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A Survey on Methods of Ontology Learning from Text

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Intelligent Computing Paradigm and Cutting-edge Technologies (ICICCT 2019)

Part of the book series: Learning and Analytics in Intelligent Systems ((LAIS,volume 9))

Abstract

Ontologies continue to emerge in performing a greater function in various business processes and knowledge sharing in the field of modern information systems. They help to exchange and extend, from syntax to semantic, data and knowledge. They are viewed as a remedy for interoperable semantics which power semantic web. Since the turn of the millennium, ontology learning (OL) from text has received a considerable attention with a sudden surge of textual information for promising research. Remarkably, with intermingling of various disciplines like text mining (TM), statistical analysis techniques (SAT), machine learning (ML), knowledge representation (KR), natural language processing (NLP), etc., more research is on in the area of Ontological Engineering and OL from text. This survey brings out several outcomes of foregone research, discusses challenges and limitations of research in the past decade and points to new challenges in future.

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Authors and Affiliations

  1. Department of Computer Science, Sacred Heart College, Tirupattur, 635 601, Tamil Nadu, India

    Ravi Lourdusamy & Stanislaus Abraham

Authors
  1. Ravi Lourdusamy
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  2. Stanislaus Abraham
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Corresponding author

Correspondence to Stanislaus Abraham .

Editor information

Editors and Affiliations

  1. University of Technology Sydney, Sydney, Australia

    Lakhmi C. Jain

  2. CSIE Department, National Dong Hwa University, New Taipei City, Taiwan

    Sheng-Lung Peng

  3. Al-Balqa’ Applied University, Salt, Jordan

    Basim Alhadidi

  4. Department of Computer Science, Brainware University, Kolkata, West Bengal, India

    Souvik Pal

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Lourdusamy, R., Abraham, S. (2020). A Survey on Methods of Ontology Learning from Text. In: Jain, L., Peng, SL., Alhadidi, B., Pal, S. (eds) Intelligent Computing Paradigm and Cutting-edge Technologies. ICICCT 2019. Learning and Analytics in Intelligent Systems, vol 9. Springer, Cham. https://doi.org/10.1007/978-3-030-38501-9_11

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