Large Language Models:Expectations for Semantics-Driven Systems Engineering

Dimitris Karagiannis, Robert Andrei Buchmann , Johann Eder, Hans-Georg Fill, Ulrich Frank, Emanuele Laurenzi, John Mylopoulos, Dimitris Plexousakis, Maribel Yasmina Santos

Publications: Electronic/multimedia outputWeb publication

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 relying on explicit knowledge structures and 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.
Original languageEnglish
Publication statusPublished - 15 Nov 2023

Austrian Fields of Science 2012

  • 102028 Knowledge engineering

Cite this