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There’s a lot of buzz about #semanticlayers on LinkedIn these days. So what is a semantic layer? According to AtScale, “The semantic layer is a metadata and abstraction layer built on the source data (eg.. data warehouse, data lake, or data mart). The metadata is defined so that the data model gets enriched and becomes simple enough for the business user to understand.” It’s a metadata layer. Which can be taken a step further. A metadata layer is best implemented using metadata standards that support interoperability and extensibility. There are open standards such as Dublin Core Metadata Initiative and there are home-grown standards, established within organizations and domains. If you want to design and build semantic layers, build from metadata standards or build a metadata standard, according to #FAIR principles (findable, accessible, interoperable, reusable). Some interesting and BRILLIANT ✨folks to check out in the metadata domain space: Ole Olesen-Bagneux (O2)’s (check out his upcoming book about the #metagrid) Lisa N. Cao Robin Fay Jenna Jordan Larry Swanson Resources in comments 👇👇👇 | 29 comments on LinkedIn

How the idea of a semantic layer started, what are its elements, and how we can use it to enhance the performance of large language models

We present AutoSchemaKG, a framework for fully autonomous knowledge graph construction that eliminates the need for predefined schemas. Our system leverages large language models to simultaneously...

In large Knowledge Graphs (KGs), measuring the importance of each entity is essential for improving search, navigation, and reasoning…

Forecasting is a fundamentally new capability that is missing from the current purview of generative AI. Here's how Kumo is changing that.

Uniquely Pioneering Graph Analytics Combined With Deep Search Galway, Ireland – 24th June, 2025 — Siren, the all-in-one investigation company, today announced its adoption of Graph Query Language (GQL), the world’s first ISO-standard query language for graphs, made public in 2024. With this move, Siren becomes the first investigative platform to offer seamless, standards-based graph … Continue reading "Siren Adopts ISO-Standard GQL to Power the Next Generation of Graph Intelligence"

After publishing my article on the Missing Semantic Center, a brilliant colleague asked me a question that gets to the heart of our technology stack: "But Tavi - this doesn't look like an OWL 2 DL ontology. What's going on here?" This question highlights a profound aspect of why systems have struggl

I've been working on autonomous AI systems, and wanted to share some thoughts on what I believe makes them effective. The challenge isn't just making AI that follows instructions well, but creating systems that can reason, and act independently.

🧠 When Is an Ontological Approach Not the Right Fit for Sharing and Reusing System Knowledge in Design and Development? Ontologies promise knowledge integration, traceability, reuse, and machine reasoning across the full engineering system lifecycle. From functional models to field failures, ontologies offer a way to encode and connect it all. 💥 However, ontologies are not a silver bullet. There are plenty of scenarios where an ontology is not just unnecessary, it might actually slow you down, confuse your team, or waste resources. So when exactly does the ontological approach become more burden than benefit? Based on my understanding and current work in this space, 🚀 For engineering design, it's important to recognise situations where adopting a semantic model is not the most effective approach: 1. When tasks are highly localised and routine If you're just tweaking part drawings, running standard FEA simulations, or updating well-established design details, then the knowledge already lives in your tools and practices. Adding an ontology might feel like installing a satellite dish to tune a local radio station. 2. When terminology is unstable or fragmented Ontologies depend on consistent language. If every department speaks its own dialect, and no one agrees on terms, you can't build shared meaning. You’ll end up formalising confusion instead of clarifying it. 3. When speed matters more than structure In prototyping labs, testing grounds, or urgent production lines, agility rules. Engineers solve problems fast, often through direct collaboration. Taking time to define formal semantics? Not always practical. Sometimes the best model is a whiteboard and a sharp marker. 4. When the knowledge won’t be reused Not all projects aim for longevity or cross-team learning. If you're building something once, for one purpose, with no intention of scaling or sharing, skip the ontology. It’s like building a library catalog for a single book. 5. When the infrastructure isn't there Ontological engineering isn’t magic. It needs tools, training, and people who understand the stack. If your team lacks the skills or platforms, even the best-designed ontology will gather dust in a forgotten folder. Use the Right Tool for the Real Problem Ontologies are powerful, but not sacred. They shine when you need to connect knowledge across domains, ensure long-term traceability, or enable intelligent automation. But they’re not a requirement for every task just because they’re clever. The real challenge is not whether to use ontologies, but knowing when they genuinely improve clarity, consistency, and collaboration, and when they just complicate the obvious. 🧠 Feedback and critique are welcome; this is a living conversation. Felician Campean #KnowledgeManagement #SystemsEngineering #Ontology #MBSE #DigitalEngineering #RiskAnalysis #AIinEngineering #OntologyEngineering #SemanticInteroperability #SystemReliability #FailureAnalysis #KnowledgeIntegration | 11 comments on LinkedIn

