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Large language models: Expectations for semantics-driven systems engineering
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Author (Corporation)
Publication date
2024
Typ of student thesis
Course of study
Collections
Type
01A - Journal article
Editors
Editor (Corporation)
Supervisor
Parent work
Data & Knowledge Engineering
Special issue
DOI of the original publication
Link
Series
Series number
Volume
152
Issue / Number
Pages / Duration
102324
Patent number
Publisher / Publishing institution
Elsevier
Place of publication / Event location
Amsterdam
Edition
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Programming language
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Practice partner / Client
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.
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ISBN
ISSN
0169-023X
1872-6933
1872-6933
Language
English
Created during FHNW affiliation
Yes
Strategic action fields FHNW
Publication status
Published
Review
Peer review of the complete publication
Open access category
Closed
License
Citation
Buchmann, R., Eder, J., Fill, H.-G., Frank, U., Karagiannis, D., Laurenzi, E., Mylopoulos, J., Plexousakis, D., & Santos, M. Y. (2024). Large language models: Expectations for semantics-driven systems engineering. Data & Knowledge Engineering, 152, 102324. https://doi.org/10.1016/j.datak.2024.102324