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Automated ontology generation from a plain text using statistical and NLP techniques

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Abstract

Major portion of web pages contains the natural language text and understanding of natural language text from the web pages is a major challenge for machines. Due to this lacking search engines are not able to provide relevant information to the users. This problem is tackled by natural language processing techniques and the development of ontologies from natural language text. With the help of such ontologies search of information can increases manifold. Much research in the field of text processing and automatic ontology building from text has been done to address these challenges. The proposed method in this paper is another effort to build automatic ontology from domain specific text. In this method we first extract concepts from a given domain specific text. We have used a Stanford parser to parse the text and the dictionary of basic concepts is created manually containing all the domain specific concepts and their relationships by recognizing laxico-syntactic patterns in the text corpus. Once concepts and relations among concepts as well as properties of concepts are identified, the extracted information can be represented in the form of graph and OWL.

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Author information

Authors and Affiliations

  1. AIIT, Amity University, Noida, India

    Naresh Kumar, Minakshi Kumar & Manjeet Singh

  2. YMCA University of Science and Technology, Faridabad, India

    Naresh Kumar, Minakshi Kumar & Manjeet Singh

Authors
  1. Naresh Kumar
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  2. Minakshi Kumar
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  3. Manjeet Singh
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Correspondence to Naresh Kumar.

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Appendix

Appendix

See Tables 1, 2, 3, 4, 5 and 6.

Table 4 Rules for generating nested concepts
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Table 5 Dictionary of valid concepts for computer domain
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Table 6 Dictionary of valid properties for computer domain
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Kumar, N., Kumar, M. & Singh, M. Automated ontology generation from a plain text using statistical and NLP techniques. Int J Syst Assur Eng Manag 7 (Suppl 1), 282–293 (2016). https://doi.org/10.1007/s13198-015-0403-1

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  • DOI: https://doi.org/10.1007/s13198-015-0403-1

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