Enhancing Domain Modeling with Pre-trained Large Language Models: An Automated Assistant for Domain Modelers

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F24%3A10488754" target="_blank" >RIV/00216208:11320/24:10488754 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1007/978-3-031-75872-0_13" target="_blank" >https://doi.org/10.1007/978-3-031-75872-0_13</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-75872-0_13" target="_blank" >10.1007/978-3-031-75872-0_13</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Enhancing Domain Modeling with Pre-trained Large Language Models: An Automated Assistant for Domain Modelers

Popis výsledku v původním jazyce

Domain modeling involves creating abstract representations of information within a specific domain using techniques such as conceptual modeling and ontology engineering. Traditionally, manual creation and maintenance of domain models are labor intensive and require modeling expertise. This paper explores the automation of domain modeling using pre-trained large language models (LLMs), presenting an experimental LLM-based conceptual modeling assistant that collaborates with a human expert. The assistant provides modeling suggestions based on a given textual description of the domain of interest, aiding in the design of classes, attributes, and associations. We present a generic framework for domain modeling assistants that consists of class, attribute, and association generators, and show how they can be implemented using an LLM. We demonstrate a concrete configuration of this framework and its prototype implementation. We evaluated the effectiveness of the framework configuration across various domains. Our findings indicate that the assistant significantly enhances the efficiency of modeling while maintaining reasonable quality of the outputs.

Název v anglickém jazyce

Enhancing Domain Modeling with Pre-trained Large Language Models: An Automated Assistant for Domain Modelers

Popis výsledku anglicky

Domain modeling involves creating abstract representations of information within a specific domain using techniques such as conceptual modeling and ontology engineering. Traditionally, manual creation and maintenance of domain models are labor intensive and require modeling expertise. This paper explores the automation of domain modeling using pre-trained large language models (LLMs), presenting an experimental LLM-based conceptual modeling assistant that collaborates with a human expert. The assistant provides modeling suggestions based on a given textual description of the domain of interest, aiding in the design of classes, attributes, and associations. We present a generic framework for domain modeling assistants that consists of class, attribute, and association generators, and show how they can be implemented using an LLM. We demonstrate a concrete configuration of this framework and its prototype implementation. We evaluated the effectiveness of the framework configuration across various domains. Our findings indicate that the assistant significantly enhances the efficiency of modeling while maintaining reasonable quality of the outputs.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ISBN

978-3-031-75872-0

ISSN

—

e-ISSN

1611-3349

Počet stran výsledku

19

Strana od-do

235-253

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

PITTSBURGH, PENNSYLVANIA, USA

Datum konání akce

28. 10. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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