Completeness of Knowledge in Models Extracted from Natural Text

Viktorija Gribermane, Erika Nazaruka

Abstract

Requirements given in the form of text in natural language are a widely used way of defining requirements for software. Various domain modeling approaches aim to extract domain models from the given natural text with different goals and output models. The article focuses on evaluating 17 approaches for domain model extraction based on the completeness of the extracted knowledge of the resulting target models. Criteria for the evaluation have been defined and a comparison has been given, which highlights the importance of including all three - functional, behavioral and structural information, in order to retain the most complete extracted knowledge.

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Paper Citation


in Harvard Style

Gribermane V. and Nazaruka E. (2021). Completeness of Knowledge in Models Extracted from Natural Text. In Proceedings of the 16th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE, ISBN 978-989-758-508-1, pages 114-125. DOI: 10.5220/0010454301140125


in Bibtex Style

@conference{enase21,
author={Viktorija Gribermane and Erika Nazaruka},
title={Completeness of Knowledge in Models Extracted from Natural Text},
booktitle={Proceedings of the 16th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE,},
year={2021},
pages={114-125},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010454301140125},
isbn={978-989-758-508-1},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE,
TI - Completeness of Knowledge in Models Extracted from Natural Text
SN - 978-989-758-508-1
AU - Gribermane V.
AU - Nazaruka E.
PY - 2021
SP - 114
EP - 125
DO - 10.5220/0010454301140125