LLMs already contain overlapping world models. You just have to ask them right. Ontologists reply to an LLM output, “That’s not a real ontology—it’s not a formal conceptualization.” But that’s just the No True Scotsman fallacy dressed up in OWL. Boring. Not growth-oriented. Look forward, angel. A foundation model is a compression of human knowledge. The real problem isn't that we "lack a conceptualization". The real problem with an FM is that they contain too many. FMs contain conceptualizations—plural. Messy? Sure. But usable. At Stardog, we’re turning this latent structure into real ontologies using symbolic knowledge distillation. Prompt orchestration → structure extraction → formal encoding. OWL, SHACL, and friends. Shake till mixed. Rinse. Repeat. Secret sauce simmered and reduced. This isn't theoretical hard. We avoid that. It’s merely engineering hard. We LTF into that! But the payoff means bootstrapping rich, new ontologies at scale: faster, cheaper, with lineage. It's the intersection of FM latent space, formal ontology, and user intent expressed via CQs. We call it the Symbolic Latent Layer (SLL). Cute eh? The future of enterprise AI isn’t just documents. It’s distilling structured symbolic knowledge from LLMs and plugging it into agents, workflows, and reasoning engines. You don’t need a priesthood to get a formal ontology anymore. You need a good prompt and a smarter pipeline and the right EKG platform. There's a lot more to say about this so I said it at Stardog Labs https://lnkd.in/eY5Sibed | 17 comments on LinkedIn

Every year, businesses and consumers lose billions of dollars to fraud, with consumers reporting $12.5 billion lost to fraud in 2024, a 25% increase year over year. People who commit fraud often work together in organized fraud networks, running many different schemes that companies struggle to detect and stop. In this post, we discuss how to use Amazon Neptune Analytics, a memory-optimized graph database engine for analytics, and GraphStorm, a scalable open source graph machine learning (ML) library, to build a fraud analysis pipeline with AWS services.

Graph is the new star schema. Change my mind. Why? Your agents can't be autonomous unless your structured data is a graph. It is really very simple. 1️⃣ To act autonomously, an agent must reason across structured data. Every autonomous decision - human or agent - hinges on a judgment: have I done enough? “Enough" boils down to driving the probability of success over some threshold. 2️⃣ You can’t just point the agent at your structured data store. Context windows are too small. Schema sprawl is too real. If you think it works, you probably haven’t tried it. 3️⃣ Agent must first retrieve - with RAG - the right tables, columns, and snippets. Decision making is a retrieval problem before it’s a reasoning problem. 4️⃣ Standard RAG breaks on enterprise metadata. The corpus is too entity-rich. Semantic similarity is breaking on enterprise help articles - it won't perform on column descriptions. 5️⃣ To make structured RAG work, you need a graph. Just like unstructured RAG needed links between articles, structured RAG needs links between tables, fields, and - most importantly - meaning. Yes, graphs are painful. But so was deep learning—until the return was undeniable. Agents need reasoning over structured data. That makes graphs non-optional. The rest is just engineering. Let’s stop modeling for reporting—and start modeling for autonomy. | 28 comments on LinkedIn

To highlight the different uses and impact of semantics and ontologies, I wanted to present a series of interview to professionals from different industries and roles. This is going to be a distributed post.

❓ How can you turn business questions into production-ready agentic knowledge graphs? Join Prashanth Rao and Dennis Irorere at the Agentic AI Summit to find out. Prashanth is an AI Engineer and DevRel lead at Kùzu Inc.—the open-source graph database startup—where he blends NLP, ML, and data engineering to power agentic workflows. Dennis is a Data Engineer at Tripadvisor’s Viator Marketing Technology team and Director of Innovation at GraphGeeks, driving scalable, AI-driven graph solutions for customer growth. In “Agentic Workflows for Graph RAG: Building Production-Ready Knowledge Graphs,” they’ll guide you through three hands-on lessons: 🔹 From Business Question to Graph Schema – Modeling your domain for downstream agents and LLMs, using live data sources like AskNews. 🔹 From Unstructured Data to Agent-Ready Graphs with BAML – Writing declarative pipelines that reliably extract entities and relationships at scale. 🔹 Agentic Graph RAG in Action – Completing the loop: translating NL queries into Cypher, retrieving graph data, and synthesizing responses—with fallback strategies when matches are missing. If you’re building internal tools or public-facing AI agents that rely on knowledge graphs, this workshop is for you. 🗓️ Learn more & register free: https://hubs.li/Q03qHnpQ0 #AgenticAI #GraphRAG #KnowledgeGraphs #AgentWorkflows #AIEngineering #ODSC #Kuzu #Tripadvisor

Structural Alignment has implications not just for graph-based AI / machine learning, but also for how we design graphs so that they can be…

Learning on knowledge graphs can be expressed directly in terms of graph structure. Here’s how — and here’s how you can do it yours

An overview of how the structure of knowledge graphs can be measured, based on my thesis “Structural Alignment in Link Prediction”.

