An approach to merge domain ontologies using granular computing


Granular computing is the emerging technique which performs data processing through making multiple levels of descriptions. Each level of description is expressed through granules or chunks of data also defined as information granules. The granule, the granule structure, and the granule layer are the heart of granular computing. Ontologies are vital information archives. On all disciplines of science and technology, ontologies are developed according to the requirements. Hence, the huge number of ontologies is available in the concerned domain which creates information duplication and storage problem. Merging of existing ontologies overcomes these issues. There are many merging approaches available. The existing approaches do not use granular computing for merging the ontologies. The proposed approach employs granular computing for merging the existing domain ontologies, thereby unifying multiple domain ontologies into a single representative domain ontology. For that, this research work proposes the following four granular computing processes, namely, association, isolation, purification, and reduction which can be applied over a group of similar nodes in the ontologies thereby unifying them. The proposed method achieves the ontology merging by performing two phases, namely similarity calculation phase and granular computing phase. The similarity calculation phase identifies the inter-label similarity between the labels of ontologies and computes the relevant group of nodes. Subsequently, granular computing applies association, isolation, purification, and reduction over a group of relevant nodes. The proposed approach is validated using the film industry and transportation domain ontologies and compared against its counterpart hybrid semantic similarity measure (HSSM). The results concluded that the proposed approach outperforms HSSM.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20


  1. Bargiela A, Pedrycz W (2003) Granular computing: an introduction. Kluwer Academic Publishers, Dordrecht

    Google Scholar 

  2. Calegari S, Ciucci D (2010) Granular computing applied to ontologies. Int J Approx Reason 51(4):391–409

    MATH  Article  Google Scholar 

  3. Ch AK (2011) Mining association rules using non-negative matrix factorization formal concept analysis. In: Venugopal KR, Patnaik LM (eds) International conference on information processing. Springer, Berlin, pp 31–39

    Google Scholar 

  4. Falcon R, Nápoles G, Bello R, Vanhoof K (2019) Granular cognitive maps: a review. Granul Comput 4(3):451–467

    Article  Google Scholar 

  5. Gomez-Perez A, Fernández-López M, Corcho O (2006) Ontological engineering: with examples from the areas of knowledge management, e-commerce the semantic web. Springer Science & Business Media, New York

    Google Scholar 

  6. Gruber TR (1993) A translation approach to portable ontology specifications. Knowl Acquisition 5(2):199–220

    Article  Google Scholar 

  7. Hovy E (1998) Combining and standardizing large-scale, practical ontologies for machine translation and other uses. In: Proceedings of the 1st international conference on language resources and evaluation (LREC), pp 535–542

  8. Huang C, Li J, Dias SM (2016) Attribute significance, consistency measure and attribute reduction in formal concept analysis. Neural Netw World 26(6):607–623

    Article  Google Scholar 

  9. Jyoti SD, Singh K (2015) Comparison of various similarity measure techniques for generating recommendations for e-commerce sites social websites. Am Int J Res Sci Technol Eng Math 11(2):219–221

    Google Scholar 

  10. Kumar C (2011) Knowledge discovery in data using formal concept analysis from projections. Int J Appl Math Comput Sci 21(4):745–756

    Article  Google Scholar 

  11. Kumar CA (2012) Fuzzy clustering-based formal concept analysis for association rules mining. Appl Artif Intell 26(3):274–301

    Article  Google Scholar 

  12. Kumar CA, Srinivas S (2010) Concept lattice reduction using fuzzy K-means clustering. Expert Syst Appl 37(3):2696–2704

    Article  Google Scholar 

  13. Li S, Lu Q, Li W (2005) Experiments of ontology construction with formal concept analysis. In: Huang C-R, Lenci A, Oltramari A (eds) Proceedings of OntoLex 2005—ontologies and lexical resources. Asian Federation of Natural Language Processing

  14. Li X, Qiu T, Liu Q, Bai X (2010) Ontology building from incomplete information system based on granular computing. In: 2010 IEEE international conference on granular computing. IEEE, pp 292–296

  15. Li J, Huang C, Xu W, Qian Y, Liu W (2015) Cognitive concept learning via granular computing for big data. In: 2015 international conference on machine learning and cybernetics (ICMLC), vol 1. IEEE, pp 289–294

  16. Li C, Li J, He M (2016) Concept lattice compression in incomplete contexts based on K-medoids clustering. Int J Mach Learn Cybern 7(4):539–552

