Managing human-AI collaborations within Industry 5.0 scenarios via knowledge graphs: key challenges and lessons learned

In this paper, we discuss technologies and approaches based on Knowledge Graphs (KGs) that enable the management of inline human interventions in AI-assisted manufacturing processes in Industry 5.0 under potentially changing conditions in order to maintain or improve the overall system performance. Whereas KG-based systems are commonly based on a static view with their structure fixed at design time, we argue that the dynamic challenge of inline Human-AI (H-AI) collaboration in industrial settings calls for a late shaping design principle. In contrast to early shaping, which determines the system's behavior at design time in a fine granular manner, late shaping is a coarse-to-fine approach that leaves more space for fine-tuning, adaptation and integration of human intelligence at runtime. In this context we discuss approaches and lessons learned from the European manufacturing project Teaming.AI, https://www.teamingai-project.eu/, addressing general challenges like the modeling of domain expertise with particular focus on vertical knowledge integration, as well as challenges linked to an industrial KG of choice, such as its dynamic population and the late shaping of KG embeddings as the foundation of relational machine learning models which have emerged as an effective tool for exploiting graph-structured data to infer new insights.

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Publication Details

Journal
Frontiers in Artificial Intelligence
Published
2024-11-11
DOI
https://doi.org/10.3389/frai.2024.1247712
Citations
16
Primary Topic
Data Quality and Management
Type
article
Field-Weighted Citation Impact
7.85

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article

Managing human-AI collaborations within Industry 5.0 scenarios via knowledge graphs: key challenges and lessons learned

Agastya Silvina, Thomas Hoch, Gernot Stübl, Kabul Kurniawan et al.
16 citations
Frontiers in Artificial Intelligence
Data Quality and Management
7.85
article

Managing human-AI collaborations within Industry 5.0 scenarios via knowledge graphs: key challenges and lessons learned

Agastya Silvina, Thomas Hoch, Gernot Stübl, Kabul Kurniawan, Jorge Martínez-Gil, Nazım Kemal Üre, Maqbool Khan, Hector Diego Estrada-Lugo, Heiko Paulheim, Franz Krause, Maria Chiara Leva, Elmar Kiesling, Bernhard Heinzl, Bernhard A. Moser, Pedro Demolder, Hans Gaux, Javier Dominguez-Ledo
article en
16 citations

Abstract

In this paper, we discuss technologies and approaches based on Knowledge Graphs (KGs) that enable the management of inline human interventions in AI-assisted manufacturing processes in Industry 5.0 under potentially changing conditions in order to maintain or improve the overall system performance. Whereas KG-based systems are commonly based on a static view with their structure fixed at design time, we argue that the dynamic challenge of inline Human-AI (H-AI) collaboration in industrial settings calls for a late shaping design principle. In contrast to early shaping, which determines the system's behavior at design time in a fine granular manner, late shaping is a coarse-to-fine approach that leaves more space for fine-tuning, adaptation and integration of human intelligence at runtime. In this context we discuss approaches and lessons learned from the European manufacturing project Teaming.AI, https://www.teamingai-project.eu/, addressing general challenges like the modeling of domain expertise with particular focus on vertical knowledge integration, as well as challenges linked to an industrial KG of choice, such as its dynamic population and the late shaping of KG embeddings as the foundation of relational machine learning models which have emerged as an effective tool for exploiting graph-structured data to infer new insights.

Frontiers in Artificial IntelligenceVol. 7
Vienna University of Economics and Business (AT), Pakistan Institute of Engineering and Applied Sciences (PK), University of Mannheim (DE), Ideko (Spain) (ES), Software Competence Center Hagenberg (Austria) (AT), Profactor (Austria) (AT), Technological University Dublin (IE), Pak-Austria Fachhochschule: Institute of Applied Sciences and Technology (PK), Istanbul Technical University (TR)
European Commission, Österreichische Forschungsförderungsgesellschaft, H2020 Excellent Science
Industry, innovation and infrastructure
Openalex Percentile: Top 2%
Data Quality and Management
7.85
DOIOpen Access PDFOpenAlex
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