Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-7776
Main Title: Knowledge Representation of Requirements Documents Using Natural Language Processing
Author(s): Schlutter, Aaron
Vogelsang, Andreas
Type: Conference Object
Language Code: en
Abstract: Complex systems such as automotive software systems are usually broken down into subsystems that are specified and developed in isolation and afterwards integrated to provide the functionality of the desired system. This results in a large number of requirements documents for each subsystem written by different people and in different departments. Requirements engineers are challenged by comprehending the concepts mentioned in a requirement because coherent information is spread over several requirements documents. In this paper, we describe a natural language processing pipeline that we developed to transform a set of heterogeneous natural language requirements into a knowledge representation graph. The graph provides an orthogonal view onto the concepts and relations written in the requirements. We provide a first validation of the approach by applying it to two requirements documents including more than 7,000 requirements from industrial systems. We conclude the paper by stating open challenges and potential application of the knowledge representation graph.
URI: https://depositonce.tu-berlin.de//handle/11303/8642
http://dx.doi.org/10.14279/depositonce-7776
Issue Date: 2018
Date Available: 4-Dec-2018
DDC Class: 004 Datenverarbeitung; Informatik
Subject(s): natural language processing
knowledge representation
automotive software
requirements
industrial systems
Stanford CoreNLP
Semantic Role Labeling
NLP
License: http://rightsstatements.org/vocab/InC/1.0/
Proceedings Title: REFSQ 2018 Joint Proceedings of the Co-Located Events – NPL4RE: 1st Workshop on Natural Language Processing for Requirements Engineering
Publisher: RWTH
Publisher Place: Aachen
Publisher DOI: 10.1186/s12911-017-0440-6
Series: CEUR workshop proceedings
Series Number: 2075
ISSN: 1613-0073
Appears in Collections:FG IT-basierte Fahrzeuginnovationen » Publications

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