Re: DCMI 2025 - October 22-25, University of Barcelona, Spain

Paola, here's Grok's summary of the relationship:

In practice, think of Dublin Core as a librarian handing over neatly labeled books to an AI. The AI can take those labels and run with them—building a knowledge base, feeding a recommendation engine, or enriching a dataset—but it’s not the AI’s whole brain, just a helpful input. For example, libraries and digital archives (like Europeana or the Library of Congress) use DC to tag vast collections, and AI tools can leverage that to power search, discovery, or even cross-lingual translation.

From my perspective, it's better than ChatGPT's summary.  See, for example, Grok's observation:

Simplicity vs. Complexity Trade-off: AI knowledge representation often aims for deep expressiveness (e.g., capturing causal relationships or hierarchical concepts). Dublin Core, by contrast, is deliberately simple and doesn’t natively support complex reasoning. It’s more about description than inference. So, while it’s useful for organizing raw data, AI systems typically need to layer additional structures (like ontologies or embeddings) on top to do heavier lifting.

Here's its conclusion about the relevance of the StratML standard:

How It Ties Together ~ StratML relates to our earlier points about problem-solving and reasoning by serving human objectives. It’s not a deep reasoning engine itself (it’s not solving problems), but it structures the knowledge that fuels reasoning—especially for AI. It’s like handing an AI a playbook: "Here’s what we want; figure out how to do it." Compared to something like Dublin Core, which is descriptive and static, StratML is prescriptive and dynamic, focused on intent and action.

Owen Amburhttps://www.linkedin.com/in/owenambur/
 

    On Thursday, February 20, 2025 at 10:35:56 AM EST, Paola Di Maio <paola.dimaio@gmail.com> wrote:   

 must have been thirty years ago or so before we wrapped our heads around this topicrelevance to AI KR?best
PDM---------- Forwarded message ---------
From: Jian Qin <jqin@syr.edu>
Date: Thu, Feb 20, 2025 at 11:05 PM
Subject: DCMI 2025 - October 22-25, University of Barcelona, Spain
To: dcc-associates@lists.ed.ac.uk <dcc-associates@lists.ed.ac.uk>



First Announcement 

The Twenty-Third International Conference on Dublin Core and Metadata Applications of The Dublin Core Metadata Initiative (DCMI 2025) 

"(Meta)data at the Core: Bridging Human Knowledge and AI Innovation"

October 22-25 (main conference), University of Barcelona, Spain

Conference website:  https://www.dublincore.org/conferences/2025/

Submission types include: Full Papers, Short Papers, Panels, Workshops, Posters, Project Reports, Student Forums, Tutorials, Best Practices, and Talks.

DCMI 2025 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.

Please watch out for the full Call for Participation and Submission as well as deadlines for submissions, which will be announced soon. 

 

Jian Qin  

Ph.D., Professor 


School of Information Studies |Syracuse University

226 Hinds Hall, Syracuse, NY 13244 | https://ischool.syr.edu/jian-qin/

(315) 443-5642 | jqin@syr.edu | ORCID: https://orcid.org/0000-0002-7094-2867

 

Recent publications:

Liu, Q. & Qin, J. (2025). The role of ontologies in machine learning: A case study of Gene Ontology. Accepted byiConference 2025. 

Qin, J. & Liu, Q. (2024). Organizing knowledge in knowledgebases: A case study. In:Knowledge Organization for Resilience in Times of Crisis: Challenges and Opportunities: Proceedings of the Eighteenth International ISKO Conference, 2024, Wuhan, China,pp. 393-400. Baden-Baden: Ergon. http://doi.org/10.5771/9783987400476

Qin, J. & Yu, B. (2023). Trustworthy AI and metadata. In: The Dublin Core International Conference DCMI2023, Daegu, South Korea, November 6-10. https://doi.org/10.23106/dcmi.953354037