Connected Data 2025 Trip Report For the first time, I was at a knowledge graph conference where every single talk I attended came from businesses.  Not by vendors. Not POCs. Real production deployments with mature architectures and well thought out roles and processes. I actually was able to attend eight talks, and give a talk myself. I usually go to conferences to meet with people and spend time in the hallways.  This was different, I found that I wanted to clone myself because I wanted to attend so many talks. @JuanSequeda's epic recollection of the epic #CDL25 conference juansequeda.substack.com/p/t… Missed it? Fight the FOMO - get yourself a Remote Access Ticket and dive in 👇 2025.connected-data.london/ -- Connected Data London 2025 is a wrap! Join community legends and new voices in #CDL25 for all things #KnowledgeGraph #Graph #analytics #datascience #AI #graphDB #SemTech #Ontology 👉 Discover more about the event and access presentations and replays: 2025.connected-data.london/ 🤝 Want to partner with us on next year's event? Contact us at [email protected] to discuss sponsorship opportunities 📩 Signup for our newsletter and be the first to know about news, special offers and important dates at connected-data.london

Two Meanings of “Semantic Layer” and Why Both Matter in the Age of AI Everyone’s talking about semantic layers, but not everyone means the same thing. On the analytics side, semantic layer usually refers to something we’ve seen before. It’s the business-friendly model that sits between raw data and BI tools. A governed way to define joins, metrics, and consistent logic. This version of semantics is about trust. It makes sure when someone (or something) asks, “What’s our total revenue last month?” the answer is accurate, governed, and consistent. There’s another camp using the same phrase and they’ve been doing it far longer. For ontologists, RDF, OWL and JSON-LD are open standards for exchanging semantic data. To them, a semantic layer isn’t a metrics model, it’s a knowledge model. It’s about representing meaning, relationships, and context across systems and content. This is where schema.org, controlled vocabularies, metadata standards, and ontologies come into play . The structures that let AI understand that customer, client, and member all point to the same concept. This version of semantics is about understanding. It connects definitions and relationships, providing the context AI uses to make sense of information. These meanings of semantic layer aren’t competing, they’re complementary. To get the most benefit combine them: Structured semantics → Governed queries over structured data → Trust in numbers Ontological semantics → Contextual understanding of concepts → Understanding of meaning linkedin.com/posts/meaganpal… By Meagan Palmer, with comments from Jessica Talisman and Juan Sequeda. Come meet and learn from Jessica and Juan at Connected Data London, where they will be sharing their insights on Knowledge Infrastructures and the Ontology Pipeline for AI Systems, and What Data Engineers & Governance Professionals Must Know About Knowledge Graphs -- Connected Data London 2025 is coming! 20-21 November, Leonardo Royal Hotel London Tower Bridge Join community legends and new voices in #CDL25 for all things #KnowledgeGraph #Graph #analytics #datascience #AI #graphDB #SemTech #Ontology 👉 Discover more about the event and secure your place today: 2025.connected-data.london/ 🤝 Want to partner with us on this year's event? Contact us at [email protected] to discuss sponsorship opportunities 📩 Signup for our newsletter and be the first to know about news, special offers and important dates connected-data.london