., Kudo Y., Murai T. (2020) Overview of Rough Set Theory. In: Topics in Rough Set Theory, Intelligent Systems Reference Library, vol 168, Springer.
[17]Pawlak, Z., (1982). Rough sets, International Journal of Parallel Programming, 11(5), 341-356.
[18]Pawlak, Z., Polkowski, L., Skowron, A., (2008). Rough set theory, in: Wiley Encyclopedia of Computer Science and Engineering.
[19]Zhang, Q., Xie, Q., Wang, G., (2016). A survey on rough set theory and its applications, CAAI Transactions on Intelligence Technology, Volume 1, Issue 4, 323-333.
[20]Dubois, D. & Prade, H. (1994). A survey of belief revision and updating rules in various uncertainty models, Int. J. Intell. Syst. 9 (1), 61-100.
[21]Bourahla, M. (2015). Reasoning over Vague Concepts, Artificial Intelligence and Soft Computing - 14th International Conference, ICAISC 2015, Zakopane, Poland, June 14-28, 2015, Proceedings, Part II, Lecture Notes in Computer Science, volume 9120, pp. 591-602, Springer.
[22]Anil Kumar K.M, Bhargava S, Apoorva R, Jemal Abawajy, "Detection of False Income Level Claims Using Machine Learning", International Journal of Modern Education and Computer Science, Vol.14, No.1, pp. 65-77, 2022.
[23]Jayalath Ekanayake, "Bug Severity Prediction using Keywords in Imbalanced Learning Environment", International Journal of Information Technology and Computer Science, Vol.13, No.3, pp.53-60, 2021.
[24]Rajesh Chakraborty, Uttam Kumar Mandal, Rabindra Nath Barman, " A Comparative Study of ANN and GEP Model to Predict the Pressure Drop in the Water Transportation System", International Journal of Information Engineering and Electronic Business, Vol.12, No.5, pp. 47-57, 2020.
[25]Stoilos, G., Venetis, T., Stamou, G. (2015). A Fuzzy Extension to the OWL 2 RL Ontology Language, Comput. J., 58(11), 2956-2971.
[26]Krishnamurthy, S., Janardanan, A., & Akoramurthy, B. (2018). Rough Set Based Ontology Matching, International Journal of Rough Sets and Data Analysis (IJRSDA), 5(2), 46-68. http://doi.org/10.4018/IJRSDA.2018040103
[27]Liao, X., Nazir, S., Shen, J., Shen, B., Khan, S., (2021). Rough Set Approach toward Data Modelling and User Knowledge for Extracting Insights, Complexity, vol. 2021, Article ID 7815418, 9 pages. https://doi.org/10.1155/2021/7815418
[28]Kalibatiene, D., Miliauskaite, J. (2021). A Systematic Mapping with Bibliometric Analysis on Information Systems Using Ontology and Fuzzy Logic, Appl. Sci., 11, 3003, https://doi.org/10.3390/app11073003
[29]Cucala, D. T., Grau, B. C. & Horrocks, I., (2021). Pay-as-you-go consequence-based reasoning for the description logic SROIQ, Artificial Intelligence, 298.
[30]Nuradiansyah, A., (2020). Reasoning in Description Logic Ontologies for Privacy Management, Künstl Intell 34, 411–415. https://doi.org/10.1007/s13218-020-00681-8
[31]Steigmiller, A. & Glimm, B., (2021). Parallelised ABox Reasoning and Query Answering with Expressive Description Logics, ESWC 2021, 23-39