SpringerLink
Log in

KIASOntoRec: A Knowledge Infused Approach for Socially Aware Ontology Recommendation

  • Conference paper
  • First Online:
Innovations in Bio-Inspired Computing and Applications (IBICA 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 649))

  • 556 Accesses

Abstract

The World Wide Web is progressively adopting web 3.0 standards, and there is a demand for semantically compatible recommendation techniques in today’s world. This paper presents a framework to facilitate Knowledge infused automatic Ontology recommendation. User queries and user clicks data are preprocessed and Structural Topic Modelling is employed to identify the latent sematic structure found in the corpus and generate topics. RDF triplets are generated based on the topics extracted using OntoCollab. RDF data along with the Google’s Knowledge Base API used to obtain the subgraphs and relevant topics. Bagging algorithm based on Random Forest Classification Technique with Decision Trees Algorithm is used to classify the terms. A knowledge base is constructed by merging the subgraphs. Semantic similarity among entities of the knowledge graph and the top 25% of the classified results is computed using SemantoSim and ANOVA. The topic set is extracted and subjected to axiomatization. Finally, the most relevant Ontology to the user query is suggested to the user.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /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 (Canada)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (Canada)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (Canada)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Giri, G.L., Deepak, G., Manjula, S.H., Venugopal, K.R.: OntoYield: a semantic approach for context-based ontology recommendation based on structure preservation. In: Chaki, N., Cortesi, A., Devarakonda, N. (eds.) Proceedings of International Conference on Computational Intelligence and Data Engineering. Lecture Notes on Data Engineering and Communications Technologies, vol. 9, pp. 265–275. Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-6319-0_22

    Chapter  Google Scholar 

  2. Dhandi, M., Chakrawarti, R.K.: A comprehensive study of web usage mining. In: 2016 Symposium on Colossal Data Analysis and Networking (CDAN), pp. 1–5. IEEE (March 2016)

    Google Scholar 

  3. Hameed, M.A., Al Jadaan, O., Ramachandram, S.: Collaborative filtering based recommendation system: A survey. Int. J. Comput. Sci. Eng. 4(5), 859 (2012)

    Google Scholar 

  4. Zhang, Y., Meng, Y., Huang, J., Xu, FF., Wang, X., Han, J.: Minimally supervised categorization of text with metadata. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1231–1240 25 Jul 2020

    Google Scholar 

  5. Franz, N.M.: Biological taxonomy and ontology development: scope and limitations. Biodivers. Inform. 7(1), 45–66 (2010)

    Google Scholar 

  6. Sinha, M., Reddy, M., Bhattacharyya, P.: An approach towards construction and application of multilingual indo-wordnet. In: 3rd Global Wordnet Conference (GWC 2006), Jeju Island, Korea, Jan 2006 

    Google Scholar 

  7. Speiser, J.L., Miller, M.E., Tooze, J., Ip, E.: A comparison of random forest variable selection methods for classification prediction modeling. Expert Syst. Appl. 134, 93–101 (2019)

    Article  Google Scholar 

  8. Pushpa, C.N., Deepak, G., Thriveni, J., Venugopal, K.R.: A hybridized framework for Ontology modeling incorporating latent semantic analysis and content-based filtering. Int. J. Comput. Appl. 150(11), 33–41 (2016)

    Google Scholar 

  9. Iwendi, C., Ponnan, S., Munirathinam, R., Srinivasan, K., Chang, C.Y.: An efficient and unique TF/IDF algorithmic model-based data analysis for handling applications with big data streaming. Electronics 8(11), 1331 (2019)

    Article  Google Scholar 

  10. Surya, D., Deepak, G., Santhanavijayan, A.: KSTAR: a knowledge based approach for socially relevant term aggregation for web page recommendation. In: Motahhir, S., Bossoufi, B. (eds.) Digital Technologies and Applications. Lecture Notes in Networks and Systems, vol. 211, pp. 555–564. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-73882-2_50

    Chapter  Google Scholar 

  11. Zhan, C., Zheng, Y., Zhang, H., Wen, Q.: Random-forest-bagging broad learning system with applications for COVID-19 Pandemic. IEEE Internet Things J. 8(21), 15906–15918 (2021)

    Google Scholar 

  12. Sejal, D., Ganeshsingh, T., Venugopal, K.R., Iyengar, S.S., Patnaik, L.M.: Image recommendation based on ANOVA cosine similarity. Procedia Comput. Sci. 89, 562–567 (2016)

    Article  Google Scholar 

  13. Surya, D., Deepak, G., Santhanavijayan, A.: QFRDBF: query facet recommendation using knowledge centric DBSCAN and firefly optimization. In: Motahhir, S., Bossoufi, B. (eds.) Digital Technologies and Applications. Lecture Notes in Networks and Systems, vol. 211, pp. 801–811. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-73882-2_73

    Chapter  Google Scholar 

  14. Kumar, N., Deepak, G., Santhanavijayan, A.: A novel semantic approach for intelligent response generation using emotion detection incorporating NPMI measure. Procedia Comput. Sci. 167, 571–579 (2020)

