Advertisement

A Comparative Application of Multi-criteria Decision Making in Ontology Ranking

  • Jean Vincent Fonou-DombeuEmail author
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 353)

Abstract

The number of available ontologies on the web has increased tremendously in recent years. The choice of suitable ontologies for reuse is a decision-making problem. However, there has been little use of decision-making on ontologies to date. This study applies three Multi-Criteria Decision Making (MCDM) algorithms in ontology ranking. A number of ontologies/alternatives and the complexity metrics or attributes of these ontologies are used in the decision. The experiments are carried out with 70 ontologies and the performance of the algorithms are analysed and compared. The results show that all the algorithms have successfully ranked the input ontologies based on their degree/level of complexity. Furthermore, the results portray a strong correlation between the ranking results of the three MCDM algorithms, thereby, providing more insights on the performance of MCDM algorithms in ontology ranking.

Keywords

Algorithms Biomedical ontologies Complexity metrics Multi-criteria Decision Making Ontology ranking Ontology reuse 

References

  1. 1.
    Trokanas, N., Cecelja, F.: Ontology evaluation for reuse in the domain of process systems engineering. Comput. Chem. Eng. 85, 177–187 (2016)CrossRefGoogle Scholar
  2. 2.
    Lonsdale, D., Embley, D.W., Ding, Y., Xu, L., Hepp, M.: Reusing ontologies and language components for ontology generation. Data Knowl. Eng. 69, 318–330 (2010)CrossRefGoogle Scholar
  3. 3.
    Bontas, E.P., Mochol, M., Tolksdorf, R.: Case studies on ontology reuse. In: 5th International Conference on Knowledge Management (I-Know 2005), Graz, Austria (2005)Google Scholar
  4. 4.
    Groza, A., Dragoste, I., Sincai, I., Jimborean, I., Moraru, V.: An ontology selection and ranking system based on the analytical hierarchy process. In: The 16th International Symposium on Symbolic and Numerical Algorithms for Scientific Computing, Timisoara, Romania (2014)Google Scholar
  5. 5.
    Esposito, A., Zappatore, M., Tarricone, L.: Applying multi-criteria approaches to ontology ranking: a comparison with AKtiveRank. Int. J. Metadata Semant. Ontol. 7, 197–208 (2012)CrossRefGoogle Scholar
  6. 6.
    Alani, H., Brewster, C., Shadbolt, N.: Ranking ontologies with AKTiveRank. In: Cruz, I., Decker, S., Allemang, D., Preist, C., Schwabe, D., Mika, P., Uschold, M., Aroyo, L.M. (eds.) ISWC 2006. LNCS, vol. 4273, pp. 1–15. Springer, Heidelberg (2006).  https://doi.org/10.1007/11926078_1CrossRefGoogle Scholar
  7. 7.
    Naskar, D., Dutta, B.: Ontology and ontology libraries: a study from an ontofier and an ontologist perspective. In: Proceedings of 19th International Symposium on Electronic Theses and Dissertations (ETD 2016 “Data and Dissertations”), Lille, France, pp. 1–12 (2016)Google Scholar
  8. 8.
    d’Aquin, M., Noy, N.F.: Where to publish and find ontologies? A survey of ontology libraries. Web Semant. Sci. Serv. Agents World Wide Web 11, 96–111 (2012)CrossRefGoogle Scholar
  9. 9.
    Ensan, F., Du, W.: A semantic metrics suite for evaluating modular ontologies. Inf. Syst. 38, 745–770 (2013)CrossRefGoogle Scholar
  10. 10.
    Zhang, H., Li, Y.F., Tan, H.B.K.: Measuring design complexity of semantic web ontologies. J. Syst. Softw. 83, 803–814 (2010)CrossRefGoogle Scholar
  11. 11.
    Liao, L., Shen, G., Huang, Z., Wang, F.: Cohesion metrics for evaluation semantic web ontologies. Int. J. Hybrid Inf. Technol. 9, 369–380 (2016)CrossRefGoogle Scholar
