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Leveraging LLMs in Semantic Mapping for Knowledge Graph-based Automated Enterprise Model Generation

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dc.contributor.authorReitemeyer, Benedikt
dc.contributor.authorFill, Hans-Georg
dc.contributor.editorGiese, Holger
dc.contributor.editorRosenthal
dc.contributor.editorKristina
dc.date.accessioned2024-03-12T05:30:27Z
dc.date.available2024-03-12T05:30:27Z
dc.date.issued2024
dc.description.abstractAutomated enterprise model generation applies artificial intelligence and other machine- processable approaches to improve decision making and adoption in complex and changing en- vironments. The emergence of Large Language Models (LLMs) opens a new playing field for machine-processability in enterprise modeling, especially when it comes to processing natural lan- guage contextual knowledge. In this extended abstract, we show the use of LLMs in semantic mapping tasks for real-world and modeling language concepts based on an ArchiMate and National Information Exchange Model (NIEM) example. The results indicate that LLMs are useful in automated enterprise modeling tasks.en
dc.identifier.doi10.18420/modellierung2024-ws-006
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/43780
dc.language.isoen
dc.pubPlaceBonn
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofModellierung 2024 Satellite Events
dc.subjectEnterprise Modeling
dc.subjectLarge Language Models
dc.subjectSemantic Mapping
dc.titleLeveraging LLMs in Semantic Mapping for Knowledge Graph-based Automated Enterprise Model Generationen
dc.typeText/Workshop Paper
gi.conference.date12. - 15. März
gi.conference.locationPotsdam
gi.conference.sessiontitleLLM4Modeling

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