🚀 The wait is over! The Call for Submissions for #CDL26 is NOW OPEN. Be a part of the celebration: 10 Years Connecting Data, People and Ideas The leading global technology conference for those using Relationships, Meaning, and Context in Data to achieve great things.  Join us in the heart of London as we celebrate a decade of innovation in Knowledge Graphs, Graph Analytics, Data Science, AI, Graph Databases, Semantic Tech and Ontology this November. Share your use cases and breakthroughs. Submissions are open across 2 areas: Presentations: Real world use cases and innovative approaches across 3 tracks: Nodes, focus on use cases, Edges, focus on innovation, Educational, focus on applications. Masterclasses:  Hands-on tutorials in which instructors teach attendees skills they can use in their daily work. Why Speak at CDL26? Global Platform: Join 350 luminaries who have graced our stage and reach our ever-growing global audience of thousands. Adoption and Innovation: From the resurgence of Ontologies to the cutting edge of Agentic AI and Context Graphs. Speaker Benefits: Free event pass, speaker guidance, and exclusive network discounts. 📅 Deadline: Aug 31 ✅  Notification of Acceptance - September 14, 2026  Topics of interest and submission guidelines here: 🔗 connected-data.london/2026-c… #ConnectedData #KnowledgeGraphs #DataScience #AI #GraphDB #Analytics #SemTech #EmergingTech

Knowledge Graphs as the Missing Data Layer for LLM-Based Industrial Asset Operations How much does the data model behind the tools matter? Industrial asset management generates vast quantities of structured data: sensor telemetry, work orders, failure mode analyses, equipment hierarchies, and maintenance schedules. The rise of Large Language Models (LLMs) has prompted efforts to build autonomous agents that can reason over this data—answering operational questions, predicting failures, and recommending maintenance actions. LLM-based agents for industrial asset operations show limited accuracy when reasoning over flat document stores. AssetOpsBench provides the first systematic evaluation of such agents, benchmarking seven contemporary LLMs across 141 expert-curated maintenance scenarios. It establishes that GPT-4 agents achieve 65% on 139 industrial maintenance scenarios, and compares LLM orchestration paradigms (Agent-As-Tool vs. Plan-Execute) on a fixed data layer. This approach from Samyama transforms the AssetOpsBench data sources into a typed knowledge graph using an 8-step ETL pipeline and treats a typed knowledge graph as a grounding substrate, routing each question by how it is best answered:  (i) LLM-generated Cypher for structured retrieval, which lifts the same GPT-4 model from 65% to 82-83%;  (ii) native graph and optimization primitives, with no LLM, reaching 99% on graph-answerable scenarios; and  (iii) generation-augmented knowledge (GAK) for answers absent from the data -- the engine's agent materializes the missing facts as provenance-tagged graph nodes, then answers.  A recurring theme is inverted LLM usage: constraining the LLM to query generation or one-shot enrichment from a typed schema and letting the graph execute deterministically. On the 88 real AssetOpsBench failure-mode scenarios the benchmark itself flags non-deterministic -- ten equipment types absent from the graph -- GAK lifts answerability from zero to 100% of equipment types and answers 81.8% of scenarios, every materialized fact tagged source:LLM-derived for auditability. This work also contributes 40 graph-native scenarios.  For structured operational domains the data layer -- not the LLM orchestration -- is the primary lever, and a typed knowledge graph serves as a grounding substrate between raw industrial data and LLM reasoning. By Madhulatha Mandarapu, Sandeep Kunkunuru arxiv.org/abs/2605.26874 #LLM #EmergingTech #AssetManagement #Research #Innovation #DataEngineering #KnowledgeEngineering -- 💬 ‘A great newsletter’ - Claudia Remlinger, former Sr. Marketing Director, Neo4j.  Join readers from Amazon, Capgemini, Michelin, Neo4j & more Subscribe to the Year of the Graph newsletter for quarterly updates and insights on all things #KnowledgeGraph, #GraphDB, Graph #Analytics / #DataScience / #AI and #SemTech 👇 yearofthegraph.xyz/newslette…

