Towards a Data Driven Natural Language Interface for Industrial IoT Use Cases

Gui Z, Harth A (2021)

Publication Type: Conference contribution

Publication year: 2021

Publisher: Institute of Electrical and Electronics Engineers Inc.

Conference Proceedings Title: Proceedings of the 2021 IEEE International Conference on Human-Machine Systems, ICHMS 2021

Event location: Magdeburg, DEU DE

ISBN: 9781665401708

DOI: 10.1109/ICHMS53169.2021.9582450

Open Access Link: https://ieeexplore.ieee.org/document/9582450

Abstract

The ubiquitous availability of sensors and smart devices makes IoT networks more and more complex to manage and control. A natural language interface (NLI) would allow users to interact with the devices via human language by translating the user command into a machine-interpretable meaning representation, often called logical forms.Despite the rapid development of conversational interfaces in smart home and personal intelligent assistant use cases, there are limited research and applications in industrial sensor and actuator networks, usually referred to as Industrial Internet of Things (IIoT). In this paper, we show an early phase design principle of a semantic representation to express IIoT device interactions and propose a data-focused workflow of IIoT automation system architecture.

Authors with CRIS profile

Zhou Gui Lehrstuhl für Wirtschaftsinformatik, insbesondere Technische Informationssysteme Andreas Harth Lehrstuhl für Wirtschaftsinformatik, insbesondere Technische Informationssysteme

How to cite

APA:

Gui, Z., & Harth, A. (2021). Towards a Data Driven Natural Language Interface for Industrial IoT Use Cases. In Andreas Nurnberger, Giancarlo Fortino, Antonio Guerrieri, David Kaber, David Mendonca, Malte Schilling, Zhiwen Yu (Eds.), Proceedings of the 2021 IEEE International Conference on Human-Machine Systems, ICHMS 2021. Magdeburg, DEU, DE: Institute of Electrical and Electronics Engineers Inc..

MLA:

Gui, Zhou, and Andreas Harth. "Towards a Data Driven Natural Language Interface for Industrial IoT Use Cases." Proceedings of the 2021 IEEE International Conference on Human-Machine Systems, ICHMS 2021, Magdeburg, DEU Ed. Andreas Nurnberger, Giancarlo Fortino, Antonio Guerrieri, David Kaber, David Mendonca, Malte Schilling, Zhiwen Yu, Institute of Electrical and Electronics Engineers Inc., 2021.

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