Fuzzy data in semantic Wiki-resources: models, sources and processing methods

J. V. Rogushina


We analyze main types of dirty data processed by intelligente information systems, criteria of data classification and means of detection non-classical properties of data. Results of this analysis are represented by ontological model that contains taxonomy of classical and nonclassical data and knowledge-oriented methods
of their transformation. Special attention is paid to semantically incorrect data that corresponds to vague knowledge. This ontological model intended to provide more effectively methods for transforming raw data into smart data suitable for automatic analysis, knowledge acquisition and reuse in other information systems. The
ontological approach provides integration of the proposed model with other external ontologies that formalize characteristics of various methods and software tools that can be used fo data analysis (data mining, inductive inference, semantic queries, and instrimental tools for testing various aspects of the ontology quality, etc.).
The work uses the experience of knowledge base developing of the portal version of the Great Ukrainian Encyclopedia e-VUE. This information resource is based on the semantic Wiki technology, it has a large volume, a complex structure and contains a large number of various heterogeneous information objects. Wiki resources are interesting from the point of view of collaborative processing the fuzzy data
that describe heterogeneous information objects and knowledge structures. Due to the fact that the creation of this information resource involves a large number of specialists of various scientific fields, who have different areas of expertise
and qualifications in use of knowledge-oriented technologies, there are many differences in the understanding of the rules for presenting and structuring data, and therefore a significant part of the Encyclopedia content needs additional
verification of its correctness. Therefore, we need in formalized and scalable solutions for detection and processing various types of inconsistence, incompleteness and semantic incorrectness of data. The proposed approach can be useful for the creation of other large-scale resources
based on both the semantic Wiki technology and other technological platforms for collaborative processing of distributed data and knowledge.

Prombles in programming 2023; 2: 67-83 


ontology; semantically incorrect data; dirty data; Wiki resource


Zadeh L. A. Fuzzy sets and information granularity. Fuzzy sets, fuzzy logic, and fuzzy systems: selected papers, 1979, pp.433-454.

Motro, A., Smets, P. Uncertainty Manage- ment in Information Systems: From Needs to Solutions. Springer, 1997. 464 p. DOI: http://dx.doi.org/10.1007/978-1-4615-6245- 0.

Codd E. F. Missing information (applicable and inapplicable) in relational databases. ACM Sigmod Record, 15(4), 1986, pp.53- 53.

Parsons S. Current Approaches to Handling Imperfect Information in Data and Knowl- edge Bases // Knowledge and Data Engi- neering IEEE, Vol.8, №3, 1996. pp. 483-488.

Zadeh L. A. The concept of a linguistic variable and its application to approxi- mate reasoning. Information sciences, 8(3), 1975pp.199-249, DOI: http://dx.doi.org/ 10.1016/0020-0255(75)90036-5.

Kim W., Choi, B. J., Hong E. K., Kim S. K., Lee D. A taxonomy of dirty data. Data mining and knowledge discovery, 7, 2003, pp.81-99.

Kim W., Chae K. J., Cho D. S., Choi B., Jeong A., Kim M., Yong H. S. The Chamois component-based knowledge engineering framework. Computer, 35(5), 2002, pp.45- 54.

Koren Y. Working with MediaWiki. San Bernardino, CA, USA: WikiWorks Press. 157-159(2012). URL: uplooder.net.

Semantic MediaWiki. https://www.seman- tic-mediawiki.org/wiki/Semantic_MediaWi- ki.

Guarino N. Formal Ontology in Information Systems. Formal Ontology in Information Systems. // Proc. of FOIS’98, 3-15, 1998.

Rogushina J.V., Grishanova I.J. Ontological methods and tools for semantic extension of the media WIKI. Problems in programming,

№ 2-3, 2020. pp.61-73. DOI:10.15407/ pp2020.02-03.061.

Andon P.I., Rogushina J.V., Grishanova I.Y., Reznichenko V.A., Kyrydon A.M., Aristova A.V., Tyschenko A.O. Experience of Se- mantic Technologies Use for Development of Intelligent Web Encyclopedia. Proc. of the 12th International Scientific and Practi- cal Conference of Programming (UkrPROG 2020),CEUR Workshoop Proceedings, 2021, Vol-2866, P.246-259. http://ceur-ws.org/ Vol-2866/ceur_246-259andon24.pdf

Tversky A. Features of similarity. Psycho- logical review, 84(4), 1977, pp.327-341.

Rada R., Mili H., Bicknell E., Blettner M. Development and application of a metric on semantic nets. IEEE transactions on systems, man, and cybernetics, 19(1), 1989, pp.17-30.

Resnik P. Semantic Similarity in a Taxono- my: An Information-Based Measure and its Application to Problems of Ambiguity in Natural Language. In: Journal of Artificial Intelligence Research 11, 1999, pp.95-130..

Rogushina J. Use of Semantic Similarity Estimates for Unstructured Data Analysis. Selected Papers of ITS 2019. CEUR Vol- 2577, pp.246-258. URL: http://ceur-ws. org/Vol-2577/paper20.pdf [last accessed 2023/02/122].

RDF Web Ontology Language. Overview, W3C, 2012. https://www.w3.org/RDF/ [last accessed 2023/02/15].

Rogushina J., Grishanova I. Ontological methods and tools for semantic extension of the media WIKI technology. Problems in Programming, № 2-3, 2020, pp.61-73.

Pidnebesna H., Stepashko V. Ontology Ap- plication to Constructing the GMDH-Based Inductive Modeling Tools. Semantic Web Technologies, 2022, pp. 263-292.

Panov P., Dzeroski S., Soldatova L. On- toDM: An ontology of data mining. In: 2008 IEEE International Conference on Data Min- ing Workshops, IEEE, 2008, pp. 752-760.


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