An overview of knowledge graphs and the link prediction task, based on my thesis “Structural Alignment in Link Prediction”.

Find out how to combine a knowledge graph with RAG for GraphRAG. Provide more complete GenAI outputs.

Tired of being told that silos are gone? The real value comes from connecting them. 🔄 The myth of data silos: why they never really disappear, and how to turn them into your biggest advantage. Even after heavy IT investment, data silos never truly go away, they simply evolve. In food production, I saw this first-hand: every system (ERP, quality, IoT, POS) stored data in its own format. Sometimes, the same product ended up with different IDs across systems, batch information was fragmented, and data was dispersed in each silo. People often say, “Break down the silos.” But in reality, that’s nearly impossible. Businesses change, new tools appear, acquisitions happen, teams shift, new processes and production lines are launched. Silos are part of digital life. For years, I tried classic integrations. They helped a bit, but every change in one system caused more issues and even more integration work. I wish I had known then what I know now: Stop trying to destroy silos. Start connecting them. Here’s what makes the difference: Add a Semantic Layer – a smart translator that sits between your data sources and your business applications. It maps different formats and names into a common language, without changing your original systems. Put a Knowledge Graph on top and you don’t just translate – you connect. Suddenly, all your data sources, even legacy silos, become part of a single network. Products, ingredients, machines, partners, and customers are all logically linked and understood across your business. In practice, this means: - Production uses real sales and shelf-life data. - Sales sees live inventory, not outdated reports. - Forecasting is based on trustworthy, aligned data. That’s the real shift: Silos are not problems to kill, but assets to connect. With a Semantic Layer and a Knowledge Graph, data silos become trusted building blocks for your business intelligence. Better Data, Better ROI. If you’ve ever spent hours reconciling reports, you’ll recognise this recurring pain in companies that haven’t optimised their data integration with a semantic and KG approach. So: Do you still treat silos as problems, or could they be your next competitive advantage if you connect them the right way? Meaningfy #DataSilos #SemanticLayer #KnowledgeGraph #BusinessData #DigitalTransformation

Cellosaurus is now available in RDF format, with a triple store that supports SPARQL queries If this sounds a bit abstract or unfamiliar… 1) RDF stands for Resource Description Framework. Think of RDF as a way to express knowledge using triplets: Subject – Predicate – Object. Example: HeLa (subject) – is_transformed_by (predicate) – Human papillomavirus type 18 (object) These triplets are like little facts that can be connected together to form a graph of knowledge. 2) A triple store is a database designed specifically to store and retrieve these RDF triplets. Unlike traditional databases (tables, rows), triple stores are optimized for linked data. They allow you to navigate connections between biological entities, like species, tissues, genes, diseases, etc. 3) SPARQL is a query language for RDF data. It lets you ask complex questions, such as: - Find all cell lines with a *RAS (HRAS, NRAS, KRAS) mutation in p.Gly12 - Find all Cell lines from animals belonging the order "carnivora" More specifically we now offer from the Tool - API submenu 6 new options: 1) SPARQL Editor (https://lnkd.in/eF2QMsYR). The SPARQL Editor is a tool designed to assist users in developing their SPARQL queries. 2) SPARQL Service (https://lnkd.in/eZ-iN7_e). The SPARQL service is the web service that accepts SPARQL queries over HTTP and returns results from the RDF dataset. 3) Cellosaurs Ontology (https://lnkd.in/eX5ExjMe). An RDF ontology is a formal, structured representation of knowledge. It explicitly defines domain-specific concepts - such as classes and properties - enabling data to be described with meaningful semantics that both humans and machines can interpret. The Cellosaurus ontology is expressed in OWL. 4) Cellosaurus Concept Hopper (https://lnkd.in/e7CH5nj4). The Concept Hopper, is a tool that provides an alternative view of the Cellosaurus ontology. It focuses on a single concept at a time - either a class or a property - and shows how that concept is linked to others within the ontology, as well as how it appears in the data. 5) Cellosaurus dereferencing service (https://lnkd.in/eSATMhGb). The RDF dereferencing service is the mechanism that, given a URI, returns an RDF description of the resource identified by that URI, enabling clients to retrieve structured, machine-readable data about the resource from the web in different formats. 6) Cellosaurus RDF files download (https://lnkd.in/emuEYnMD). This allows you to download the Cellosaurus RDF files in Turtle (ttl) format.