    MathSciNet  Article  Google Scholar 

  17. Lisi FA, Mencar C (2018) A granular computing method for OWL ontologies. Fundam Inform 159(1–2):147–174

    MathSciNet  MATH  Article  Google Scholar 

  18. Liu Y, Jiang Y, Huang L (2010) Modeling complex architectures based on granular computing on ontology. IEEE Trans Fuzzy Syst 18(3):585–598

    Article  Google Scholar 

  19. Makwana A, Ganatra A (2018) A better approach to ontology integration using clustering through global similarity measure. JCS 14(6):854–867

    Google Scholar 

  20. Mao H, Hu M, Yao Y (2019) Algebraic approaches to granular computing. Granul Comput.

    Article  Google Scholar 

  21. Meng L, Huang R, Gu J (2013) A review of semantic similarity measures in wordnet. Int J Hybrid Inf Technol 6(1):1–12

    Google Scholar 

  22. Neches R, Fikes RE, Finin T, Gruber T, Patil R, Senator T, Swartout WR (1991) Enabling technology for knowledge sharing. AI Mag 12(3):36

    Google Scholar 

  23. Pawlak Zdzisław (1982) Rough sets. Int J Parallel Prog 11(5):341–356

    MATH  Google Scholar 

  24. Priya M, Kumar CA (2015) A survey of state of the art of ontology construction merging using formal concept analysis. Indian J Sci Technol 8(24):1–7

    Article  Google Scholar 

  25. Priya M, Kumar CA (2018) Construction and Merging of ACM and ScienceDirect Ontologies. In: Abraham A et al (eds) International conference on intelligent systems design and applications. Springer, pp 238–252

  26. Priya M, Kumar CA (2019a) A novel method for merging academic social network ontologies using formal concept analysis hybrid semantic similarity measure. Library Hi Tech, Emerald Publishing Limited, Bingley.

    Google Scholar 

  27. Priya M, Kumar CA (2019b) A novel method for merging ontologies using formal concept analysis. Int J Cloud Comput (article in press)

  28. Qi J, Wei L, Wan Q (2019) Multi-level granularity in formal concept analysis. Granul Comput 4(3):351–362

    Article  Google Scholar 

  29. Qiu T, Chen X, Huang H, Liu Q (2006) Ontology capture based on granular computing. In: Sixth international conference on intelligent systems design and applications, vol 1. IEEE, pp 770–774

  30. Qiu T, Chen X, Liu Q, Huang H (2007) A granular space model for ontology learning. In: 2007 IEEE international conference on granular computing (GRC 2007). IEEE p 61

  31. Rangel CR, Altamira J, Cerrada M, Aguilar J (2018) Procedure based on semantic similarity for merging ontologies by non-redundant knowledge enrichment. Int J Knowl Manag (IJKM) 14(2):16–36

    Article  Google Scholar 

  32. Sikder IU (2017) Application of granular computing paradigm in knowledge induction. World Acad Sci Eng Technol Int J Comput Electr Autom Control Inf Eng 11(10):1091–1094

    Google Scholar 

  33. Sologub GB (2011) On measuring of similarity between tree nodes. In: Young scientists conference in information retrieval, pp 63–71

  34. Varelas G, Voutsakis E, Raftopoulou P, Petrakis EG, Milios EE (2005) Semantic similarity methods in wordNet and their application to information retrieval on the web. In: Proceedings of the 7th annual ACM international workshop on web information and data management. ACM, pp 10–16

  35. Yan H, Zhang F, Liu B (2015) Granular computing based ontology learning model its applications. Cybern Inf Technol 15(6):103–112

    Google Scholar 

  36. Zadeh L (2008) Is there a need for fuzzy logic? Inf Sci 178:2751–2779

    MathSciNet  MATH  Article  Google Scholar 

  37. Zhou G, Liang J (2006) Granular computing model based on ontology. In: 2006 IEEE international conference on granular computing. IEEE, pp 321–324

Download references


Authors sincerely thank the anonymous reviewers for their useful insights. Also, authors thank the editor for the support. One of the author, Ch. Aswani Kumar, sincerely thank the seed grant support from VIT, Vellore.

Author information



Corresponding author

Correspondence to Ch. Aswani Kumar.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Priya, M., Aswani Kumar, C. An approach to merge domain ontologies using granular computing. Granul. Comput. 6, 69–94 (2021).

Download citation


  • Association
  • Granular computing
  • Isolation
  • Ontology merging
  • Purification
  • Reduction
  • Similarity measure