Advances in Conceptual Modeling : ER 2025 Workshops, FCM, CMLS, LLM4Modeling, OntoCom, and QUAMES, Poitiers, France, October 20-23, 2025, Proceedings. (Lecture Notes in Computer Science)

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Advances in Conceptual Modeling : ER 2025 Workshops, FCM, CMLS, LLM4Modeling, OntoCom, and QUAMES, Poitiers, France, October 20-23, 2025, Proceedings. (Lecture Notes in Computer Science)

M. Fonseca, Claudenir (EDT)/ Bernasconi, Anna (EDT)/ de Cesare, Sergio (EDT)

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Springer Nature Switzerland AG（2025/11発売）
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ポイント 152pt

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製本 Paperback:紙装版/ペーパーバック版／ページ数 211 p.
言語 ENG
商品コード 9783032086198

Full Description

This book constitutes the refereed proceedings of the workshops held at the 44th International Conference on Conceptual Modeling, ER 2025, which took place in Poitiers, France, during October 20-23, 2025.

The 11 full papers and 2 other papers included in this book were carefully reviewed and selected from 26 submissions.

The highly dynamic nature of ER workshops helps shape the future of conceptual modeling and contributes to its impact across diverse domains of knowledge, from life sciences to engineering and business.

Five workshops were organized at ER 2025. These include:

- Fundamentals of Conceptual Modeling (FCM)

- 6th International Workshop on Conceptual Modeling for Life Sciences (CMLS)

- 3rd International Workshop on Modeling in the Age of Large Language Models (LLM4Modeling)

- 11th International Workshop on Ontologies and Conceptual Modeling (OntoCom)

- 6th International Workshop on Quality and Measurement of Model-Driven Software Development (QUAMES)

.- FCM.
.- Logical Foundations of Conceptual Modelling for Relational and SQL Databases - an introduction.
.- Disentangling the schema turn: Restoring the information base to conceptual modelling.
.- Representing Autopoiesis and Scanning the Environment in Fractal Enterprise Model.
.- Towards a fundamental theory of modeling discrete systems.
.- Towards Specifying "Model".
.- CMLS.
.- A conceptual model for discovering implicit temporal knowledge in clinical data.
.- LLM4Modeling.
.- Vibe Modeling: Challenges and Opportunities.
.- Model Deepening with Large Language Models: Insights from Exploratory Studies with ChatGPT.
.- From ConOps to an Operational Domain Model: Harnessing LLMs for Conceptual Model Design.
.- Towards diagram-based data model generation with LLMs.
.- OntoCom.
.- Unpacking Trust: An Ontological Framework for Information Trustworthiness in Decision-Making.
.- QUAMES.
.- Towards Model-Driven Testing for Assuring the Quality of Large Language Models.
.- Prompt-Guided Evaluation of GPT-4o, Gemini and DeepSeek on UML-to-Java Code Generation.