MOVE2026 Workshop Call for Submissions and Participation
Polovina, Simon (BTE)
Hello, all.
The MOVE 2026 call for papers, abstracts, or presentation submissions has been extended to July 27th. Visit MOVE 2026 Call for Participation for more info and submission details.
Please note that there is no registration fee for this event, and the post-workshop proceedings are planned to be published by Springer in their CCIS series as before: Measuring Ontologies for Value Enhancement: Aligning Computing Productivity with Human Creativity for Societal Adaptation: First International Workshop, MOVE 2020, Virtual Event, October 17–18, 2020, Revised Selected Papers | Springer Nature Link. The second set of MOVE proceedings is in press in the same series.
We encourage you to join us. Thank you!
Simon
MOVE2026 Workshop Call for Papers and Presentations
Details
Published: 02 May 2026
MOVE2026 - Measuring Ontologies for Value Enhancement (MOVE) Workshop
Theme: MOVE and AI
16-18 Sept. 2026
Dear Members of the MOVE and wider Research Community,
Dr. Simon Polovina (Department of Computing and Digital Technologies, Sheffield Hallam, UK, and Similie Logics Limited, Sheffield, UK), Dr. David Jakobsen (Department of Culture and Communication, Aalborg University, Denmark), and I, Dr. Rubina Polovina (Systems Affairs, Toronto, Ontario, Canada), are organizing the third MOVE workshop, titled “MOVE and AI”. It will be a hybrid (on-site and virtual) event, co-located at the Informatics Research Centre, Henley Business School, University of Reading, UK, 16-18 September 2026.
There are no registration or publication fees; participation and publication are free of charge.
Our research community comprises academics from different science faculties and practitioners across the globe. We serve as a platform for publishing cutting-edge research that explores the role of ontologies and related paradigms and technologies in advancing AI. By engaging with the MOVE and wider research community, we can attract diverse, high-quality submissions from academia and industry, fostering innovation at the intersection of ontologies, ontology-like methods, and AI.
About the MOVE Community
The MOVE initiative explores the role of ontologies in AI systems, focusing on frameworks for knowledge representation, reasoning, and interoperability. Our work emphasizes how systematically integrated ontologies can address complex challenges in AI.
The MOVE community held its inaugural workshop in 2020, in collaboration with the CSCW 2020 (Computer Supported Cooperative Work) conference. Selected and enhanced papers from this workshop were published in the post-proceedings, “Aligning Computing Productivity with Human Creativity for Societal Adaptation,” available at https://link.springer.com/book/10.1007/978-3-031-22228-3.
The second MOVE workshop was hosted in 2024 in collaboration with the University of Technology Sydney, Australia. We are currently finalizing the proceedings for the MOVE2024 workshop published by Springer.
MOVE workshops play a pivotal role in generating high-quality research contributions. These workshops provide a platform for engaging diverse researchers and practitioners, fostering collaboration, and cultivating innovative ideas. Short papers (up to 15 pages without references) presented at MOVE workshops undergo a single-blind peer-review process to ensure their relevance, originality, and alignment with the track’s themes. Only selected and enhanced MOVE workshop papers will be submitted to the special track, where they will undergo an additional peer review to turn them into long papers. Alongside submissions received directly through the manuscript collection website, the workshops will serve as a valuable source of scholarly and practical contributions, enriching the special track with cutting-edge insights into ontology-driven AI research.
Proposed Themes
We envision the MOVE2026 topics encompassing, but not limited to, the following themes:
- Foundations of Ontologies: Advancing formalization to strengthen AI reasoning and decision-making.
- Evaluation and Benchmarking: Developing methods to assess and enhance ontology quality and utility.
- Applications Across AI Subfields: Leveraging ontologies in robotics, healthcare, NLP, and intelligent systems.
- Human-Computer Interfaces: Enhancing interaction and personalization through ontology-based frameworks.
- Emerging Paradigms: Integrating ontologies with quantum computing, hybrid AI, and explainable AI and other emerging technologies.
- Knowledge Representation: Supporting complex problem-solving via contextual and temporal modeling.
- Interoperability and Standards: Improving AI system coherence through standardized frameworks.
- Reasoning Under Uncertainty: Facilitating probabilistic and non-deterministic reasoning with ontologies.
- Multidimensional Modeling: Bridging dimensions like context, relationships, and time for holistic AI models.
- Advances in Knowledge Graphs: Enhancing scalability and utility via ontology-driven approaches.
- Conceptual Structures: Bringing Computer Productivity and AI to Human Creativity
- Case Studies: Highlighting real-world successes of ontology-driven AI.
- Ethics and Ontologies: Addressing bias, transparency, and accountability in ethical AI design.
- AI and Enterprise Architecture: Aligning ontologies with enterprise systems for adaptive, efficient solutions.
- Collective Intelligence: Advancing collaboration and knowledge sharing through ontology paradigms.
- Endeavour Architecture: Enabling human agency and purposeful action in Enterprise Architecture, such as advancing Human Rights.
- Facilitating Governance with AI and Ontologies: Advancing the integration of AI with ontologies and ontology-like frameworks to enhance governmental functions, with examples such as policymaking, complex decision-making processes, and informed political actions.
- Exploring AGI/ASI: Investigating ontologies’ role in designing and advancing general, multidimensional intelligence, such as human-like intelligence and beyond.
- Large Language Models (LLMs): Investigating how ontologies can enhance the interpretability, consistency, and contextual grounding of LLMs in various applications.
- Deep Semantics: Exploring ontologies and ontology-based frameworks to capture nuanced meanings, relationships, and contextual dependencies, enabling richer AI understanding and reasoning.
- Semiotics and Meaning-Making: Investigating sign systems, symbol grounding, and interpretation processes in AI, including the role of ontologies in structuring and stabilizing meaning across representations, modalities, and contexts.
- Sense-Making and Interpretation: Examining how agents construct, stabilize, and revise meaning under uncertainty, drawing on established sense-making traditions (e.g., organizational and information science) and connecting these processes to ontology design, semantic modeling, and AI reasoning.
- Philosophical Foundations of Meaning and Intelligence: Engaging with relevant philosophical theories (e.g., philosophy of mind, language, cognition, and logic) where they inform AI modeling, ontology design, and the interpretation of intelligence. Submissions should demonstrate clear implications for computational or representational frameworks.
- Origins of Intelligence and Life: Exploring theories of the emergence of life and intelligence, including biologically grounded and systems-based accounts. Contributions may examine how such theories inform AI architectures, definitions of intelligence, the development of ontology-driven models, and the structuring of the DIKW (Data–Information–Knowledge–Wisdom) space.
- Leveraging ontologies to facilitate NLP: Enhancing the understanding, translation, and generation of natural languages.
- Advance ecological and biological research: Using ontologies and AI to support groundbreaking efforts, such as facilitating interspecies communication, including whale communication, and broader ecological insights.
- Revolutionize neuroscience and medicine: Applying ontologies and AI to uncover complex neural dynamics, enhance diagnostics, personalize treatments, and advance our understanding of the brain and human health
Dr Simon Polovina
Department of Computing and Digital Technologies, Sheffield Hallam University, UK