The Ontology Based Method for Checking Semantic Inconsistency of Relational Databases and Official Documents

Tyumen State University Herald. Physical and Mathematical Modeling. Oil, Gas, Energy


Release:

2018, Vol. 4. №3

Title: 
The Ontology Based Method for Checking Semantic Inconsistency of Relational Databases and Official Documents


For citation: Kropotin A. A., Bidulya Yu. V., Ivashko A. G., Samoylov M. Yu. 2018. “The Ontology Based Method for Checking Semantic Inconsistency of Relational Databases and Official Documents”. Tyumen State University Herald. Physical and Mathematical Modeling. Oil, Gas, Energy, vol. 4, no 3, pp. 120-131. DOI: 10.21684/2411-7978-2018-4-3-120-131

About the authors:

Alexander A. Kropotin, Cand. Sci. (Phys.-Math.), Senior Lecturer, Department of Software and Systems Engineering, University of Tyumen; a.a.kropotin@utmn.ru

Yuliya V. Bidulya, Cand. Sci. (Philol.), Associate Professor, Department of Information Systems, University of Tyumen; y.v.bidulya@utmn.ru

Alexander G. Ivashko, Dr. Sci. (Tech.), Professor, Department of Program and System Engineering, University of Tyumen; a.g.ivashko@utmn.ru

Mikhail Yu. Samoylov, Assistant, Department of Software and Systems Engineering, University of Tyumen; m.y.samojlov@utmn.ru

Abstract:

This work aims to develop a formalism method of description logic to automate the process of determining the semantic conflicts between organization documents and the structure of a relational database. This article proposes an ontological method for verifying the relational representation of a business process to solve the problem of verifying the consistency of information about entities and the relations of the domain and their relational representation within the framework of an individual organization. The ontological model of conceptual objects provides rules for describing the conceptual schemas of the entity — the relationship of relational databases in the form of axioms and statements of the descriptive logic SROIQ(D). This method allows to identify inconsistencies caused by the difference in data types, valid values, and unacceptable values of the same attribute in ontological representations of data in the domain database. To identify inconsistencies in information about entities and domain relations and their relational representation, it is proposed to apply the implementation of a tabular algorithm that would reveal inconsistencies between terminological axioms and statements of general ontology relative to each other.

References:

  1. Alalwan N., Zedan H., Siewe F. 2009. “Generating OWL Ontology for Database Integration”. Proceedings of the 2009 3rd International Conference on Advances in Semantic Processing (11-16 October, Sliema, Malta), pp. 22-31. DOI 10.1109/SEMAPRO.2009.21
  2. Astrova I., Korda N., Kalja A. 2007. “Storing OWL Ontologies in SQL Relational Databases”. World Academy of Science, Engineering and Technology International Journal of Computer, Electrical, Automation, Control and Information Engineering, vol. 1, no 5, pp. 1261-1266. 
  3. Chatterjee N., Kaushik N. 2017. “RENT: Regular Expression and NLP-Based Term Extraction Scheme for Agricultural Domain”. Proceedings of the International Conference on Data Engineering and Communication Technology, pp. 511-511. Springer.
  4. Chujai P., Kerdprasop N., Kerdprasop K. 2014. “On Transforming the ER Model to Ontology Using Protege OWL Tool”. International Journal of Computer Theory and Engineering, vol. 6, no 6, pp. 887-891.
  5. Fahad M. 2008. “ER2OWL: Generating OWL Ontology from ER Diagram”. Intelligent Information Processing IV — 5th IFIP International Conference on Intelligent Information Processing (19-22 October, Beijing, China). The International Federation for Information Processing, vol. 288, pp. 28-37. Mohammad Ali Jinnah University, Islamabad, Pakistan.
  6. Horrocks I., Kutz O., Sattler U. 2006. “The Even More Irresistible SROIQ”. Proceedings of the 10th International Conference on Principles of Knowledge Representation and Reasoning (KR'06). Edited by P. Doherty, J. Mylopoulos, and C. A. Welty. Pp. 57-67. AAAI Press.
  7. Kropotin А. А., Grigoryev А. V., Ivashko А. G. 2016. “Database Schema Method for Automatic Semantic Errors Resolving During Information Systems”. Integration, Informal Proceedings of the 2016 10th IEEE International Conference on Application of Information and Communication Technologies — AICT2016 (12-14 October, Baku, Azerbaijan).
  8. Kropotin А. А., Grigoryev А. V., Bidulya Yu. V., Ivashko А. G., Durynin N. S. 2016. “Realization of the Ontologically Based Method for Checking Structural Inconsistences of Relational Databases”. Proceedings of the 27th International DAAAM Symposium on Intelligent Manufacturing and Automation 2016 (26-29 October, Mostar, Bosnia and Herzegovinа), vol. 27, pp. 762-767. DOI 10.2507/27th.daaam.proceedings.110
  9. Louhdi M. R. C., Behja H., Alaoui S. O. El. 2013. “Transformation Rules For Building Owl Ontologies from Relational Databases”. Proceedings of the 2nd International Conference on Advanced Information Technologies and Applications (November), pp. 271-283.
  10. Magnini B., Negri M., Pianta E., Romano L., Speranza M., Serafini L., Girardi C., Bartalesi V., Sprugnoli R. 2005. “From Text to Knowledge for the Semantic Web: The ONTOTEXT Project”. Proceedings of the 2nd Italian Semantic Web Workshop (14-16 December, University of Trento, Trento, Italy).
  11. Motik B., Shearer R., Horrocks I. 2009. “Hypertableau Reasoning for Description Logics”. Journal of Artificial Intelligence Research, vol. 36, pp. 165-228. 
  12. Telnarova Z. 2010. “Relational Database as a Source of Ontology Creation”. Proceedings of the International Multiconference on Computer Science and Information Technology, pp. 135-139.