  12. 12.
    Leea, H.C., Chang, C.T.: Comparative analysis of MCDM methods for ranking renewable energy sources in Taiwan. Renew. Sustain. Energy Rev. 92, 883–896 (2018)CrossRefGoogle Scholar
  13. 13.
    Chou, J.R.: A weighted linear combination ranking technique for multi-criteria decision analysis. S. Afr. J. Econ. Manag. Sci. Spec. 16, 28–41 (2013)Google Scholar
  14. 14.
    Park, J., Ohb, S., Ahn, J.: Ontology selection ranking model for knowledge reuse. Expert. Syst. Appl. 38, 5133–5144 (2011)CrossRefGoogle Scholar
  15. 15.
    Sridevi, K., Umarani, R.: Ontology ranking algorithms on semantic web: a review. Int. J. Adv. Res. Comput. Commun. Eng. 2, 3471–3476 (2013)Google Scholar
  16. 16.
    Butt, A.S., Haller, A., Xie, L.: DWRank: learning concept ranking for ontology search. Semant. Web 7, 447–461 (2016)CrossRefGoogle Scholar
  17. 17.
    Yu, W., Cao, J., Chen, J.: A novel approach for ranking ontologies on the semantic web. In: 1st International Symposium on Pervasive Computing and Applications, Urumchi, Xinjiang, China, pp. 608–612 (2006)Google Scholar
  18. 18.
    Yu, W., Chen, J.: Ontology ranking for the semantic web. In: 3rd International Symposium on Intelligent Information Technology Application, NanChang, China, pp. 573–574 (2009)Google Scholar
  19. 19.
    Jones, M., Alani, H.: Content-based ontology ranking. In: 9th International Protégé Conference, Stanford, CA, USA, pp. 1–4 (2006)Google Scholar
  20. 20.
    Subhashini, R., Akilandeswari, J., Haris, S.: An integrated ontology ranking method for enhancing knowledge reuse. Int. J. Eng. Technol. (IJET) 6, 1424–1431 (2014)Google Scholar
  21. 21.
    Vrandečić, D., Sure, Y.: How to design better ontology metrics. In: Franconi, E., Kifer, M., May, W. (eds.) ESWC 2007. LNCS, vol. 4519, pp. 311–325. Springer, Heidelberg (2007).  https://doi.org/10.1007/978-3-540-72667-8_23CrossRefGoogle Scholar
  22. 22.
    Duque-Ramos, A., Boeker, M., Jansen, L., Schulz, S., Iniesta, M., Fernandez-Breis, J.T.: Evaluating the good ontology design guideline (GoodOD) with the ontology quality requirements and evaluation method and metrics (OQuaRE). PLoS One 9, 1–14 (2014)CrossRefGoogle Scholar
  23. 23.
    Zavadskas, E.K., Turskis, Z., Kildiene, S.: State of art surveys of overviews on MCDM/MADM methods. Technol. Econ. Dev. Econ. 20, 165–179 (2014)CrossRefGoogle Scholar
  24. 24.
    Taha, R.A., Daim, T.: Multi-criteria applications in renewable energy analysis, a literature review. Res. Technol. Manag. Electr. Ind. 8, 17–30 (2013)Google Scholar
  25. 25.
    Kumara, A., et al.: A review of multi criteria decision making (MCDM) towards sustainable renewable energy development. Renew. Sustain. Energy Rev. 69, 596–609 (2017)CrossRefGoogle Scholar
  26. 26.
    Mulliner, E., Malys, N., Maliene, V.: Comparative analysis of MCDM methods for the assessment of sustainable housing affordability. Omega 59, 146–156 (2016)CrossRefGoogle Scholar
  27. 27.
    Balcerzak, A.P., Pietrzak, M.B.: Application of TOPSIS method for analysis of sustainable development in European Union countries. In: Proceedings of 10th International Days of Statistics and Economics, Prague, Czech Republic, pp. 82–92 (2016)Google Scholar
  28. 28.
    Ochs, C., et al.: An empirical analysis of ontology reuse in BioPortal. J. Biomed. Inform. 71, 165–177 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Mathematics, Statistics and Computer ScienceUniversity of KwaZulu-NatalScottsville, PietermaritzburgSouth Africa

Personalised recommendations