Linkage scenarios of relational databases and ontologies: a systematic mapping
DOI:
https://doi.org/10.29019/enfoqueute.759Keywords:
relational databases, ontologies, systematic mappingAbstract
Relational databases are one of the most used data sources. However, as a storage source, they present a group of shortcomings. It is complex to store semantic knowledge in relational databases. To solve the deficiencies in knowledge representation of relational databases, one trend has been to use ontologies. Ontologies possess a richer semantic and are closer to the end user vocabulary than relational database schemas. The objective of the present research was to carry out a systematic mapping about the scenarios where relational databases and ontologies are linked to provide a better integration, query, and visualization of stored data. The mapping was carried out by applying a methodological proposal established in the literature. As outcomes of the research, it was detected that the mapping of relational databases to ontologies and the ontologies usage for the integration of heterogeneous data sources were the most common scenarios. Likewise, trends and challenges were identified in each scenario, which might deserve further research efforts in the future.
Downloads
References
Abbes, H.; Gargouri, F. (2017). MongoDB-Based Modular Ontology Building for Big Data Integration. Journal on Data Semantics, 7: 1-27. https://doi.org/10.1007/s13740-017-0081-z
Ameen, A. et al. (2014). Reasoning in Semantic Web Using Jena. Computer Engineering and Intelligent Systems, 5(4): 39-48. https://core.ac.uk/download/pdf/234644794.pdf
Bizer, C.; Seaborne, A. (2004). D2RQ-treating non-RDF databases as virtual RDF graphs. In Proceedings of the 3rd international semantic web conference (ISWC2004) (Vol. 2004). Springer. https://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.126.2314
Capsenta, J. F. S.; Miranker, D. P. (2017). A Pay-As-You-Go Methodology for Ontology-Based Data Access. IEEE Internet Computing, 21(2): 92-96. https://doi.org/10.1109/MIC.2017.46
Čerāns, K.; Būmans, G. (2015). RDB2OWL: a language and tool for database to ontology mapping. In Proceedings of the CAiSE 2015 Forum at the 27th International Conference on Advanced Information Systems Engineering (CAiSE 2015), Kista, Sweden (81-88). http://ceur-ws.org/Vol-1367/paper-11.pdf
Freitas, R., et al. (2017). Using linked data in the data integration for maternal and infant death risk of the SUS in the GISSA Project. In Proceedings of the 23rd Brazillian Symposium on Multimedia and the Web (193-196). https://doi.org/10.1145/3126858.3131606
Gorskis, H.; Aleksejeva, L.; Polaka, I. (2016). Database Analysis for Ontology Learning. Procedia Computer Science, 102: 113-120. https://doi.org/10.1016/j.procs.2016.09.377
Haw, S. C.; May, J. W. (2017). Mapping Relational Databases to Ontology Representation: A Review. In Proceedings of the International Conference on Digital Technology in Education, 54-58. https://doi.org/10.1145/3134847.3134852
Hazber, M. A., et al. (2019). A survey: Transformation for integrating relational database with semantic Web. In Proceedings of the 2019 3rd International Conference on Management Engineering, Software Engineering and Service Sciences (66-73). https://dl.acm.org/doi/10.1145/3312662.3312692
Horridge, M.; Bechhofer, S. (2011). The OWL API: A Java API for OWL Ontologies. Semantic Web, 2(1): 11-21. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.678.6080&rep=rep1&type=pdf
Karimi, H.; Kamandi, A. (2019). PT US CR. Expert Systems With Applications, 125: 412-424. https://doi.org/10.1016/j.eswa.2019.02.014
Liu, X.; Gao, F. (2018). An Approach for Learning Ontology from Relational Database. Proceedings of the 2018 International Conference on Algorithms, Computing and Artificial Intelligence. https://doi.org/10.1145/3302425.3302495
López Rodríguez, Y. A.; Hidalgo Delgado, Y.; Silega Martínez, N. (2016). Método para la integración de ontologías en un sistema para la evaluación de créditos. Revista Cubana de Ciencias Informáticas, 10(4): 97-111. http://scielo.sld.cu/scielo.php?script=sci_arttext&pid=S2227-18992016000400007
Maran, V.; Medeiros, G.; Machado, A. (2017). Database Ontology-Supported Query for Ubiquitous Environments. In Proceedings of the 23rd Brazillian Symposium on Multimedia and the Web, 185-188. https://doi.org/10.1145/3126858.3131575
Miller, G. A. (1995). WordNet: A Lexical Database for English. Communications of the ACM, 38(11): 39-41. https://doi.org/10.1145/219717.219748
