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2025 | OriginalPaper | Chapter

A Novel Interdisciplinarity Model Towards Inter-domain Information Pairing

Authors : Nicolas Douard, Ahmed Samet, George Giakos, Denis Cavallucci

Published in: World Conference of AI-Powered Innovation and Inventive Design

Publisher: Springer Nature Switzerland

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Abstract

This chapter introduces a groundbreaking interdisciplinarity model that combines the IDM methodology with Generative AI to systematically extract latent interdisciplinary insights from a comprehensive corpus of human knowledge. By addressing the limitations of traditional bibliometric approaches, the proposed method leverages full-text content and advanced semantic modeling to detect latent interdisciplinary connections. The study focuses on identifying interdisciplinary documents, even when metadata classifies them within a single domain, using a supervised machine learning classifier. The Text Convolutional Neural Network approach demonstrated superior performance, suggesting that about 25% of all human knowledge can be seen as interdisciplinary. This research lays the foundation for constructing comprehensive interdisciplinary knowledge maps and enabling systematic cross-domain transfer, driving innovation through biomimicry and interdisciplinary insights.

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Title: A Novel Interdisciplinarity Model Towards Inter-domain Information Pairing
Authors: Nicolas Douard
Ahmed Samet
George Giakos
Denis Cavallucci
Publisher: Springer Nature Switzerland
Book: World Conference of AI-Powered Innovation and Inventive Design
Print ISBN: 978-3-031-75922-2

Electronic ISBN: 978-3-031-75923-9

Copyright Year: 2025
DOI: https://doi.org/10.1007/978-3-031-75923-9_17

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