Information Modelling (IM) has been under the spotlight of both academia and industry for decades. Important aspects of IM include methods and practices of representing concepts, relationships, constraints, rules, and operations to specify data semantics for a chosen domain of interest. As a response to the IM challenge, a number of modelling paradigms and languages have emerged, ranging from ERM, UML, and ORM to OWL and Knowledge Graphs, together with a wide range of systems supporting the life cycle of information models.

Despite past success, existing approaches and systems for IM struggle to cope with new challenges arising from global industrial digitalisation, fully computerised and software-driven automation of production processes, and enterprise-wide integration of software components. Such trends and the technological and industrial developments that come with them are an important part of Industry 4.0 and the Industrial Internet of Things. They require IM approaches that, for example, capture the functionality of and information flow between different assets in a plant, such as equipment and production processes. Moreover, they require information models based on ISA and IEC standards with desirable properties such as reusability, explainability, scalability, and simulability.

At the same time, the increasing adoption of AI and large language models in industrial and organisational applications introduces new challenges related to trustworthiness, bias, accountability, and transparency. Information models can play an important role in structuring domain knowledge, representing assumptions, constraints, and decision contexts, and supporting explainable and auditable AI systems. This is particularly relevant for high-stakes applications such as human resource management, recruitment, compliance, public-sector decision support, and political bias detection, where untrustworthy or biased AI outputs may have significant organisational or societal consequences.

These new challenges require new theory, methodology, best practice, systems, and shared experience, developed and discussed by a wide range of stakeholders. In this workshop, we aim to gather researchers and practitioners who address these challenges with the help of semantic technologies. We particularly invite IM experts who are excited and committed to pushing the frontiers of IM further and supporting modern industry in its current technological and AI-driven transformation. The workshop welcomes novel methods, systems, solutions, experience, and practice for semantic industrial information modelling, including trustworthy and bias-aware information modelling for LLM-based applications.