Nakhla, Z.; Nouira, K. (2017). Automatic approach to enrich databases using ontology: Application in medical domain. Procedia Computer Science, 112: 387-396. https://doi.org/10.1016/j.procs.2017.08.221
Petersen, K.; Vakkalanka, S.; Kuzniarz, L. (2015). Guidelines for conducting systematic mapping studies in software engineering : An update. Information and software technology, 64: 1-18. https://doi.org/10.1016/j.infsof.2015.03.007
Pop, C., et al. (2015). M2O: A Library for Using Ontologies in Software Engineering. Intelligent Computer Communication and Processing (ICCP): 69-75. https://doi.org/10.1109/ICCP.2015.7312608
Reynoso, J. L., et al. (2015). Automatic Mapping Magnetic Resonance Images into Multimedia Database Using SIFT. IEEE Latin America Transactions, 13(8): 2709-2714. https://doi.org/10.1109/TLA.2015.7332153
Seo, D., et al. (2014). Development of Korean spine database and ontology for realizing e-Spine. Cluster computing. Recuperado de https://link.springer.com/article/10.1007/s10586-013-0344-x
Sequeda, J. F.; Miranker, D. P. (2013). Ultrawrap: SPARQL execution on relational data. Journal of Web Semantics, 22: 19-39. https://doi.org/10.1016/j.websem.2013.08.002
Soylu, A., et al. (2016). Ontology-based end-user visual query formulation: Why, what, who, how, and which? Universal Access in the Information Society, 16: 435-467 https://doi.org/10.1007/s10209-016-0465-0
Studer, R.; Benjamins, V. R.; Fensel, D. (1998). Knowledge Engineering : Principles and Methods. Data and Knowledge engineering, 25(1): 161-197. https://doi.org/10.1016/S0169-023X(97)00056-6
Sujatha, B.; Raju, S. V. (2016). Ontology Based Natural Language Interface for Relational Databases. Procedia Computer Science, 92: 487-492. https://doi.org/10.1016/j.procs.2016.07.372
Tao, M.; Ota, K.; Dong, M. (2016). Ontology-based Data Semantic Management and Application in IoT- and Cloud-Enabled Smart Homes. Future Generation Computer Systems, 76: 528-539 https://doi.org/10.1016/j.future.2016.11.012
Thi, P., et al. (2014). RDB2RDF : Completed Transformation from Relational Database into RDF Ontology. In Proceedings of the 8th International Conference on Ubiquitous Information Management and Communication, 88. https://doi.org/10.1145/2557977.2558083
Tonella, P., et al. (2007). Empirical studies in reverse engineering : state of the art and future trends. Empirical Software Engineering, 12(5): 551-571. https://doi.org/10.1007/s10664-007-9037-5
Urrutia, A., et al. (2017). An Ontology to Assess Data Quality Domains. A Case Study Applied to a Health Care Entity. IEEE Latin America Transactions, 15(8): 1506-1512. https://doi.org/10.1109/TLA.2017.7994799
Zdravkovi, M., et al. (2013). Explication and semantic querying of enterprise information systems. Knowledge and information systems, 40(3): 697-724. https://doi.org/10.1007/s10115-013-0650-x
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2021 The Authors
![Creative Commons License](http://i.creativecommons.org/l/by/3.0/88x31.png)
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
The articles and research published by the UTE University are carried out under the Open Access regime in electronic format. This means that all content is freely available without charge to the user or his/her institution. Users are allowed to read, download, copy, distribute, print, search, or link to the full texts of the articles, or use them for any other lawful purpose, without asking prior permission from the publisher or the author. This is in accordance with the BOAI definition of open access. By submitting an article to any of the scientific journals of the UTE University, the author or authors accept these conditions.
The UTE applies the Creative Commons Attribution (CC-BY) license to articles in its scientific journals. Under this open access license, as an author you agree that anyone may reuse your article in whole or in part for any purpose, free of charge, including commercial purposes. Anyone can copy, distribute or reuse the content as long as the author and original source are correctly cited. This facilitates freedom of reuse and also ensures that content can be extracted without barriers for research needs.
This work is licensed under a Creative Commons Attribution 3.0 International (CC BY 3.0).
The Enfoque UTE journal guarantees and declares that authors always retain all copyrights and full publishing rights without restrictions [© The Author(s)]. Acknowledgment (BY): Any exploitation of the work is allowed, including a commercial purpose, as well as the creation of derivative works, the distribution of which is also allowed without any restriction.