Skip to main content

GFSOM: Genetic Feature Selection for Ontology Matching

  • Conference paper
  • First Online:
Genetic and Evolutionary Computing (ICGEC 2018)

Abstract

This paper studies the ontology matching problem and proposes a genetic feature selection approach for ontology matching (GFSOM), which exploits the feature selection using the genetic approach to select the most appropriate properties for the matching process. Three strategies are further proposed to improve the performance of the designed approach. The genetic algorithm is first performed to select the most relevant properties, and the matching process is then applied to the selected properties instead of exploring all properties of the given ontology. To demonstrate the usefulness and accuracy of the GFSOM framework, several experiments on DBpedia ontology database are conducted. The results show that the ontology matching process benefits from the feature selection and the genetic algorithm, where GFSOM outperforms the state-of-the-art ontology matching approaches in terms of both the execution time and quality of the matching process.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    http://wiki.dbpedia.org/Datasets.

  2. 2.

    http://wiki.dbpedia.org/Datasets.

References

  1. Smith, B., Ashburner, M., Rosse, C., Bard, J., Bug, W., Ceusters, W., et al.: The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration. Nat. Biotechnol. 25(11), 1251 (2007)

    Article  Google Scholar 

  2. Cerón-Figueroa, S., López-Yáñez, I., Alhalabi, W., Camacho-Nieto, O., Villuendas-Rey, Y., Aldape-Pérez, M., et al.: Instance-based ontology matching for e-learning material using an associative pattern classifier. Comput. Hum. Behav. 69, 218–225 (2017)

    Article  Google Scholar 

  3. Iwata, T., Kanagawa, M., Hirao, T., Fukumizu, K.: Unsupervised group matching with application to cross-lingual topic matching without alignment information. Data Min. Knowl. Discov. 31(2), 350–370 (2017)

    Article  MathSciNet  Google Scholar 

  4. Wang, J., Ding, Z., Jiang, C.: Gaom: genetic algorithm based ontology matching. In: IEEE Asia-Pacific Conference on Services Computing, 2006. APSCC’06, pp. 617–620. IEEE (2006)

    Google Scholar 

  5. Acampora, G., Loia, V., Salerno, S., Vitiello, A.: A hybrid evolutionary approach for solving the ontology alignment problem. Int. J. Intell. Syst. 27(3), 189–216 (2012)

    Article  Google Scholar 

  6. Martinez-Gil, J., Alba, E., Aldana-Montes, J.F.: Optimizing ontology alignments by using genetic algorithms. In: Proceedings of the Workshop on Nature Based Reasoning for the Semantic Web. Karlsruhe, Germany (2008)

    Google Scholar 

  7. Acampora, G., Loia, V., Vitiello, A.: Enhancing ontology alignment through a memetic aggregation of similarity measures. Inf. Sci. 250, 1–20 (2013)

    Article  Google Scholar 

  8. Xue, X., Chen, J.: Optimizing ontology alignment through hybrid population-based incremental learning algorithm. Memetic Comput. 1–9 2018

    Google Scholar 

  9. Djenouri, Y., Belhadi, A., Fournier-Viger, P., Lin, J.C.W.: Fast and effective cluster-based information retrieval using frequent closed itemsets. Inf. Sci. 453, 154–167 (2018)

    Article  MathSciNet  Google Scholar 

  10. Djenouri, Y., Djamel, D., Djenoouri, Z.: Data-mining-based decomposition for solving MAXSAT problem: towards a new approach. IEEE Intell. Syst. (2017)

    Google Scholar 

  11. Djenouri, Y., Belhadi, A., Fournier-Viger, P., Lin, J.C.W.: An hybrid multi-core/GPU-based mimetic algorithm for big association rule mining. In: International Conference on Genetic and Evolutionary Computing, pp. 59–65. Springer (2017)

    Google Scholar 

  12. Lin, J.C.W., Zhang, Y., Fournier-Viger, P., Djenouri, Y., Zhang, J.: A metaheuristic algorithm for hiding sensitive itemsets. In: International Conference on Database and Expert Systems Applications, pp. 492–498. Springer (2018)

    Google Scholar 

  13. Niu, X., Rong, S., Wang, H., Yu, Y.: An effective rule miner for instance matching in a web of data. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management, pp. 1085–1094. ACM (2012)

    Google Scholar 

  14. Shao, C., Hu, L.M., Li, J.Z., Wang, Z.C., Chung, T., Xia, J.B.: RiMOM-IM: a novel iterative framework for instance matching. J. Comput. Sci. Technol. 31(1), 185–197 (2016)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hiba Belhadi .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Belhadi, H., Akli-Astouati, K., Djenouri, Y., Lin, J.CW., Wu, J.MT. (2019). GFSOM: Genetic Feature Selection for Ontology Matching. In: Pan, JS., Lin, JW., Sui, B., Tseng, SP. (eds) Genetic and Evolutionary Computing. ICGEC 2018. Advances in Intelligent Systems and Computing, vol 834. Springer, Singapore. https://doi.org/10.1007/978-981-13-5841-8_68

Download citation

Publish with us

Policies and ethics