Acquisition of Semantics for Machine-Learning and Deep-Learning based Applications

Wittenberg T, Lang T, Eixelberger T, Gruber R (2024)

Publication Type: Authored book

Publication year: 2024

Publisher: Springer Nature

ISBN: 9783031648328

DOI: 10.1007/978-3-031-64832-8_8

Abstract

For the development, training, and validation of machine learning (ML) and deep learning (DL) based methods, such as, e.g., image analysis, prediction of critical events, extraction or reconstruction of information from disrupted data streams, searching similarities in data collections, or planning of procedures, a lot of data is needed. Additionally to this data (images, bio-signals, vital-signs, text records, machine states, trajectories, antenna data, …) adequate supplementary information about the meaning encoded in the data is required. Only with this additional information – the meaning or knowledge – a tight relation between the raw data and the human-understandable concepts – the semantics – from the real world can be established. Nevertheless, as the amount of data needed to develop robust ML or DL methods is strongly increasing, the assessment and acquisition of the related knowledge becomes more and more challenging. Within this chapter, an overview of concepts of knowledge acquisition applied to the different examples of applications is described and evaluated. Six main groups of knowledge acquisition related to AI-based technologies have been identified, namely (1) manual annotation methods, (2) data augmentation, (3) generative networks or simulation techniques, (4) synchronized sensors, (5) Active Learningapproaches, and (6) explicit knowledge modeling using semantic networks.

Authors with CRIS profile

Thomas Wittenberg

Involved external institutions

Fraunhofer-Institut für Integrierte Schaltungen (IIS) DE Germany (DE)

How to cite

APA:

Wittenberg, T., Lang, T., Eixelberger, T., & Gruber, R. (2024). Acquisition of Semantics for Machine-Learning and Deep-Learning based Applications. Springer Nature.

MLA:

Wittenberg, Thomas, et al. Acquisition of Semantics for Machine-Learning and Deep-Learning based Applications. Springer Nature, 2024.

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