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

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

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

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2024

Confidentiality

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

Article name in the collection

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

ISBN

978-3-031-75872-0

ISSN

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e-ISSN

1611-3349

Number of pages

19

Pages from-to

235-253

Publisher name

Springer

Place of publication

Cham

Event location

PITTSBURGH, PENNSYLVANIA, USA

Event date

Oct 28, 2024

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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