Leveraging semantic representations via knowledge graph embeddings

Franz Krause*, Kabul Kurniawan, Elmar Kiesling, Jorge Martinez-Gil, Thomas Hoch, Mario Pichler, Bernhard Heinzl, Bernhard Moser

*Corresponding author for this work

Publication: Chapter in book/Conference proceedingChapter in edited volume

Abstract

The representation and exploitation of semantics has been gaining popularity in recent research, as exemplified by the uptake of large language models in the field of Natural Language Processing (NLP) and knowledge graphs (KGs) in the Semantic Web. Although KGs are already employed in manufacturing to integrate and standardize domain knowledge, the generation and application of corresponding KG embeddings as lean feature representations of graph elements have yet to be extensively explored in this domain. Existing KGs in manufacturing often focus on top-level domain knowledge and thus ignore domain dynamics, or they lack interconnectedness, i.e., nodes primarily represent non-contextual data values with single adjacent edges, such as sensor measurements. Consequently, context-dependent KG embedding algorithms are either restricted to non-dynamic use cases or cannot be applied at all due to the given KG characteristics. Therefore, this work provides an overview of state-of-the-art KG embedding methods and their functionalities, identifying the lack of dynamic embedding formalisms and application scenarios as the key obstacles that hinder their implementation in manufacturing. Accordingly, we introduce an approach for dynamizing existing KG embeddings based on local embedding reconstructions. Furthermore, we address the utilization of KG embeddings in the Horizon2020 project Teaming.AI (www.teamingai-project.eu.) focusing on their respective benefits.

Original languageEnglish
Title of host publicationArtificial Intelligence in Manufacturing
Subtitle of host publicationEnabling Intelligent, Flexible and Cost-Effective Production Through AI
Place of PublicationCham
PublisherSpringer
Pages71-85
Number of pages15
ISBN (Electronic)9783031464522
ISBN (Print)9783031464515
DOIs
Publication statusPublished - 8 Feb 2024

Bibliographical note

Publisher Copyright:
© The Author(s) 2024. All rights reserved.

Keywords

  • Dynamic knowledge graph embedding
  • Industry 5.0
  • Knowledge graph
  • Knowledge graph embedding
  • Manufacturing
  • Representation learning

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