Semantic Interoperability in Industrial Operations: Challenges and Innovations

The rapid digitalization of manufacturing and logistics — driven by Industry 4.0, the Industrial Internet of Things (IIoT), and cyber-physical systems, among other pillars — is transforming the landscape with smart connected devices, systems, and applications available at all levels of operations: control, scheduling, planning, and supply chains. As factories and supply chains become smarter and more interconnected, involving multiple partners, systems from different vendors, legacy platforms, and cutting-edge modules must communicate and work together seamlessly, exchanging and using information without compatibility issues. However, achieving this level of interoperability is not purely a technical challenge; it requires a shared understanding of data semantics. Semantic interoperability refers to the ability of diverse information systems, devices, or applications to exchange data with unambiguous, shared meaning. It ensures that information exchanged is not only transferred correctly but also understood in the same way by all parties involved.

Despite ongoing advancements, realizing semantic interoperability in industrial operations remains a major challenge. Diverse data models, lack of standardized vocabularies, varying ontologies, and proprietary formats often create silos that hinder seamless information exchange and coordinated action. These obstacles can lead to inefficiencies, miscommunication, and costly errors, undermining the very benefits promised by digital transformation. Creating semantic bridges that enable accurate and meaningful interpretation of the various types of operations data is vital for adaptive, autonomous decision-making and data-driven optimization in modern industry. Semantic technologies allow for easier integration, adaptation, and interconnection of systems and decision-making tools.

The goal of this Research Topic is to bring together cutting-edge research and innovative practices that address the critical challenges of semantic interoperability that appear in the chemical industry and other related domains, such as biochemical, biomanufacturing, food, energy, among others, at all levels of operations: control, scheduling, planning, and supply chains. We aim to identify and explore the key theoretical, methodological, and technological advancements that can enable reliable and scalable integration of heterogeneous systems. From developing and evaluating ontologies, knowledge graphs, and other semantic models to leveraging artificial intelligence for automated data harmonization, we encourage contributions that push the boundaries of what is currently possible.

We welcome Original Research, Review, Mini Review and Perspective articles on themes including, but not limited to:

• Design and implementation of ontologies, knowledge graphs and other semantic models for data and processes participating at different levels of industrial operations: control, scheduling, planning, and supply chains
• Integration frameworks leveraging semantic web technologies and standards
• AI and machine learning solutions for resolving semantic discrepancies
• Case studies of semantic interoperability in real-world industrial or manufacturing settings
• Best practices in aligning legacy and modern systems through semantic technologies
• Collaborative platforms and architectures for supporting operations across organizational boundaries, as well as across different levels of an industrial enterprise.
• Future research directions, challenges, and opportunities in semantic interoperability

Submissions should emphasize practical relevance and scalability within the context of industrial operations taking place in the chemical industry and related ones (biochemical, biomanufacturing, food, energy, among others), whether the focus is on conceptual advances, technical solutions, or empirical validation. By fostering an interdisciplinary discussion, this Research Topic seeks to accelerate the adoption of robust semantic interoperability solutions that empower the chemical industry to realize the full potential of intelligent operations.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• Methods
• Mini Review
• Original Research
• Perspective
• Review
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• Methods
• Mini Review
• Original Research
• Perspective
• Review
• Technology and Code

Keywords: Semantic Interoperability, Industrial Operations, Ontologies, Knowledge Graphs, Industry 4.0

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.