    Article  Google Scholar 

  15. Arulmozhivarman, M., Deepak, G.: OWLW: ontology focused user centric architecture for web service recommendation based on LSTM and whale optimization. In: Musleh Al-Sartawi, A.M.A., Razzaque, A., Kamal, M.M. (eds.) EAMMIS 2021. LNNS, vol. 239, pp. 334–344. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-77246-8_32

    Chapter  Google Scholar 

  16. Kaushik, I.S., Deepak, G., Santhanavijayan, A.: QuantQueryEXP: a novel strategic approach for query expansion based on quantum computing principles. J. Discrete Math. Sci. Crypt. 23(2), 573–584 (2020)

    MathSciNet  MATH  Google Scholar 

  17. Deepak, G., Kasaraneni, D.: OntoCommerce: an ontology focused semantic framework for personalised product recommendation for user targeted e-commerce. Int. J. Comput. Aided Eng. Technol. 11(4–5), 449–466 (2019)

    Article  Google Scholar 

  18. Yethindra, D.N., Deepak, G.: A semantic approach for fashion recommendation using logistic regression and ontologies. In: 2021 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), pp. 1–6 September 2021

    Google Scholar 

  19. Chandramouli, A., Deepak, G.: OntoFusionCrop: an ontology centric approach for crop recommendation based on bagging and semantic alignment. In: Motahhir, S., Bossoufi, B. (eds.) Digital Technologies and Applications, vol. 2, pp. 210–219. Springer International Publishing, Cham (2022). https://doi.org/10.1007/978-3-031-02447-4_22

    Chapter  Google Scholar 

  20. Agrawal, D., Deepak, G.: HSIL: hybrid semantic infused learning approach for course recommendation. In: Motahhir, S., Bossoufi, B. (eds.) Digital Technologies and Applications, vol. 1, pp. 417–426. Springer International Publishing, Cham (2022). https://doi.org/10.1007/978-3-031-01942-5_42

    Chapter  Google Scholar 

  21. Chhatwal, G.S., Deepak, G.: Integrative KnowGen: integrative knowledge base generation for criminology as a domain of choice. In: Motahhir, S., Bossoufi, B. (eds.) Digital Technologies and Applications, vol. 2, pp. 475–484. Springer International Publishing, Cham (2022). https://doi.org/10.1007/978-3-031-02447-4_49

    Chapter  Google Scholar 

  22. Srivastava, R.A., Deepak, G.: Semantically driven machine learning-infused approach for tracing evolution on software requirements. In: Shukla, S., Gao, X.-Z., Kureethara, J.V., Mishra, D. (eds.) Data Science and Security, pp. 31–41. Springer Nature Singapore, Singapore (2022). https://doi.org/10.1007/978-981-19-2211-4_3

    Chapter  Google Scholar 

  23. Nachiappan, R., Deepak, G.: OSIBR: ontology focused semantic intelligence approach for book recommendation. In: Motahhir, S., Bossoufi, B. (eds.) Digital Technologies and Applications, vol. 1, pp. 397–406. Springer International Publishing, Cham (2022). https://doi.org/10.1007/978-3-031-01942-5_40

    Chapter  Google Scholar 

  24. Shaw, H., Deepak, G.: MetaBlog: a metadata driven semantics aware approach for blog tagging. In: Motahhir, S., Bossoufi, B. (eds.) Digital Technologies and Applications, vol. 2, pp. 406–414. Springer International Publishing, Cham (2022). https://doi.org/10.1007/978-3-031-02447-4_42

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, Indian Institute of Technology, Delhi, India

    Aastha Valecha

  2. Department of Computer Science and Engineering, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, India

    Gerard Deepak

  3. Department of Computer Science and Engineering, National Institute of Technology, Tiruchirappalli, India

    Deep ak Surya

Authors
  1. Aastha Valecha
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gerard Deepak
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Deep ak Surya
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gerard Deepak .

Editor information

Editors and Affiliations

  1. Faculty of Computing and Data Science, Flame University, Pune, Maharashtra, India

    Ajith Abraham

  2. Computer Science and Engineering Department, Thapar Institute of Engineering and Technology, Patiala, Punjab, India

    Anu Bajaj

  3. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs, Auburn, WA, USA

    Niketa Gandhi

  4. Interdisciplinary Studies Research Center (ISRC), Institute of Engineering, Polytechnique of Porto (ISEP/P.PORTO), INOV (Institute for Systems and Computer Engineering, Technology and Science), Porto, Portugal

    Ana Maria Madureira

  5. Department of Industrial Engineering, Istanbul Technical University, Istanbul, Türkiye

    Cengiz Kahraman

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Valecha, A., Deepak, G., Surya, D.a. (2023). KIASOntoRec: A Knowledge Infused Approach for Socially Aware Ontology Recommendation. In: Abraham, A., Bajaj, A., Gandhi, N., Madureira, A.M., Kahraman, C. (eds) Innovations in Bio-Inspired Computing and Applications. IBICA 2022. Lecture Notes in Networks and Systems, vol 649. Springer, Cham. https://doi.org/10.1007/978-3-031-27499-2_36

Download citation

Publish with us

Policies and ethics

Search

Navigation