How do you explain the difference between Semantic Layers and Ontologies? That’s the discussion I had yesterday with the CTO of a very large and well known organization. 📊 Semantic Layers Today: The First Stepping Stone • Semantic layer is commonly used in data analytics/BI reporting, tied to modeling fact/dimension tables and defining measures • DataLakehouse/Data Cloud, transformation tools, BI tools and semantic layer vendors exemplify this usage • Provide descriptive metadata: definitions, calculations (e.g., revenue formulas), and human readable labels, to enhance the schema • Serve as a first step toward better data understanding and governance • Help in aligning glossary terms with tables and columns, improving metadata quality and documentation • Typically proprietary (even if expressed in YAML) and are not broadly interoperable • Enable “chat with your data” experiences over the warehouse When organizations need to integrate diverse data sources beyond the data warehouse/lakehouse model, they hit the limits of fact/dimension modeling. This is where ontologies and knowledge graphs come in. 🌐 Ontologies & Knowledge Graphs: Scaling Beyond BI • Represent complex relationships, hierarchies, synonyms, and taxonomies that go beyond rigid table structures • Knowledge graphs bridge the gap from technical metadata to business metadata and ultimately to core business concepts • Enable the integration of all types of data (structured, semi-structured, unstructured) because a graph is a common model • Through open web standards such as RDF, OWL and SPARQL you get interoperability without lock in Strategic Role in the Enterprise • Knowledge graphs enable the creation of an enterprise brain, connecting disparate data and semantics across all systems inside an organization • Represent the context and meaning that LLMs lack. Our research has proven this. • They lay the groundwork for digital twins and what-if scenario modeling, powering advanced analytics and decision-making. 💡 Key Takeaway The semantic layer is a first step, especially for BI use cases. Most organizations will start with them. This will eventually create semantic silos that are not inherently interoperable. Over time, they realize they need more than just local semantics for BI; they want to model real-world business assets and relationships across systems. Organizations will realize they want to define semantics once and reuse them across tools and platforms. This requires semantic interoperability, so the meaning behind data is not tied to one system. Large scale enterprises operate across multiple systems, so interoperability is not optional, it’s essential. To truly integrate and reason over enterprise data, you need ontologies and knowledge graphs with open standards. They form the foundation for enterprise-wide semantic reuse, providing the flexibility, connectivity, and context required for next-generation analytics, AI, and enterprise intelligence. | 102 comments on LinkedIn

While industry experts continue to analyze, interpret, and act on threat data, the complexity of cyber threats necessitates solutions that can quickly convert expert knowledge into machine-readable formats.

AI and knowledge graphs will transform product documentation, especially for complex, networked systems that require configuration…

It’s been a while since I have posted about the Ontology Pipeline. With parts borrowed from library science, the Ontology Pipeline is a simple framework for building rich knowledge infrastructures. Librarians are professional stewards of knowledge, and have valuable methodologies for building information and knowledge systems for human and machine information retrieval tasks. While LinkedIn conversations seem to be wrestling with defining “what is the semantic layer”, we are failing to see the root of semantics. Semantics matter because knowledge structures, not just layers, define semantics. Semantics are more than labels or concept maps. Semantics lend structure and meaning through relationships, disambiguation of concepts, definitions and context. The Ontology pipeline is an iterative build process that is focused upon ensuring data hygiene while minding domain data, information and knowledge. I share this framework because it is how I have successfully built information and knowledge ecosystems , with or without AI. #taxonomy #ontology #metadata #knowledgegraph #ia #ai Some friends focused on building knowledge infrastructures Andrew Padilla Nagim Ashufta Ole Olesen-Bagneux Jérémy Ravenel Paco Nathan Adriano Vlad-Starrabba Andrea Gioia | 10 comments on LinkedIn

The first draft 'G' chapter of the geometric deep learning book is live! 🚀 Alice enters the magical, branchy world of Graphs and Graph Neural Networks 🕸️ (Large Language Models are there too!) I've spent 7+ years studying, researching & talking about graphs -- This text is my best attempt at conveying everything i've learnt 💎 You may read this chapter in the usual place (link in comments!) Any and all feedback / thoughts / questions on the content, and/or words of encouragement for finishing this book (pretty please! 😇) are warmly welcomed! Michael Bronstein Joan Bruna Taco Cohen | 18 comments on LinkedIn

The first part of my textbook revisions is complete and there’s now a first version of the accompanying workbook (also available here)! It’s designed partially to not substantially increase the pri…