Large language models: Expectations for semantics-driven systems engineering

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.

Schlagwörter

Fachgebiet (DDC)

330 - Wirtschaft

Projekt

Veranstaltung

Startdatum der Ausstellung

Enddatum der Ausstellung

Startdatum der Konferenz

Enddatum der Konferenz

Datum der letzten Prüfung

ISBN

ISSN

0169-023X
1872-6933

Sprache

Englisch

Während FHNW Zugehörigkeit erstellt

Ja

Zukunftsfelder FHNW

Publikationsstatus

Veröffentlicht

Begutachtung

Peer-Review der ganzen Publikation

Open Access-Status

Closed

Lizenz

Zitation

BUCHMANN, Robert, Johann EDER, Hans-Georg FILL, Ulrich FRANK, Dimitris KARAGIANNIS, Emanuele LAURENZI, John MYLOPOULOS, Dimitris PLEXOUSAKIS und Maribel Yasmina SANTOS, 2024. Large language models: Expectations for semantics-driven systems engineering. Data & Knowledge Engineering. 2024. Bd. 152, S. 102324. DOI 10.1016/j.datak.2024.102324. Verfügbar unter: https://irf.fhnw.ch/handle/11654/48427

Komplettanzeige