Reusability ontology in business processes with similarity matching

Authors

  • Meida Cahyo Untoro Institut Teknologi Sepuluh Nopember
  • Riyanarto Sarno Institut Teknologi Sepuluh Nopember
  • Nurul Fajrin Ariyani Intitut Teknologi Sepuluh Nopember

DOI:

https://doi.org/10.26555/jifo.v12i1.a7175

Abstract

The working technology will provide information and knowledge. Information and technology can be developed in various ways, by reusing the technologies. In this study modeled the ontology of SOPs using protégé. Ontology will be matched between ontology A and B to obtain similarity and reuse ontology to create a more optimal ontology. Matching is a matching process between both ontologies to get the same value from both ontologies. Jaro-Winkler distance is used to find commonality between ontology. The result of the Jaro-Winkler distance has a value of 0 and 1, in matching will be obtained value close to 0 or 1. On matching ontology obtained two tests using 40% SPARQL query. In the test it uses Jaro-Winkler distance with a value of 0.67. This research yields matching value between ontology A and ontology B which is the same so that reuse ontology can be done for better ontology

References

Arwan, A., Sidiq, M., Priyambadha, B., Kristianto, H., & Sarno, R. (2013). Ontology and semantic matching for diabetic food recommendations. International Conference on Information Technology and Electrical Engineering: “Intelligent and Green Technologies for Sustainable Developmentâ€, ICITEE 2013. Doi.org/10.1109/ICITEED.2013.6676233.

Caldarola, E. G., Rinaldi, A. M., Picariello, A., (2015). An Approach to Ontology Integration for Ontology Reuse in Knowledge Based Digital Ecosystems. Conference: The 7th International ACM Conference on Management of computational and collective IntElligence in Digital EcoSystems (ACM MEDES'15). Doi.org/ 10.1145/2857218.2857219.

Fernández-López, M., Gómez-Pérez, A., & Suárez-Figueroa, M. C. (2013). Methodological guidelines for reusing general ontologies. Data and Knowledge Engineering. Doi.org/10.1016/j.datak.2013.03.006.

Tudorache, T., Nyulas, C., Noy, N. F., & Musen, M. A. (2013). WebProtege: A collaborative ontology editor and knowledge acquisition tool for the web. Semantic Web. Doi.org/10.3233/SW-2012-0057.

Jain, V., & Singh, M. (2013). Ontology Based Information Retrieval in Semantic Web: A Survey. International Journal of Information Technology and Computer Science. Doi.org/10.5815/ijitcs.2013.10.06.

Kunaefi, A., & Sarno, R. (2013). Ontology Mapping for Erp Business Process. Seminar Nasional Teknologi Informasi Dan Multimedia. Ojs.amikom.ac.id/index.php/semnasteknomedia/article/view/616.

Gandon, F. L. (2013). Ontologies in Computer Science. Ontology Theory, Management and Design. Doi.org/10.4018/978-1-61520-859-3.ch001.

Hayuhardhika, W., Putra, N., Sugiyanto, Sarno, R., & Sidiq, M. (2013). Weighted Ontology and weighted tree similarity algorithm for diagnosing Diabetes Mellitus. International Conference on Computer, Control, Informatics and Its Applications: “Recent Challenges in Computer, Control and Informaticsâ€, IC3INA. Doi.org/10.1109/IC3INA.2013.6819185.

D. Corsar and D. Sleeman, “Reusing JessTab rules in Protégé,†AI 2005 SI. Doi.org/10.1016/j.knosys.2005.11.010.

Hazber, M. A. G., Li, R., Gu, X., Xu, G., & Li, Y. (2015). Semantic SPARQL Query in a Relational Database Based on Ontology Construction. 2015 11th International Conference on Semantics, Knowledge and Grids (SKG). Doi.org/10.1109/SKG.2015.14.

Kurz, T., Schlegel, K., & Kosch, P. H. (2015). Enabling Access to Linked Media with SPARQL-MM. Doi.org/10.1145/2740908.2742914.

Meditskos, G., Dasiopoulou, S., & Kompatsiaris, I. (2016). MetaQ: A knowledge-driven framework for context-aware activity recognition combining SPARQL and OWL 2 activity patterns. Pervasive and Mobile Computing. Doi.org/10.1016/j.pmcj.2015.01.007.

Chiba, H., & Uchiyama, I. (2017). SPANG: a SPARQL client supporting generation and reuse of queries for distributed RDF databases. BMC Bioinformatics. Doi.org/10.1186/s12859-017-1531-1.

Xiang, Z., Courtot, M., Brinkman, R. R., Ruttenberg, A., & He, Y. (2010). OntoFox: web-based support for ontology reuse. BMC Research Notes. Doi.org/10.1186/1756-0500-3-175.

Didih Rizki Chandranegara, Riyanarto Sarno. (2016). Ontology Alignment using Combined Similarity Method and Matching Method. Informatics and Computing (ICIC), International Conference on. DOI:10.1109/IAC.2016.7905722.

Sun, Y. (2015). A Comparative Evaluation of String Similarity Metrics for Ontology Alignment. Journal of Information and Computational Science. Doi.org/10.12733/jics20105420.

Zarembo, I., Teilans, A., Rausis, A., & Buls, J. (2015). Assessment of name based algorithms for land administration ontology matching. Procedia Computer Science. Doi.org/10.1016/j.procs.2014.12.008.

Drebler, K., & Ngomo, A. C. N. (2017). On the efficient execution of bounded Jaro-Winkler distances. Semantic Web. Doi.org/10.3233/SW-150209.

Maree, M., & Belkhatir, M. (2015). Addressing semantic heterogeneity through multiple knowledge base assisted merging of domain-specific ontologies. Knowledge-Based Systems. Doi.org/10.1016/j.knosys.2014.10.001.

www.w3.org/standards/semanticweb/ontology.

Euzenat, J., Shavaiko, P. (2010). Ontology matching second Edition. Dl.acm.org/citation.cfm?id=1951780.

Downloads

Published

2019-01-15

Issue

Section

Articles