The Early Bird clock is ticking. ⏱️ In just 3 days, ticket prices for #CDL26 will increase across all pass types - including All-Access, Day Passes, and Remote. This is your final window to join us at the Leonardo Royal Hotel London Tower Bridge while saving 30%. What’s on the table: ✅ Technical deep-dives into Knowledge Graphs and LLMs. ✅ Hands-on Masterclasses. ✅ Networking with the world’s top data architects. Don’t wait for the deadline. Save 30% and book your pass today. 👉 2026.connected-data.london/c… #CDL26 #ConnectedData #KnowledgeGraphs #DataScience #AI #GraphDB #Analytics #SemTech #EmergingTech #EnterpriseData #DataStrategy #AIArchitecture

Leveraging Knowledge Graphs for Disrupting Criminal Networks This presentation proposes a three-step process that utilises knowledge graphs to empower law enforcement agencies in criminal intelligence gathering and criminal network analysis. The approach tackles the challenge of siloed data hindering effective crime investigation. Step 1: Transitioning to Holistic Intelligence Data The paper acknowledges the data analysis hurdle faced by law enforcement due to disparate data sources. It proposes transitioning to a knowledge graph to unify these sources and create a holistic view of criminal intelligence data. The knowledge graph collates structured and unstructured data - internal to police forces and from open source data (ex. OSINT) - and resolve entities. This step also leverages LLMs to properly convert text in organised knowledge. Step 2: From Search to Exploration in Criminal Intelligence The second step advocates for leveraging the knowledge graph's ability to explore data connections. This enables analysts to navigate connections among people and events, fostering deeper investigative insights compared to traditional search methods. Graphs facilitate this task in terms of speed and understandability. Step 3: From Analytics to Predictive Policing Finally, the presentation explores how knowledge graphs can be used for predictive policing. By analysing patterns within the graph, the approach helps identify potential criminal activity and key network players. Also, it allows to reveal hidden connections and to relate crimes to potential offenders empowering modern suspect nomination and risk assessment functions. The predictions provided are fully explainable to guarantee transparency in police operations.  Overall Benefit The presentation positions knowledge graphs as a transformative tool for criminal intelligence, aiding disruption of criminal networks regardless their size and structure, improved decision-making, and ultimately, enhanced public safety. youtube.com/watch?v=QcWsGQeI… -- Alessandro Negro. Chief Scientist, GraphAware Alessandro Negro is a Chief Scientist at GraphAware. He holds a Ph.D. in Computer Science and has authored several publications on graph-based machine learning. -- Welcome to Connected Data London's #ThrowbackThursday Every Thursday at 3pm GMT, we are releasing gems from our vault on #YouTube Tune in and learn from leaders and innovators; subscribe to our channel and watch premieres as they are released!  #knowledgegraph #graphdatabase #graph #AI #datascience #analytics #semtech #ontology

Your agent's memory problem is an information architecture problem 🧠 Most AI agent frameworks handle "memory" the same way: turn text into embeddings, store them, and retrieve whatever seems most similar to the current query. For demos, that works fine. But for an agent that needs to actually retain knowledge across sessions, notice when something has changed, and resolve contradictions, Artur Ciocanu argues this approach falls apart fast. His example: every major vector database now combines similarity search with old-fashioned keyword matching, because similarity alone wasn't good enough. If your modern retrieval system needs a decades-old technique as a crutch, maybe similarity was never the right foundation for "knowledge" to begin with. The essay traces a path through three disciplines. Computer science gives you excellent containers (databases, indexes, search) but no sense of what the data means or how it relates to other data. Cognitive science offered the idea of "episodic" vs "semantic" memory, borrowed from how human brains work. But human memory is built to be reconstructive and a bit lossy. Agent knowledge needs to be the opposite: precise, versioned, and consistent.  Mem0, a popular memory tool, illustrates the tension well: its newest version stores every new fact rather than updating old ones, on the theory that conflicts get sorted out later at retrieval time. The community has flagged cases where this clearly breaks (the system stores "my name is Alice" and "my name is Bob" as two separate facts, both retrieved with equal confidence). The third discipline is the unexpected one: library science, plus its close cousins, ontology and knowledge engineering. Librarians have spent over a century solving exactly this problem: how do you organize information so someone can find the right thing later, in a context you can't predict in advance? That tradition offers tools that should sound familiar to anyone working with knowledge graphs: deciding at the point of storage whether something is worth keeping at all, normalizing different terms that mean the same thing into one canonical concept, systematically deciding what to discard rather than just letting it fade from memory, and trusting facts differently depending on where they came from (something a person told you directly versus something the system guessed). The essay reframes the whole problem: an agent's memory isn't a brain. It's more like a customer data platform, where different agents are channels writing information about a person into a shared profile. The piece builds on the work of Jessica Talisman (library science and information architecture for AI) and Kurt Cagle (ontology engineering), and points to Hindsight, a research system from Vectorize.io, as evidence the field is independently rediscovering these same ideas under benchmark pressure. An interesting read for anyone working with knowledge graphs, ontologies, or skeptical of the idea that retrieval alone equals memory. arturciocanu.substack.com/p/… -- #EmergingTech #OntologyEngineering #AIAgents #InformationArchitecture #DataManagement Connected Data London 2026 | 11–12 November | Leonardo Royal Hotel London Tower Bridge 🎤 Share your work with the world's most passionate data community. The Call for Submissions is open.  connected-data.london/2026-c… 🎟 Tickets on sale now. Early bird discounts up to 30%. 2026.connected-data.london 📺 Sponsorship opportunities available. Contact info@connected-data.london for details. #KnowledgeGraph #GraphRAG #Ontology #Graph #AI #DataScience #GraphDB #SemTech

Everyone needs a context layer, but what exactly is it and how do you get one? Many organizations are looking for advice on private context. They all start the same way: "how do we make our context usable for agents?" The phrasing is always the same. What's underneath it never is. The moment you look closely, it breaks apart into several different problems that only look alike from a distance. What actually sorts this space, Elisenda Bou-Balust argues, is the kind of context you're dealing with. That decides what exists to work with and which tools are even on the table. Together with Miguel Arias they consolidated the whole landscape into one map. The AI Context Layer Market Map lays out the entire space on a single page: → Bucket 1: the agent's context. Its own memory and operating manual, sorted by how much you model: pure RAG → hybrid → graph-native → full ontology. → Bucket 2: institutional knowledge. The scattered docs, chats and tickets your company already lives in. Surface-owners (who own where work happens) vs. neutral layers (who index across everything). → Bucket 3: systems of record. The hard operational data, split by who (or what) generates it: human-generated business records and machine-generated telemetry. Each side has its surface-owners and its neutral layers: centralized, federated, semantic. → Plus the two things that cut across all of it: governance & trust (the tax that scales with you), and the emerging public data context layer (verified facts and data from the world, so agents can query external knowledge). Caveat: it's not exhaustive and it's a snapshot of a fast-moving space. Plus, as Tony Seale argues: You Can't Buy Your Own Context Everyone is suddenly racing to sell you your own context. But a context you can buy is a context your competitors can buy too. Barely a week passes now without another "context layer", "context graph", or "context foundation" for your AI agents - your organisation's reasoning, its exceptions, its hard-won workarounds, packaged up and ready to plug in.  The whole industry is starting to agree that context is the missing piece. It sounds like precisely what the moment demands. But here is the kicker - it is precisely the thing you cannot just buy. Your context is not a feature you bolt on. It is your model of your own world - the thing that lets your organisation perceive, predict, and act as a single, coherent entity.  Every system that survives does the same thing: it holds a boundary between itself and the world, and works without pause to keep what's inside coherent against a world that never stops trying to throw it off.  That boundary is the difference between being a system and being a pile of parts. Your connected data, your ontology, your formalised meaning is that boundary. GraphAI is the category where graph structure stops being passive infrastructure and starts doing active work, shaping what models retrieve, what agents remember, and what machine learning algorithms learn. State of the Graph is mapping a new frontier: how graphs are being used inside AI systems.  Every AI company needs a context layer. Nobody agrees what that is. linkedin.com/pulse/every-ai-… The AI Context Layer Market Map linkedin.com/posts/elisendab… Why You Can't Buy Your Own Context linkedin.com/posts/tonyseale… GraphAI as the Emerging Frontier on the Graph World Map stateofthegraph.com/2026/04/… -- The Year of the Graph's Spring 2026 newsletter issue on all things #KnowledgeGraph, #GraphDB, Graph #Analytics / #DataScience / #AI and #SemTech is coming soon. Subscribe and follow to be in the know. Reach out if you'd like to be featured 👇 yearofthegraph.xyz/newslette…

Programme Update: New Speakers from Gartner, Scania and Google 🎤 The Connected Data London 2026 programme continues to expand. We are pleased to introduce our latest speaker additions for our 10th anniversary event in London: Vinay Balasubramaniam (Google): Director of Product Management for BigQuery, our Gold Sponsor. He will discuss the BigQuery Core SQL Engine, price performance and advanced analytics covering vector search, graph analytics and AI co processing, drawing on his extensive background leading enterprise platform strategy. Afraz Jaffri (Gartner): A VP Analyst delivering strategic market insights on the intersection of AI, context and knowledge graphs. He will break down how to implement graph based solutions for reliable agentic systems, drawing on a decade of experience advising enterprise leaders on data platform strategy. Bei Li (Google): A founder of Spanner Graph and BigQuery Graph from our Gold Sponsor team. He will share the technical vision and engineering development behind both graph systems since their inception, alongside insights from building foundational data infrastructure for Google Search and Ads. Nikos Trokanas (Scania Group): An Ontology Architect specialising in knowledge graphs, ontology engineering and generative AI integration. He will share practical insights from his 15 years of experience delivering semantic infrastructure across the automotive, finance and biomedical sectors. Read more about our speakers and their upcoming sessions: 👉 2026.connected-data.london/?… #CDL26 #ConnectedData #KnowledgeGraphs #DataArchitecture #Gartner #Scania #Google #BigQuery #AI Ps. Early Bird pricing ends Monday 15 June. Book your ticket now for the lowest rate: 👉 2026.connected-data.london/

Build Meaning Before Machines: Why Semantics, Ontologies, And Knowledge Graphs Matter For Agentic AI Forrester's analysis notes that Agentic AI is exposing a foundational gap in most enterprise data strategies: Data without meaning is unusable for autonomous systems. Agents don’t just retrieve data — they interpret, decide, and act. Without explicit context, they guess. And when agents guess, they get joins wrong, misinterpret metrics, and act on flawed assumptions. This is why ontologies, semantic layers, and knowledge graphs are rapidly becoming core architectural components. They provide what agentic systems lack in traditional data environments: a shared language, explicit relationships, and machine-readable context. Two recently published reports give leaders clear definitions for semantics, ontologies, and knowledge graphs and provide a path for enterprises to get started on their AI transformation journey. Semantic Layers Are The Starting Point Make Data AI Ready Via Semantic Layer Platforms (with Noel Yuhanna) focuses on the first step in this journey: making data interpretable before making it intelligent.  Semantic layers have long ensured business-intelligence consistency. In the agentic era, they also give agents the governed context needed to turn natural language into accurate queries and actions. Modern semantic layer platforms also extend beyond metric definitions with runtime services, APIs, lineage, and policy enforcement across hybrid and multicloud environments — keeping business meaning stable as platforms change.  The report also introduces the data graph as a bridge to knowledge graphs, capturing relationships and usage patterns so organizations can give agents more context without jumping directly to a full knowledge graph architecture. Knowledge Graphs Define The Destination Combine Semantics, Ontology, And Knowledge Graphs For AI-Ready Data (with Indranil Bandyopadhyay and Charlie Dai) demystifies semantics, ontology, and knowledge graphs as terms.  The report suggests a desired end state: a semantically rich enterprise where all enterprise entities are not just connected but understood. It proposes a layered approach in which ontologies define knowledge, semantics enforce clarity and consistency, and knowledge graphs connect these elements into a model that supports reasoning and discovery.  Knowledge graphs are more than a data integration technique; they form the foundation of an enterprise digital twin. By making all enterprise entities and relationships explicit, they help AI interpret context, infer connections, and act more accurately across domains. Start With Semantics, Then Evolve To A Digital Twin The two reports together define a clear evolution path. Most organizations are not yet ready to build a knowledge graph. The semantic layer is the right starting point. It creates a consistent foundation of meaning: standardized definitions, governed metrics, and shared logic across tools and teams. The knowledge graph is the long-term destination — a form of digital twin that enables agentic AI to reason and act across the enterprise. forrester.com/blogs/build-me… #AgenticAI #KnowledgeGraph #SemanticLayer #DataStrategy #EnterpriseAI #DigitalTwin #AIReadyData -- Connected Data London 2026 | 11–12 November | Leonardo Royal Hotel London Tower Bridge 🎤 Share your work with the world's most passionate data community. The Call for Submissions is open.  connected-data.london/2026-c… 🎟 Tickets on sale now. Early bird discounts up to 30%. 2026.connected-data.london?u… 📺 Sponsorship opportunities available. Contact info@connected-data.london for details. #KnowledgeGraph #GraphRAG #Ontology #Graph #AI #DataScience #GraphDB #SemTech

Context Graphs are a convergence, and convergence needs architecture Charles Betz of Forrester Research published a piece titled "Context Graphs Are a Convergence, Not an Invention", and it deserves to be read widely. Having a VP-level analyst at a major research firm put it in writing, with the historical inventory to back it up, is genuinely significant. It signals that this conversation has moved from the practitioner fringe into the mainstream enterprise consciousness. Betz traces the lineage back 40 years: Zachman's enterprise architecture framework in 1987, the ITIL push for configuration management databases in the 1990s, APM in the early 2000s, process mining, ChatOps, organisational network analysis, FinOps, software bills of materials, and architecture decision records. His central observation: none of these systems talk to each other, and the convergence the VC community is declaring as a greenfield opportunity is in fact the long-overdue integration of work that's been accumulating in silos for four decades. Kurt Cagle extends the argument, identifying three structural gaps that "context graph" as a term does not resolve: The entity resolution gap -- a flat context graph doesn't solve it. You need a formal registration mechanism: a way to declare that an entity exists, give it a canonical identifier, and establish that the various local identifiers in legacy systems refer to it. The events-versus-state gap -- process mining logs and APM traces are event records. CMDBs and EA capability maps are state records. Conflating the two in a single knowledge graph doesn't unify them; it obscures the distinction that makes each useful. The governance gap -- "Who owns this graph?" is actually several questions at once. Governance has to be built into the architecture itself, not answered after the fact. The proposed answer is holonic architecture -- a unit that has stable, dereferenceable identity, a formal separation between infrastructure layer and payload, a machine-enforceable boundary, and governed, audited portals between domains. The W3C RDF stack (RDF 1.2, OWL 2, SHACL 1.2, SPARQL 1.2) is the only implementation substrate that arrives vendor-neutral, with formal semantics and decades of standardisation behind it. The question before the context graph community is whether the convergence happens as a coherent, formally specified, openly governed architecture -- or as a collection of incompatible vendor implementations, each claiming to be the "system of record for decisions," none of them able to talk to the others. The map is not the territory. But a good map needs more than a title; it needs a cartographic system. By Kurt Cagle linkedin.com/pulse/context-g… #EnterpriseArchitecture #SemanticWeb #ContextGraphs #OpenStandards -- Join the Conversation Subscribe to the Year of the Graph newsletter for quarterly insights on #KnowledgeGraphs, #GraphDB, Graph #Analytics, #AI, #DataScience and #SemTech . 📧 Subscribe: yearofthegraph.xyz/newslette…  💼 Sponsorship inquiries: yearofthegraph.xyz/contact/

Building a value-driven Enterprise Knowledge Graph at Rabobank: From foundations to adoption In large enterprises like Rabobank, customer data is often fragmented across systems, making it difficult to gain a unified view of customer relationships, product interaction, and potential business risks. This fragmentation limits operational efficiency, regulatory compliance, and the ability to deliver personalized services.  To address this, we developed an Enterprise Knowledge Graph (EKG) that connects siloed data into a single, coherent Global Customer View, accessible across the organization and integrated with Data & Analytics Platforms. The solution is making real business impact across the enterprise in domains like Know Your Customer (KYC), business lending and more. We share our practical approach for: Designing a Minimum Viable Graph to deliver early value Transitioning from full to incremental data loads The need for performing entity matching / resolution Driving adoption by integrating the EKG into existing tools and workflows How GenAI helped to accelerate the need for Enterprise Knowledge Graphs This talk is for business and technical professionals at a beginner to intermediate level who are exploring how to build or scale knowledge graphs in complex environments. No prior graph experience is required, but familiarity with enterprise data challenges will be helpful. The case is relevant in and outside of the financial services / banking industry. Attendees walk away with a clear understanding of how to approach knowledge graph development in a large organization, from initial design to enterprise-wide adoption.  Link to talk: 2025.connected-data.london/t… -- Colin van Lieshout. Solution Architect, Rabobank Colin is a pragmatic and visionary Solution Architect, driving the adoption and scaling of graph technology at Rabobank. Stijn Oude Elferink. Product Manager, Rabobank Stijn is a curious Product Manager who has been responsible for the Knowledge Graph strategy in Rabobank. -- Welcome to Connected Data London's #TeaserTuesday Every Tuesday, we share teasers from #CDL25 on our channels Connected Data London 2025 brought together leaders and innovators. Were you there? 🎥 Watch the sessions: 2025.connected-data.london/ 📩 Join the community: connected-data.london Tune in and learn from leaders and innovators; subscribe and watch premieres as they are released!  Join community legends and new voices in #CDL25 for all things #KnowledgeGraph #Graph #analytics #datascience #AI #graphDB #SemTech #Ontology

Atlassian Teamwork Graph: The Secret Weapon That's No Longer a Secret Atlassian has expanded Teamwork Graph from an internal product feature into a programmable developer platform — and the connected work implications are significant. Teamwork Graph unifies data across Atlassian and 100 popular apps. It collects your work, pages, ideas, service requests, projects, and more. The promise: Maximize your data’s potential effortlessly, intelligently connect any team, work, and app on a single platform with Teamwork Graph. At Team '26, Atlassian announced general availability of Teamwork Graph Connectors via Forge, an open beta of the Teamwork Graph CLI with 300 commands, and two new Rovo MCP Server tools that expose graph context to external AI agents. The Teamwork Graph contains more than 150 billion objects and relationships across Jira, Confluence, Bitbucket, Loom, JSM, and 75 third-party tools including GitHub, Google Docs, and Figma — all accessible via single authentication. But here's what matters for connected work: the graph doesn't just connect things, it understands the relationships between people, projects, documents, and decisions. When it's working, the system knows what you're trying to do. Context shows up where it's needed — without switching tools, chasing context, or stitching together fragments from a dozen sources. As Mitch Ashley of Futurum observes: context stops being an internal feature of someone else's product and becomes addressable infrastructure. A marketer asking Rovo, a developer asking Claude Code, and a Jira incident agent examining the codebase all read from one graph. Answers stay consistent. The audit trail collapses into a single boundary. Grounding agent responses in Teamwork Graph data delivered 44% more accurate results while using 48% fewer tokens in internal benchmarks. Mercedes-Benz reports a 10x increase in software delivery speed after building custom Forge connectors against the graph. AI isn't the magic here — it's the multiplier. The real transformation happens when connected data and consistent ways of working let AI act like a teammate, not just a tool. By Mitch Ashley (Futurum) Atlassian Teamwork Graph atlassian.com/platform/teamw… Atlassian Teamwork Graph: The context engine behind your AI—everywhere atlassian.com/blog/company-n… Atlassian Teamwork Graph: The Secret Weapon That’s No Longer a Secret futurumgroup.com/insights/at… #ConnectedWork #EnterpriseAI #GraphData #AgenticAI -- Connected Data London 2026 | 11–12 November | Leonardo Royal Hotel London Tower Bridge 🎤 Share your work with the world's most passionate data community. The Call for Submissions is open.  connected-data.london/2026-c… 🎟 Tickets on sale now. Early bird discounts up to 30%. 2026.connected-data.london 📺 Sponsorship opportunities available. Contact info@connected-data.london for details. #KnowledgeGraph #GraphRAG #Ontology #Graph #AI #DataScience #GraphDB #SemTech

