AI Assisted Domain Modeling Explainability and Traceability

Domain Models are abstract representations of selected elements in a domain that is created in a collaborative process between domain and modeler experts. The participants share domain knowledge to conceptualize and reason about the elements that will create the domain models. Through this exchange, a comprehensive and accurate representation of the domain is achieved, ensuring that the model captures the relevant aspects and relationships in the domain. Research in Artificial Intelligence (AI) has explored various methods to assist in the creation of domain models from text using Natural Language Processing (NLP) and Machine Learning (ML). Recent advancements with Large Language Models (LLMs) have shown that it is possible to create domain models using prompting techniques; however, the generated domain models contain errors and remain constrained by the performance of the LLM used.

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Publication Details

Published
2024-09-22
DOI
https://doi.org/10.1145/3652620.3688197
Citations
2
Primary Topic
Scientific Computing and Data Management
Type
article
Field-Weighted Citation Impact
2.10
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article

AI Assisted Domain Modeling Explainability and Traceability

Jonathan Silva Mercado
2 citations
Scientific Computing and Data Management
2.10
article

AI Assisted Domain Modeling Explainability and Traceability

Jonathan Silva Mercado
article en
2 citations

Abstract

Domain Models are abstract representations of selected elements in a domain that is created in a collaborative process between domain and modeler experts. The participants share domain knowledge to conceptualize and reason about the elements that will create the domain models. Through this exchange, a comprehensive and accurate representation of the domain is achieved, ensuring that the model captures the relevant aspects and relationships in the domain. Research in Artificial Intelligence (AI) has explored various methods to assist in the creation of domain models from text using Natural Language Processing (NLP) and Machine Learning (ML). Recent advancements with Large Language Models (LLMs) have shown that it is possible to create domain models using prompting techniques; however, the generated domain models contain errors and remain constrained by the performance of the LLM used.

University of Luxembourg (LU)
Openalex Percentile: Top 8%
Scientific Computing and Data Management
2.10
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