Skip to main content

Semiautomated Ontology Learning to Provide Domain-Specific Knowledge Search in Marathi Language

  • Conference paper
  • First Online:
Data Management, Analytics and Innovation


In this research work, our goal is to build a self-sustainable, reproducible, and extensive domain-specific ontology for the purposes of creating a knowledge search engine. We have used online data as the primary information store using which we construct ontology by identifying concepts (nodes) and relationships between concepts. The project encompasses preestablished ideas gathered from successful NLP trials and presents a new variation to the task of ontology creation. The system, for which the ontology is being created, is a knowledge search engine in Marathi. This aims at building semiautomated ontology whose target demographic is primary school children and the selected domain is science domain. This project proposes a method to build semiautomated ontology. We use a combination of natural language processing method and machine learning method to automate the ontology learning task. Automatically learned ontology is further modified by language and domain experts to enrich the contents of ontology. Unlike, standard search engines, our knowledge search engine attempts to provide learned resources directly to the user rather than website links. This approach enables the user to directly get information without having to spend time on browsing indexed links.

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

Access this chapter

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

USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others


  1. Maedche, A.: Ontology Learning for the Semantic Web. Kluwer Academic Publishers, Amsterdam (2002)

    Book  Google Scholar 

  2. Maynard, D., Funk, A., Peters, W.: Using Lexico-syntactic ontology design patterns for ontology creation and population. In: Proceedings of the 2009 International Conference on Ontology Patterns, vol. 516 (2009)

    Google Scholar 

  3. McDonald, R.: Extracting relations from unstructured text. UPenn CIS Technical Report (2004)

    Google Scholar 

  4. Maynard, D., Tablan, V., Ursu, C., Cunningham, H., Wilks, Y.: Named entity recognition from diverse text types. In: Recent Advances in Natural Language Processing Conference (2001)

    Google Scholar 

  5. Navigli, R., Velardi, P., Cucchiarelli, A., Neri, F.: Automatic ontology learning: supporting a per concept evaluation by domain experts

    Google Scholar 

  6. Maynard, D., Funk, A., Peters, W: SPRAT: a tool for automatic semantic pattern-based ontology population

    Google Scholar 

  7. Carlson, A., Betteridge, J., Kisiel, B., Settles, B., Hruschka, Jr., E.R., Mitchell, T.M.: Toward an architecture for never-ending language learning. In: Association for the Advancement of Artificial Intelligence (2010)

    Google Scholar 

  8. Davulcu, H., Vadrevu, S., Nagarajan, S.: OntoMiner: bootstrapping and populating ontologies from domain specific web sites

    Google Scholar 

  9. El-gaya, M.M., Mekky, N., Atwan, A.: Efficient proposed framework for semantic search engine using new semantic ranking algorithm. Int. J. Adv. Comput. Sci. Appl. 6(8), P136–P143 (2015)

    Google Scholar 

  10. Cai, B., Li, Y.: Design and development of semantic-based search engine model. In: 7th International Conference on Intelligent Computation Technology and Automation, pp. 145–148 (2014)

    Google Scholar 

  11. Dilek, S., Karacan, H., Jahangiri, N., Afzali, S.: Ontology Creation for an Educational Center 978-1-4673-1740-5/12/©2012 IEEE

    Google Scholar 

  12. Saggion, H., Funk, A., Maynard, D., Bontcheva, K.: Ontology-based information extraction for business applications. In: Proceedings of the 6th International Semantic Web Conference (ISWC 2007), Busan, Korea (2007)

    Google Scholar 

  13. Hearst, M.A.: Automatic acquisition of hyponyms from large text corpora. In: Conference on Computational Linguistics (COLING’92), Nantes, France. Association for Computational Linguistics (1992)

    Google Scholar 

  14. Pantel, P., Pennacchioni, M.: Espresso: leveraging generic patterns for automatically harvesting semantic relations. In: Proceedings of Conference on Computational Linguistics/Association for Computational Linguistics (COLING/ACL-06), Sydney, Australia, pp. 113–120 (2006)

    Google Scholar 

  15. Pantel, P., Ravichandran, D.: Automatically labeling semantic classes. In: Proceedings of HLT/NAACL-04, Boston, MA, pp. 321–328 (2004)

    Google Scholar 

  16. Mikolov, T., Yih, W.T., Zweig, G.: Linguistic regularities in continuous space word representations. In: NAACL HLT (2013)

    Google Scholar 

  17. Bach, N., Badaskar, S.: A review of relation extraction. Literature review for language and statistics II (2007)

    Google Scholar 

  18. Saranya, K., Jayanthy, S.: Onto-based sentiment classification using machine learning techniques. In: International Conference on Innovations in information Embedded and Communication Systems (2017)

    Google Scholar 

  19. Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)

  20. Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., Kuksa, P.: Natural language processing (almost) from scratch. J. Mach. Learn. Res. 12, 24932537 (2011)

    MATH  Google Scholar 

  21. Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian network classifiers. Mach. Learn. 29(2–3), 131–163 (1997)

    Article  Google Scholar 

  22. Liu, J., Qin, L., Wang, H.: An ontology mapping method based on support vector machine

    Google Scholar 

  23. Johnson, I., Ab, J., Charnomordic, B., Destercke, S., Thomopoulos, R.: Making ontology-based knowledge and decision trees interact: an approach to enrich knowledge and increase expert confidence in data-driven models

    Google Scholar 

  24. Chieu, H.L., Ng, H.T.: Named entity recognition: a maximum entropy approach using global information. In: Proceedings of the 19th International Conference on Computational Linguistics, vol. 1 (2002)

    Google Scholar 

  25. Siddharthan, A.: An architecture for a text simplification system. In: Proceedings of the Language Engineering Conference (LEC’02) 0-7695-1885-0/02© 2002 IEEE

    Google Scholar 

Download references


This work was supported by the Maharashtra Government Project funded by the Rajiv Gandhi Science & Technology Commission Mumbai. We also thank to Marathi Vidnyan Parishad, Pune Vibhag for their immense help in writing Marathi definitions.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Neelam Chandolikar .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Chandolikar, N., Joglekar, P., Bhosale, S., Peddawad, D., Jalnekar, R., Shilaskar, S. (2020). Semiautomated Ontology Learning to Provide Domain-Specific Knowledge Search in Marathi Language. In: Sharma, N., Chakrabarti, A., Balas, V. (eds) Data Management, Analytics and Innovation. Advances in Intelligent Systems and Computing, vol 1042. Springer, Singapore.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-32-9948-1

  • Online ISBN: 978-981-32-9949-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics