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The future of trustworthy AI. Powered by graphs. data² has secured a groundbreaking patent for explainable AI powered by graphs. 🚨 AI hallucinations destroy trust. That's not acceptable when lives and missions are at stake. While others rush to patch traditional RAG systems, we've engineered a fundamentally different approach. Our patented innovation delivers what leaders demand: 🔍 **Complete Transparency** - Watch AI traverse relationship paths in real-time - No more black box decisions 📊 **Evidence You Can Trust** - Every conclusion links to source data - Full citation trails for audit readiness How did we build it? 🔗 **Graph-Based Architecture** - Knowledge graphs capture critical relationships traditional RAG misses - Every connection adds context and validates accuracy This isn't just innovation for innovation's sake. At data² we are solving critical challenges across: ↳ Intelligence operations requiring all-source validation ↳ Cyber threat analysis demanding instant verification ↳ Energy infrastructure decisions where safety is paramount ↳ Financial investigations tracking complex money flows ↳ Supply chain operations in contested environments While others promise AI accuracy, we've patented how to prove it. 💬 Interested in learning more? Reach out directly. 🔔 Follow me Daniel Bukowski for daily insights about delivering transparent AI with graph technology. | 90 comments on LinkedIn

Getting Started with the Graph Query Language (GQL): The complete guide to designing, querying, and managing graph databases with GQL: 9781836204015: Computer Science Books @ Amazon.com

I can't tell you how many times I've had a graph analytics idea, only to spend days trying to find decent data to test it on. 😤Sound familiar? That's why I'm excited about the talk next week by Dennis Irorere on GraphFaker - a free tool from the GraphGeeks Lab to help with the graph data problem. Good graph data is ridiculously hard to come by. It's either locked behind privacy walls, messy beyond belief, or not really relationship-centric. I've been there, we've all been there. Dennis will show us how to: - Generate realistic social networks quickly - Pull actual street network data without the headaches - Access air travel networks, Wikipedia graphs, and more 🌐 Join us on July 29 - Or register for the recording. https://lnkd.in/gBxjrWGS Whether you're in research, prototyping new features, or teaching graph algorithms, this could shorten your workflow. –And what really caught my attention is that this will allow me to focus on the fun part of testing ideas. 🤓

What's new in G.V() 3.34.79? Oracle Graph support, new no-code path query generation features, Kuzu 0.11.0 upgrade and many more improvements!

What’s the difference between context engineering and ontology engineering? We hear a lot about “context engineering” these days in AI wonderland. A lot of good thing are being said but it’s worth noting what’s missing. Yes, context matters. But context without structure is narrative, not knowledge. And if AI is going to scale beyond demos and copilots into systems that reason, track memory, and interoperate across domains… then context alone isn’t enough. We need ontology engineering. Here’s the difference: - Context engineering is about curating inputs: prompts, memory, user instructions, embeddings. It’s the art of framing. - Ontology engineering is about modeling the world: defining entities, relations, axioms, and constraints that make reasoning possible. In other words: Context guides attention. Ontology shapes understanding. What’s dangerous is that many teams stop at context, assuming that if you feed the right words to an LLM, you’ll get truth, traceability, or decisions you can trust. This is what I call “hallucination of control”. Ontologies provide what LLMs lack: grounding, consistency, and interoperability, but they are hard to build without the right methods, adapted from the original discipline that started 20+ years ago with the semantic web, now it’s time to work it out for the LLM AI era. If you’re serious about scaling AI across business processes or mission-critical systems, the real challenge is more than context, it’s shared meaning. And tech alone cannot solve this. That’s why we need put ontology discussion in the board room, because integrating AI into organizations is much more complicated than just providing the right context in a prompt or a context window. That’s it for today. More tomorrow! I’m trying to get back at journaling here every day. 🤙 hope you will find something useful in what I write. | 71 comments on LinkedIn

𝙏𝙝𝙤𝙪𝙜𝙝𝙩 𝙛𝙤𝙧 𝙩𝙝𝙚 𝙙𝙖𝙮: I've been mulling over how both OWL and SHACL can be employed during the decision-making phase for AI Agents when using a knowledge graph instead of relying on an LLM that hallucinates. In this way, the LLM can still be used for assessment and sensory feedback, but it augments the graph, not the other way around. OWL and SHACL serve different roles. SHACL is not just a preprocessing validator; it can play an active role in constraining, guiding, or triggering decisions, especially when integrated into AI pipelines. However, OWL is typically more central to inferencing and reasoning tasks. SHACL can actively participate in decision-making, especially when decisions require data integrity, constraint enforcement, or trigger-based logic. In complex agents, OWL provides the inferencing engine, while SHACL acts as the constraint gatekeeper and occasionally contributes to rule-based decision-making. For example, an AI agent processes RDF data describing an applicant's skills, degree, and experience. SHACL validates the data's structure, ensuring required fields are present and correctly formatted. OWL reasoning infers that the applicant is qualified for a technical role and matches the profile of a backend developer. SHACL is then used again to check policy compliance. With all checks passed, the applicant is shortlisted, and a follow-up email is triggered. In AI agent decision-making, OWL and SHACL often work together in complementary ways. SHACL is commonly used as a preprocessing step to validate incoming RDF data. If the data fails validation, it's flagged or excluded, ensuring only clean, structurally sound data reaches the OWL reasoner. In this role, SHACL acts as a gatekeeper. They can also operate in parallel or in an interleaved manner within a pipeline. As decisions evolve, SHACL shapes may be checked mid-process. Some AI agents even use SHACL as a rule engine—to trigger alerts, detect actionable patterns, or constrain reasoning paths—while OWL continues to handle more complex semantic inferences, such as class hierarchies or property logic. Finally, SHACL can augment decision-making by confirming whether OWL-inferred actions comply with specific constraints. OWL may infer that “A is a type of B, so do X,” and SHACL then determines whether doing X adheres to a policy or requirement. Because SHACL supports closed-world assumptions (which OWL does not), it plays a valuable role in enforcing policies or compliance rules during decision execution. Illustrated:

It’s already the end of Sunday — I hope you all had a wonderful week. Mine was exceptionally busy, with the GUG seminar and the upcoming tutorial preparation. I usually take time for a personal…

I'm trying to build a Knowledge Graph. Our team has done experiments with current libraries available (𝐋𝐥𝐚𝐦𝐚𝐈𝐧𝐝𝐞𝐱, 𝐌𝐢𝐜𝐫𝐨𝐬𝐨𝐟𝐭'𝐬 𝐆𝐫𝐚𝐩𝐡𝐑𝐀𝐆, 𝐋𝐢𝐠𝐡𝐫𝐚𝐠, 𝐆𝐫𝐚𝐩𝐡𝐢𝐭𝐢 etc.) From a Product perspective, they seem to be missing the basic, common-sense features. 𝐒𝐭𝐢𝐜𝐤 𝐭𝐨 𝐚 𝐅𝐢𝐱𝐞𝐝 𝐓𝐞𝐦𝐩𝐥𝐚𝐭𝐞: My business organizes information in a specific way. I need the system to use our predefined entities and relationships, not invent its own. The output has to be consistent and predictable every time. 𝐒𝐭𝐚𝐫𝐭 𝐰𝐢𝐭𝐡 𝐖𝐡𝐚𝐭 𝐖𝐞 𝐀𝐥𝐫𝐞𝐚𝐝𝐲 𝐊𝐧𝐨𝐰: We already have lists of our products, departments, and key employees. The AI shouldn't have to guess this information from documents. I want to seed this this data upfront so that the graph can be build on this foundation of truth. 𝐂𝐥𝐞𝐚𝐧 𝐔𝐩 𝐚𝐧𝐝 𝐌𝐞𝐫𝐠𝐞 𝐃𝐮𝐩𝐥𝐢𝐜𝐚𝐭𝐞𝐬: The graph I currently get is messy. It sees "First Quarter Sales" and "Q1 Sales Report" as two completely different things. This is probably easy but want to make sure this does not happen. 𝐅𝐥𝐚𝐠 𝐖𝐡𝐞𝐧 𝐒𝐨𝐮𝐫𝐜𝐞𝐬 𝐃𝐢𝐬𝐚𝐠𝐫𝐞𝐞: If one chunk says our sales were $10M and another says $12M, I need the library to flag this disagreement, not just silently pick one. It also needs to show me exactly which documents the numbers came from so we can investigate. Has anyone solved this? I'm looking for a library —that gets these fundamentals right. | 21 comments on LinkedIn

❓ Why I Wrote This Book? In the past two to three years, we've witnessed a revolution. First with ChatGPT, and now with autonomous AI agents. This is only the beginning. In the years ahead, AI will transform not only how we work but how we live. At the core of this transformation lies a single breakthrough technology: large language models (LLMs). That’s why I decided to write this book. This book explores what an LLM is, how it works, and how it develops its remarkable capabilities. It also shows how to put these capabilities into practice, like turning an LLM into the beating heart of an AI agent. Dissatisfied with the overly simplified or fragmented treatments found in many current books, I’ve aimed to provide both solid theoretical foundations and hands-on demonstrations. You'll learn how to build agents using LLMs, integrate technologies like retrieval-augmented generation (RAG) and knowledge graphs, and explore one of today’s most fascinating frontiers: multi-agent systems. Finally, I’ve included a section on open research questions (areas where today’s models still fall short, ethical issues, doubts, and so on), and where tomorrow’s breakthroughs may lie. 🧠 Who is this book for? Anyone curious about LLMs, how they work, and how to use them effectively. Whether you're just starting out or already have experience, this book offers both accessible explanations and practical guidance. It's for those who want to understand the theory and apply it in the real world. 🛑 Who is this book not for? Those who dismiss AI as a passing fad or have no interest in what lies ahead. But for everyone else this book is for you. Because AI agents are no longer speculative. They’re real, and they’re here. A huge thanks to my co-author Gabriele Iuculano, and the Packt's team: Gebin George, Sanjana Gupta, Ali A., Sonia Chauhan, Vignesh Raju., Malhar Deshpande #AI #LLMs #KnowledgeGraphs #AIagents #RAG #GenerativeAI #MachineLearning #NLP #Agents #DeepLearning | 22 comments on LinkedIn

What makes the "𝐒𝐞𝐦𝐚𝐧𝐭𝐢𝐜 𝐃𝐚𝐭𝐚 𝐏𝐫𝐨𝐝𝐮𝐜𝐭" so valid in data conversations today?💡 𝐁𝐨𝐮𝐧𝐝𝐞𝐝 𝐂𝐨𝐧𝐭𝐞𝐱𝐭 and Right-to-Left Flow from consumers to raw materials. Tony Seale perfectly defines the value of bounded context. …𝘵𝘰 𝘴𝘶𝘴𝘵𝘢𝘪𝘯 𝘪𝘵𝘴𝘦𝘭𝘧, 𝘢 𝘴𝘺𝘴𝘵𝘦𝘮 𝘮𝘶𝘴𝘵 𝘮𝘪𝘯𝘪𝘮𝘪𝘴𝘦 𝘪𝘵𝘴 𝘧𝘳𝘦𝘦 𝘦𝘯𝘦𝘳𝘨𝘺- 𝘢 𝘮𝘦𝘢𝘴𝘶𝘳𝘦 𝘰𝘧 𝘶𝘯𝘤𝘦𝘳𝘵𝘢𝘪𝘯𝘵𝘺. 𝘔𝘪𝘯𝘪𝘮𝘪𝘴𝘪𝘯𝘨 𝘪𝘵 𝘦𝘲𝘶𝘢𝘵𝘦𝘴 𝘵𝘰 𝘭𝘰𝘸 𝘪𝘯𝘵𝘦𝘳𝘯𝘢𝘭 𝘦𝘯𝘵𝘳𝘰𝘱𝘺. 𝘈 𝘴𝘺𝘴𝘵𝘦𝘮 𝘢𝘤𝘩𝘪𝘦𝘷𝘦𝘴 𝘵𝘩𝘪𝘴 𝘣𝘺 𝘧𝘰𝘳𝘮𝘪𝘯𝘨 𝘢𝘤𝘤𝘶𝘳𝘢𝘵𝘦 𝘱𝘳𝘦𝘥𝘪𝘤𝘵𝘪𝘰𝘯𝘴 𝘢𝘣𝘰𝘶𝘵 𝘵𝘩𝘦 𝘦𝘹𝘵𝘦𝘳𝘯𝘢𝘭 𝘦𝘯𝘷 𝘢𝘯𝘥 𝘶𝘱𝘥𝘢𝘵𝘪𝘯𝘨 𝘪𝘵𝘴 𝘪𝘯𝘵𝘦𝘳𝘯𝘢𝘭 𝘴𝘵𝘢𝘵𝘦𝘴 𝘢𝘤𝘤𝘰𝘳𝘥𝘪𝘯𝘨𝘭𝘺, 𝘢𝘭𝘭𝘰𝘸𝘪𝘯𝘨 𝘧𝘰𝘳 𝘢 𝘥𝘺𝘯𝘢𝘮𝘪𝘤 𝘺𝘦𝘵 𝘴𝘵𝘢𝘣𝘭𝘦 𝘪𝘯𝘵𝘦𝘳𝘢𝘤𝘵𝘪𝘰𝘯 𝘸𝘪𝘵𝘩 𝘪𝘵𝘴 𝘴𝘶𝘳𝘳𝘰𝘶𝘯𝘥𝘪𝘯𝘨𝘴. 𝘖𝘯𝘭𝘺 𝘱𝘰𝘴𝘴𝘪𝘣𝘭𝘦 𝘰𝘯 𝘥𝘦𝘭𝘪𝘯𝘦𝘢𝘵𝘪𝘯𝘨 𝘢 𝘣𝘰𝘶𝘯𝘥𝘢𝘳𝘺 𝘣𝘦𝘵𝘸𝘦𝘦𝘯 𝘪𝘯𝘵𝘦𝘳𝘯𝘢𝘭 𝘢𝘯𝘥 𝘦𝘹𝘵𝘦𝘳𝘯𝘢𝘭 𝘴𝘺𝘴𝘵𝘦𝘮𝘴. 𝘋𝘪𝘴𝘤𝘰𝘯𝘯𝘦𝘤𝘵𝘦𝘥 𝘴𝘺𝘴𝘵𝘦𝘮𝘴 𝘴𝘪𝘨𝘯𝘢𝘭 𝘸𝘦𝘢𝘬 𝘣𝘰𝘶𝘯𝘥𝘢𝘳𝘪𝘦𝘴. Data Products enable a way to bind context to specific business purposes or use cases. This enables data to become: ✅ Purpose-driven ✅ Accurately Discoverable ✅ Easily Understandable & Addressable ✅ Valuable as an independent entity 𝐓𝐡𝐞 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧: The Data Product Model. A conceptual model that precisely captures the business context through an interface operable by business users or domain experts. We have often referred to this as The Data Product Prototype, which is essentially a semantic model and captures information on: ➡️ Popular Metrics the Business wants to drive ➡️ Measures & Dimensions ➡️ Relationships & formulas ➡️ Further context with tags, descriptions, synonyms, & observability metrics ➡️ Quality SLOs - or simply, conditions necessary ➡️ Additional policy specs contributed by Governance Stewards Once the Prototype is validated and given a green flag, development efforts kickstart. Note how all data engineering efforts (left-hand side) are not looped in until this point, saving massive costs and time drainage. The DE teams, who only have a partial view of the business landscape, are now no longer held accountable for this lack in strong business understanding. 𝐓𝐡𝐞 𝐨𝐰𝐧𝐞𝐫𝐬𝐡𝐢𝐩 𝐨𝐟 𝐭𝐡𝐞 𝐃𝐚𝐭𝐚 𝐏𝐫𝐨𝐝𝐮𝐜𝐭 𝐦𝐨𝐝𝐞𝐥 𝐢𝐬 𝐞𝐧𝐭𝐢𝐫𝐞𝐥𝐲 𝐰𝐢𝐭𝐡 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬. 🫠 DEs have a blueprint to refer and simply map sources or source data products to the prescribed Data Product Model. Any new request comes through this prototype itself, managed by Data Product Managers in collaboration with business users. Dissolving all bottlenecks from centralised data engineering teams. At this level, necessary transformations are delivered, 🔌 that activate the SLOs 🔌 enable interoperability with native tools and upstream data products, 🔌 allow reusability of pre-existing transforms in the form of Source or Aggregate data products. #datamanagement #dataproducts

Maria Chudnovsky reflects on her journey in graph theory, her groundbreaking solution to the long-standing perfect graph problem, and the unexpected ways this abstract field intersects with everyday life.

A full working Cypher script that simulates a Tendering System with multiple workflows, AI agent interactions, conversations, approvals, and more — all modeled and executed natively in a Graph.

Release announcement for Kuzu 0.11.0

In this post, we describe how to improve the quality of the Cypher queries generated by Text2Cypher via graph schema pruning, viewed through the lens of context engineering.

This blog post provides a hands-on guide for AI engineers and developers on how to build an initial KYC agent prototype with the OpenAI Agents SDK. We'll explore how to equip our agent with a suite of tools (including MCP Server tools) to uncover and investigate potential fraud patterns.

Transform Claude's Hidden Memory Into Interactive Knowledge Graphs Universal tool to visualize any Claude user's memory.json in beautiful interactive graphs. Transform your Claude Memory MCP data into stunning interactive visualizations to see how your AI assistant's knowledge connects and evolves over time. Enterprise teams using Claude lack visibility into how their AI assistant accumulates and organizes institutional knowledge. Claude Memory Viz provides zero-configuration visualization that automatically finds memory files and displays 72 entities with 93 relationships in real-time force-directed layouts. Teams can filter by entity type, search across all data, and explore detailed connections through rich tooltips. The technical implementation supports Claude's standard NDJSON memory format, automatically detecting and color-coding entity types from personality profiles to technical tools. Node size reflects connection count, while adjustable physics parameters enable optimal spacing for large knowledge graphs. Built with Cytoscape.js for performance optimization. Built with the philosophy "Solve it once and for all," the tool works for any Claude user with zero configuration. The visualizer automatically searches common memory file locations, provides demo data fallback, and offers clear guidance when files aren't found. Integration requires just git clone and one command execution. This matters because AI memory has been invisible to users, creating trust and accountability gaps in enterprise AI deployment. When teams can visualize how their AI assistant organizes knowledge, they gain insights into decision-making patterns and can optimize their AI collaboration strategies. 👩‍💻https://lnkd.in/e__RQh_q | 10 comments on LinkedIn

Get this comprehensive guide from Manning on how to build a GraphRAG system from scratch. Available now for free!

Relational thinker curious about graphs? Explore the shift from tables to nodes and relationships, with practical insights and Cypher examples.

From Message Passing to Global Attention

This is it. This is the conversation every leadership team needs to be having right now. "The Orchestration Graph" by WRITER product leader Matan-Paul Shetrit linked in comments is a must-read. The primary constraint on business is no longer execution. It's supervision. For a century, we built companies to overcome the high cost of getting things done. We built hierarchies, departments, and complex processes — all to manage labor-intensive execution. That era is over. With AI agents, execution is becoming abundant, on-demand, and programmatic. The new bottleneck is our ability to direct, govern, and orchestrate this immense new capacity. The firm is evolving from a factory into an "operating system." Your ORG CHART is no longer the map. The real map is the Orchestration Graph: the dynamic, software-defined network of humans, models, and agents that actually does the work. This isn't just a new tool or a productivity hack. It's a fundamental rewiring of the enterprise. It demands we rethink everything: Structure: How do we manage systems, not just people? Strategy: What work do we insource to our agentic "OS" versus outsource to models-as-a-service? Metrics: Are we still measuring human activity, or are we measuring system throughput and intelligence? This is the WRITER call to arms: The companies that win won't just adopt AI; they will restructure themselves around it. They will build their own Orchestration Graph, with governance and institutional memory at the core. They will treat AI not as a feature, but as the new foundation. At WRITER, this is the future we are building every single day — giving companies the platform to create their own secure, governed, and intelligent orchestration layer. The time to act is now. Read the article. Start the conversation with your leaders. And begin rewiring your firm. | 37 comments on LinkedIn

When people discuss how LLMS "reason," you’ll often hear that they rely on transduction rather than abduction. It sounds technical, but the distinction matters - especially as we start wiring LLMs into systems that are supposed to think. 🔵 Transduction is case-to-case reasoning. It doesn’t build theories; it draws fuzzy connections based on resemblance. Think: “This metal conducts electricity, and that one looks similar - so maybe it does too.” 🔵 Abduction, by contrast, is about generating explanations. It’s what scientists (and detectives) do: “This metal is conducting - maybe it contains free electrons. That would explain it.” The claim is that LLMs operate more like transducers - navigating high-dimensional spaces of statistical similarity, rather than forming crisp generalisations. But this isn’t the whole picture. In practice, it seems to me that LLMs also perform a kind of induction - abstracting general patterns from oceans of text. They learn the shape of ideas and apply them in novel ways. That’s closer to “All metals of this type have conducted in the past, so this one probably will.” Now add tools to the mix - code execution, web search, Elon Musk's tweet history 😉 - and LLMs start doing something even more interesting: program search and synthesis. It's messy, probabilistic, and not at all principled or rigorous. But it’s inching toward a form of abductive reasoning. Which brings us to a more principled approach for reasoning within an enterprise domain: the neuro-symbolic loop - a collaboration between large language models and knowledge graphs. The graph provides structure: formal semantics, ontologies, logic, and depth. The LLM brings intuition: flexible inference, linguistic creativity, and breadth. One grounds. The other leaps. 💡 The real breakthrough could come when the grounding isn’t just factual, but conceptual - when the ontology encodes clean, meaningful generalisations. That’s when the LLM’s leaps wouldn’t just reach further - they’d rise higher, landing on novel ideas that hold up under formal scrutiny. 💡 So where do metals fit into this new framing? 🔵 Transduction: “This metal conducts. That one looks the same - it probably does too.” 🔵 Induction: “I’ve tested ten of these. All conducted. It’s probably a rule.” 🔵 Abduction: “This metal is conducting. It shares properties with the ‘conductive alloy’ class - especially composition and crystal structure. The best explanation is a sea of free electrons.” LLMs, in isolation, are limited in their ability to perform structured abduction. But when embedded in a system that includes a formal ontology, logical reasoning, and external tools, they can begin to participate in richer forms of reasoning. These hybrid systems are still far from principled scientific reasoners - but they hint at a path forward: a more integrated and disciplined neuro-symbolic architecture that moves beyond mere pattern completion.

