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Large language models: Expectations for semantics-driven systems engineering.

Authors :
Buchmann, Robert
Eder, Johann
Fill, Hans-Georg
Frank, Ulrich
Karagiannis, Dimitris
Laurenzi, Emanuele
Mylopoulos, John
Plexousakis, Dimitris
Santos, Maribel Yasmina
Source :
Data & Knowledge Engineering. Jul2024, Vol. 152, pN.PAG-N.PAG. 1p.
Publication Year :
2024

Abstract

The hype of Large Language Models manifests in disruptions, expectations or concerns in scientific communities that have focused for a long time on design-oriented research. The current experiences with Large Language Models and associated products (e.g. ChatGPT) lead to diverse positions regarding the foreseeable evolution of such products from the point of view of scholars who have been working with designed abstractions for most of their careers - typically relying on deterministic design decisions to ensure systems and automation reliability. Such expectations are collected in this paper in relation to a flavor of systems engineering that relies on explicit knowledge structures, introduced here as "semantics-driven systems engineering". The paper was motivated by the panel discussion that took place at CAiSE 2023 in Zaragoza, Spain, during the workshop on Knowledge Graphs for Semantics-driven Systems Engineering (KG4SDSE). The workshop brought together Conceptual Modeling researchers with an interest in specific applications of Knowledge Graphs and the semantic enrichment benefits they can bring to systems engineering. The panel context and consensus are summarized at the end of the paper, preceded by a proposed research agenda considering the expressed positions. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
0169023X
Volume :
152
Database :
Academic Search Index
Journal :
Data & Knowledge Engineering
Publication Type :
Academic Journal
Accession number :
177854353
Full Text :
https://doi.org/10.1016/j.datak.2024.102324