Ontology Extraction from Software Requirements Using Named-Entity Recognition

• Autorzy: Kocerka Jerzy , Krześlak Michał , Gałuszka Adam

Identyfikatory

DOI

10.12913/22998624/149941

• Języki publikacji: EN

Abstrakty

EN

With the software playing a key role in most of the modern, complex systems it is extremely important to create and keep the software requirements precise and non-ambiguous. One of the key elements to achieve such a goal is to define the terms used in a requirement in a precise way. The aim of this study is to verify if the commercially available tools for natural language processing (NLP) can be used to create an automated process to identify whether the term used in a requirement is linked with a proper definition. We found out, that with a relatively small effort it is possible to create a model that detects the domain specific terms in the software requirements with a precision of 87 %. Using such model it is possible to determine if the term is followed by a link to a definition.

Słowa kluczowe

EN

engineering requirements; ontology extraction; named-entity recognition; classification and terminology; terminology; natural language processing; NLP

• Wydawca: Lublin University of Technology ; Polish Society of Ecological Engineering (PTIE), Branch of PTIE in Lublin
• Czasopismo: Advances in Science and Technology. Research Journal
• Rocznik: 2022
• Tom: Vol. 16, no 3
• Strony: 207--212
• Opis fizyczny: Bibliogr. 16 poz., fig., tab.

Twórcy

autor

Kocerka Jerzy

• Department of Automatic Control and Robotics, Silesian University of Technology, ul. Akademicka 2A, 44-100 Gliwice, Poland

• https://orcid.org/0000-0003-4985-9636

autor

Krześlak Michał

• Tritem Sp. z o.o., Ligocka 103/7, 40-568 Katowice, Poland

autor

Gałuszka Adam

• Department of Automatic Control and Robotics, Silesian University of Technology, ul. Akademicka 2A, 44-100 Gliwice, Poland

• https://orcid.org/0000-0002-6176-0500

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).