🚀 When I shared our 2025 goals for the Enterprise Data Organization, one of the things I alluded to was machine-readable column-level metadata. Let’s unpack what that means—and why it matters. 🔍 What: For datasets we deliver via modern cloud distribution, we now provide human - and machine - readable metadata at the column level. Each column has an immutable URL (no auth, no CAPTCHA) that hosts name/value metadata - synonyms, units of measure, descriptions, and more - in multiple human languages. It’s semantic context that goes far beyond what a traditional data dictionary can convey. We can't embed it, so we link to it. 💡 Why: Metadata is foundational to agentic, precise consumption of structured data. Our customers are investing in semantic layers, data catalogs, and knowledge graphs - and they shouldn’t have to copy-paste from a PDF to get there. Use curl, Python, Bash - whatever works - to automate ingestion. (We support content negotiation and conditional GETs.) 🧠 Under the hood? It’s RDF. Love it or hate it, you don’t need to engage with the plumbing unless you want to. ✨ To our knowledge, this hasn’t been done before. This is our MVP. We’re putting it out there to learn what works - and what doesn’t. It’s vendor-neutral, web-based, and designed to scale across: 📊 Breadth of datasets across S&P 🧬 Depth of metadata 🔗 Choice of linking venue 🙏 It took a village to make this happen. I can’t name everyone without writing a book, but I want to thank our executive leadership for the trust and support to go build this. Let us know what you think! 🔗 https://lnkd.in/gbe3NApH Martina Cheung, Saugata Saha, Swamy Kocherlakota, Dave Ernsberger, Mark Eramo, Frank Tarsillo, Warren Breakstone, Hamish B., Erica Robeen, Laura Miller, Justine S Iverson, | 17 comments on LinkedIn

Part 6 of 6 in the series “LLMs Need Knowledge Graphs. Use RDF or End Up Rebuilding It.”

By a mildly exasperated knowledge engineer

Temporal graph modeling, enhanced by our time-forest approach, tackles the dynamic nature of credit fraud.

TigerGraph secures a strategic investment from Cuadrilla Capital to fuel innovation in enterprise AI infrastructure and graph database technology, delivering advanced solutions for fraud detection, customer 360, supply chain optimization, and real-time data analytics.

Introducing metis: an enterprise AI platform from metaphactory. Get trusted, context-aware, knowledge-driven AI for actionable insights & intelligent agents.

💡 Should ontologies be treated as organizational resources for semantic capabilities? More and more organizations are investing in data platforms, modeling tools, and integration frameworks. But one key capability is often underused or misunderstood: ontologies as semantic infrastructure. While databases handle facts and BI platforms handle queries, ontologies structure meaning. They define what things are, not just what data says. When treated as living organizational resources, ontologies can bring: 🔹 Shared understanding across silos 🔹 Reasoning and inference beyond data queries 🔹 Semantic integration of diverse systems 🔹 Clarity and coherence in enterprise models But here’s the challenge — ontologies don’t operate in isolation. They must be positioned alongside: 🔸 Data-oriented technologies (RDF, RDF-star, quad stores) that track facts and provenance 🔸 Enterprise modeling tools (e.g., ArchiMate) that describe systems and views 🔸 Exploratory approaches (like semantic cartography) that support emergence over constraint These layers each come with their own logic — epistemic vs. ontologic, structural vs. operational, contextual vs. formal. ✅ Building semantic capabilities requires aligning all these dimensions. ✅ It demands governance, tooling, and a culture of collaboration between ontologists, data managers, architects, and domain experts. ✅ And it opens the door to richer insight, smarter automation, and more agile knowledge flows. 🔍 With projects like ArchiCG (semantic interactive cartography), I aim to explore how we can visually navigate this landscape — not constrained by predefined viewpoints, but guided by logic, meaning, and emergent perspectives. What do you think? Are ontologies ready to take their place as core infrastructure in your organization? | 16 comments on LinkedIn

Nice piece on the comparisons of Vector DBs vs Knowledge Graphs. I think this becomes even more true when you start talking about temporal knowledge graphs, in which you are effectively describing temporal causality and contingent assertions.

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