DCMI 2026 (International Conference on Dublin Core and Metadata Applications) is a ICORE C conference held in Seoul, South Korea on 2026-08-03. The paper submission deadline is 2026-04-13 (extended). Acceptance notifications are sent on 2026-05-22.
DCMI 2026, the twenty-fourth International Conference on Dublin Core and Metadata Applications, invites researchers, practitioners, and experts from diverse domains to explore the dynamic landscape of metadata in the theme of Meaning-Driven AI: Using Metadata to Align Systems with Human Values. The fast-paced advances in artificial intelligence (AI) create new research opportunities for metadata. While AI has the potential to enhance metadata quality through systematic tasks like error detection and data standardisation, meaning-driven AI explores how structured data can capture human preferences, beliefs, and experiences to create intelligent systems that truly understand what people value.
Metadata has an expanding role in enabling the transparent, trustworthy, and effective representation of data, information, and knowledge, and as a result, is being transformed from simply "data about data'' to being data that underpin knowledge. In this expansion of metadata's role, we strive to bring innovative ideas, projects, and practices together that can foster and protect humanity.
DCMI 2026 serves as a unique platform for the discussion of innovative research and practice, presenting visions for future metadata development and solutions to practical metadata problems. Join researchers, practitioners, and experts from a wide range of sectors in a collaborative exploration of metadata's evolving role through your papers, posters, panel discussions, best practice reports, designathon/hackathon, workshops, and more.
DCMI 2026 will feature exclusively in-person meetings.
Key areas:
Under the theme Meaning-Driven AI: Using Metadata to Align Systems with Human Values the DCMI 2026 conference welcomes submissions on the following topics broadly related to metadata design, deployment, and best practices (but not limited to):
Metadata and AI: The role of metadata in explainable and reproducible AI, metadata representations for machine learning (ML) models and datasets, application of AI in metadata generation, and knowledge-driven metadata for ML applications.
AI Agents: AI agents that leverage metadata to anticipate human preferences, make context-aware decisions, and act in ways that align with the values and needs of the people they serve.
Human-Centered Metadata and Interaction: Exploring user experience (UX) in metadata systems and adaptive metadata systems that evolve based on user needs.
Data Integrity and Reliability: Innovative metadata research and practices that ensure data integrity, accuracy, provenance, and reliability.
Ethics and Metadata: Addressing ethical considerations in metadata creation and management to build trust, ensure fairness, mitigate bias, and promote transparency in AI and data governance.
Adaptation to Emerging Technologies: Transforming metadata constructs and systems to enable the full utilization of technologies in AI, linked data, and knowledge bases.
Metadata and Data Science: Application of data science theories and methods in developing linked, intelligent metadata to facilitate transformation.
Metadata for the Public Good: The implications and significance of metadata in trustworthy AI; the role of metadata in supporting the fight against nefarious deepfakes, misinformation, and disinformation; open data, open science, and open metadata.
Cultural and Social Dimensions of Metadata: Digital humanities and metadata practices in memory institutions, semantic and computational metadata for cultural heritage objects, equitable metadata representation for historical materials, and critical study of metadata theories, practices, standards, and tools.
Metadata Supporting the FAIR and CARE Principles: Solutions and practices in creating FAIR metadata, case studies of data reusability fostered by metadata, and new data structures and models supporting metadata interoperability.
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