Skip to main content
SpringerLink
Log in

Retrieval of Ontological Knowledge from Unstructured Text

  • Conference paper
  • First Online:
Machine Learning and Information Processing

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1101))

  • 1189 Accesses

Abstract

In this article, we examined the issue of automatic ontology formation process from unstructured text data. To understand the ontology of the domain, ontology should be expressed in terms of information tables and ontology graphs. Ontology graph consists of taxonomic and non-taxonomic relations. Non-taxonomic relations are easier to understand to non-expert users. Extracting non-taxonomic relations from ontology is a challenge. In order to improve ontology of the domain, appropriate machine learning classifier needs to be investigated for feature classification.

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
$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
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
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

References

  1. Castro, P.S., et al. 2017. Classifying short unstructured data using Apache Spark Platforms Eduardo. In ACM/IEEE Joint Conference.

    Google Scholar 

  2. Pratibha, P. 2014. Attribute based classification and annotation of unstructured data in social networks. In IEEE International Conference on Advanced Computing.

    Google Scholar 

  3. Bartoli, Alberto. 2017. Active learning of regular expressions for entity extraction. In IEEE transaction on Cybernetics.

    Google Scholar 

  4. Hassan, Abdalraouf. 2017. Convolutional recurrent deep learning model for sentence classification. Journal of IEEE Access.

    Google Scholar 

  5. Tekli, Joe. 2015. An overview on XML semantic disambiguation from unstructured text to semi-structured data: background, applications, and ongoing challenges. IIEEE Transaction on Knowledge and Data Engineering.

    Google Scholar 

  6. Leng, Jiewu, et al. 2016 Mining and matching relationships from interaction contexts in a social manufacturing paradigm. IEEE Transactions on Systems, Man, and Cybernetics.

    Google Scholar 

  7. Ritter et al. 2017. Toward application integration with multimedia data-Daniel. In IEEE International Enterprise Distributed Object Computing Conference.

    Google Scholar 

  8. Harleen. 2016. Analysis of hadoop performance and unstructured data using zeppelin. In IEEE International Conference on Research Advances in Integrated Navigation Systems.

    Google Scholar 

  9. Gabriele, et al. 2016. Mining unstructured data in software repositories: current and future trends. In IEEE International Conference on Software Analysis, Evolution and Reengineering.

    Google Scholar 

  10. Chiange, I-Jen. 2015. Agglomerative algorithm to discover semantics from unstructured big data. In IEEE International Conference on Big Data.

    Google Scholar 

  11. Bafna, Abhishek. 2015. Automated feature learning: mining unstructured data for useful abstractions. In IEEE International Conference on Data Mining.

    Google Scholar 

  12. Reyes Ortiz, Jose A., et al. 2015. Clinical decision support systems: a survey of NLP-based approaches from unstructured data. In IEEE International workshop on Database and Expert Systems Applications.

    Google Scholar 

  13. Fang, Yanwei, et al. 2015. Fast support for unstructured data processing: the unified automata processor. In ACM Proceedings of International Symposium on Microarchitecture.

    Google Scholar 

  14. Islam, Md. Rafiqul. 2014. An approach to provide security to unstructured big data. In IEEE International Conference on Software Knowledge, Information Management and Applications.

    Google Scholar 

  15. Shen, Wei, et al. 2018. SHINE+: a general framework for domain-specific entity linking with heterogeneous information networks. IEEE Transactions on Knowledge and Data Engineering.

    Google Scholar 

  16. Sriraghav, K. et al. 2017. ScrAnViz-A tool to scrap, analyze and visualize unstructured data using attribute based opinion mining algorithm. In IEEE International Conference on Innovations in Power and Advanced Computing Technologies.

    Google Scholar 

  17. Tarasconi, Frensacsco. 2017. The role of unstructured data in real time disaster related social media monitoring. In IEEE Inernational Conference on Big Data.

    Google Scholar 

  18. Ahmad, Tanvir, et al. 2016. Framework to extract context vectors from unstructured data using big data analytics. In IEEE Conference on Contemporary Computing.

    Google Scholar 

  19. Mohammad Fikry Abdullah et al. 2015. Business intelligence model for unstructured data management. In IEEE Conference on Electrical Engineering and Informatics.

    Google Scholar 

  20. Istephan, Sarmad, et al. 2015. Extensible query framework for unstructured medical data—a big data approach. In IEEE International Conference on Data Mining Workshops.

    Google Scholar 

  21. Lee, Saun, et al. 2014. A multi-dimensional analysis and data cube for unstructured text and social media. In IEEE International Conference on Big Data and Cloud Computing.

    Google Scholar 

  22. Saini, Akriti, et al. 2014. EmoXract: domain independent emotion mining model for unstructured data. In IEEE Conference on Contemprory Computing.

    Google Scholar 

  23. Ali, Mohamaed, et al. 2017. The problem learning non taxonomic relationships of ontology from unstructured data sources. In IEEE International Conference on Automation & Computing.

    Google Scholar 

  24. Rajpathak, Dnyanesh. 2014. An ontology-based text mining method to develop D-matrix from unstructured text. In IEEE Transactions on Systems, Man, and Cybernetics: Systems.

    Google Scholar 

  25. Krzysztof, et al. 2017. From unstructured data included in real estate listings to information systems over ontological graphs. In IEEE Conference on Information and Digital Technologies.

    Google Scholar 

  26. Sadoddin, Reza, et al. 2016. Mining and visualizing associations of concepts on a large-scale unstructured data. In IEEE Conference on Big Data Computing Service & Application.

    Google Scholar 

  27. Gianis, et al. 2017. Graph Based Information Exploration Over Structured and Unstructured Data. In IEEE Conference on Big Data.

    Google Scholar 

  28. Mallek, Maha, et al. 2017. Graphical representation of statistics hidden in unstructured data: a software application. In IEEE International Conference on Systems, Man & Cybernetics.

    Google Scholar 

  29. Alexandru, et al. 2017. Toward scalable indexing and search on distributed and unstructured data. In IEEE International Congress on Big Data.

    Google Scholar 

  30. Zhu, Chunying, et al. 2015. A combined index for mixed structured and unstructured data. In IEEE International Conference Web Information Systems.

    Google Scholar 

  31. Sheokand, Vishal, et al. 2016. Best effort query answering in data spaces on unstructured data. In IEEE International Conference on Computing, Communications and Automation.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. SKNCOE, Pune, India

    Dipak Pawar & Suresh Mali

Authors
  1. Dipak Pawar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Suresh Mali
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dipak Pawar .

Editor information

Editors and Affiliations

  1. Department of Computer Science, Rama Devi Women’s University, Bhubaneswar, Odisha, India

    Debabala Swain

  2. School of Computer Engineering, Kalinga Institute of Industrial Technology Deemed to be University, Bhubaneswar, Odisha, India

    Prasant Kumar Pattnaik

  3. Department of Computer Science and Engineering, Jaypee University of Information Technology, Waknaghat, Himachal Pradesh, India

    Pradeep K. Gupta

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

Pawar, D., Mali, S. (2020). Retrieval of Ontological Knowledge from Unstructured Text. In: Swain, D., Pattnaik, P., Gupta, P. (eds) Machine Learning and Information Processing. Advances in Intelligent Systems and Computing, vol 1101. Springer, Singapore. https://doi.org/10.1007/978-981-15-1884-3_47

Download citation

Publish with us

Policies and ethics

Search

Navigation