And I love the big shoutout @juansequeda.bsky.social gives to Amalia Child, and her article "The Five Laws of Data Enablement"

Read it here: locallyoptimistic.com/post/the-fiv...

@olesenbagneux.bsky.social and @jtalisman.bsky.social gave LIS such amazing representation at Data Day Texas this year in their talks, a trend I am sure will continue.

Observing the same

bsky.app/profile/juan...

Doing the knowledge work. Modeling is one part. Talking to different stakeholders to understand what X means and figuring out where it is in the data. Today that work is done but it’s very ad hoc and ends up being technical focus and lacks the social/people side (talking to the biz)

With LLMs serving as interfaces & KG handling context and integration, we now have the tools to fully realize this vision. My call to everyone reading this is to revisit the original Semantic Web vision and build on its decades of progress. Agents were always the goal it’s time to bring them to life

A Call for Agents: To those in the AI community, the idea of agents isn’t new. But the Semantic Web vision of autonomous agents, grounded in knowledge representation, planning, and data integration, provides a robust foundation for building the next generation of intelligent systems.

LLMs might suffice. Can LLMs do data integration Technically they are integrating data through training so depending on questions, it may be sufficient. But for rigorous tasks that enterprises require, knowledge graphs remain essential. Let’s not forget that LLMs are focused on generative tasks

LLMs Knowledge Graphs Debate Together w/ Sabrina Kirrane, we moderate a debate on Knowledge Graphs vs LLMs. The conclusion? It’s not an either/or question—it’s about understanding the task context. For “beer reasoning” where you take advice from a friend while drinking beer as Oscar Corcho put it,

Humans understand concepts. Goal is to connect a human concept to a machine weight
LLMs/Semantic Layers: Can LLMs bridge the silos created by diverse semantic layer/modeling tools? Discussions focused on interoperability, schema reuse, potential for LLMs to act as universal data model translators.

Explainability: What makes a “decent” explanation? Context and user expectations are critical. Explanations should balance transparency with usability, whether for bias mitigation, debugging, or trust-building. Explanations are for Human-AI communication. AI understands pixels, weights...

Mappings: From aligning ontologies to evaluating quality and governance, this discussion reinforced the need for human-in-the-loop approaches and the potential role of LLMs in automating parts of the process.

There was a special Dagstuhl style session on Knowledge Graphs and GenAI. I participated in three breakout sessions. The takeaways aren’t surprising, it’s validation that we are aligned on the problems and working on them

They’ve extended GQL and SPARQL to return paths in graph queries in a practical way. I expect to see graph databases implementing this approach. Also check out the Google paper “Relationships are complicated! An analysis of relationships between datasets on the Web” which won the Best Paper Award.

the original semantic web vision is achievable. LLMs can act as user interfaces, knowledge graphs handle data integration, and traditional agent components like planning can complete the picture.

Check out PathFinder: Returning Paths in Graph Queries which won the Best Student Paper Award

Ora reminded us that the original vision of the Semantic Web was centered on autonomous agents—making it inherently about AI. Due to the “AI winter,” the focus shifted away from agents. With LLMs and the scalability of RDF knowledge graphs (e.g., powering Amazon’s supply chain), ...

from multiple domains. Main takeaway is to think about data provenance and lineage through the lens of a supply chain.
Ora Lassila’s Keynote: This was a standout, not just because Ora and I are friends and co-authors, but because it revisited the past, present, and future of the Semantic Web.

direct mapping into graphs, followed by semantic integration and entity resolution. End-users, like journalists, prefer simple search interfaces over complex graph visualizations.
Chaitan Baru’s Keynote: NSF has a grand vision of creating an Open Knowledge Network which integrates knowledge graphs