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     Research Journal of Applied Sciences, Engineering and Technology


Automatic Domain Knowledge Extraction from Requirements Specification Text

1S. Geetha, 2G.S. Anandha Mala and 3Suresh Kumar Sanampudi
1JNTUH, Hyderabad, India
2Department of CSE, Easwari Engineering College, Chennai
3Department of IT, JNTU College of Engineering Jagtial, Telangana, India
Research Journal of Applied Sciences, Engineering and Technology  2016  9:926-932
http://dx.doi.org/10.19026/rjaset.12.2810  |  © The Author(s) 2016
Received: October ‎9, ‎2015  |  Accepted: October ‎30, ‎2015  |  Published: May 05, 2016

Abstract

The automated generation of sequence of actions and schema extraction from requirements specification text written in natural language is really challenging and has limited explorations so far. It’s an attempt to automatically retrieving some useful elements from the requirements specification text, which is required for the software development process. This study proposes a system to simplify requirements analysis process and identify the interaction between the class objects using Natural Language Processing (NLP) techniques and crafted rules. The Automatic Domain Knowledge Extraction (ADKE) system does the step by step elicitation of classes, attributes, relationships, actors, relational schema, use cases and interaction between the classes. Finally deploys relational schema into the database, which allows user to acquire domain knowledge by applying queries and quick traceability link between the use cases. The experimental results of ADKE system are compared with other available tools and it gives better accuracy also.

Keywords:

Key attributes, natural language processing, sequence of actions, relational schema,


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Competing interests

The authors have no competing interests.

Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Copyright

The authors have no competing interests.

ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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