Power in AI is not only a merit system. It is a network system. This is the core argument of The AI Power Map: a free 70,000-word companion book with an interactive graph of 420 people and 1,709 documented edges tracing how influence, trust, talent, and capital move across the AI industry. The graph is the method. 8 methods of analysis were applied across the dataset, including betweenness centrality, community detection, motif analysis, and cofounder cluster identification.  The result is a map of recurring structural patterns: talent pipelines, diaspora arcs, acqui-hire chains, and the trust bridges that survive org chart changes. Key findings from the network: Stanford (77 nodes) and Google (#1 exporter) are the two dominant talent factories. School lineage, not company affiliation, is the stronger cohesion signal:  Sutskever, Gomez, and Karpathy share a Hinton-Toronto lineage across 5 different employers, visible in the graph edges, not inferred. Network position is the strongest visible predictor of tier. T1 individuals average significantly more documented connections than lower-tier members. 8 canonical power transfer motifs repeat across the network: the walkout-to-lab pattern (OpenAI → Anthropic), the acqui-hire-as-talent-capture pattern (Google → DNNresearch), the reputation-round pattern (SSI: $32B, no product, no revenue). Sam Altman holds the highest betweenness centrality in the dataset (0.145), connecting more disconnected subgraphs than anyone else. One node. 38 direct ties. YC, OpenAI, and Microsoft capital bridged through a single person. The November 2023 board crisis resolved not through formal governance but through social capital: a staff letter, a private conversation, and external leverage. The network held the institution together, not the org chart. The Transformer Eight paper is a single source node that produced a generation of careers and at least 3 frontier labs. One co-authored paper, eight trajectories, and an entire subgraph of the modern AI industry. This is applied network science on one of the most consequential domains of our time. The interactive map lets you explore paths between nodes, filter by community, tie strength, and institution, and trace influence across the full connected graph. By Yumi Kimura map.behaviorgraph.com/?view=… #NetworkScience #GraphAnalytics #GenAI #SocialNetworkAnalysis #AIIndustry -- 🤝 Put your graph tech brand in front of the people who matter Your graph technology deserves to be seen by buyers, analysts, and builders who are actively shaping the space.     The Year of the Graph is the independent hub that this community trusts. Slots for the upcoming Summer 2026 Issue are filling fast. Reach out and book yours now 👇 yearofthegraph.xyz/contact/

Software engineering is evolving - and Knowledge Graphs are the quiet engine behind that change. 💻⛓️ New for #CDL26, we are exploring Knowledge Graphs for Software Engineering. We’re moving past simple code completion and into deep, context-aware AI assistance. We are calling for pioneers to share their work on: Codebase Knowledge Graphs: Using AST-driven, RDF-based, or property graph approaches to map software architecture. AI-Assisted Coding: How graphs provide the context and retrieval grounding that agents need to truly understand a codebase. Graph-Driven Enterprise Architecture: Connecting software engineering to broader EA frameworks via ontology. If you’re building the tools that help AI understand code, we want to hear from you. 📝 Submit your proposal: connected-data.london/2026-c… #CDL26 #ConnectedData #SoftwareEngineering #AIProgramming #GraphAI #DevEx #KnowledgeGraph

Context Graph: How Organizations Use LLMs Cost-Effectively Large organizations want to use large language models (LLMs) to answer questions and generate accurate content, but the models themselves know nothing about the organization. The missing piece is context — the right slice of enterprise knowledge, delivered into the prompt at the right moment, in the fewest tokens. This book is about context graphs: enterprise graph data structures designed to assemble that compact, high-value context. Context graphs are structured, persistent records of product data, customer data, ontologies, and decision traces — capturing not just what happened inside an enterprise, but why it happened, who approved it, and which precedents justified it. The focus throughout the book is token efficiency and quality of content returned from an LLM. Every modeling decision, retrieval pattern, and architectural choice is evaluated against that pair of constraints. This textbook is written for three overlapping groups: Enterprise architects and senior engineers designing AI-powered systems who need a principled approach to organizational memory and context management. AI/ML practitioners and data engineers building LLM-powered applications and struggling with hallucinations, missing context, and poor decision quality in agent workflows. Technical product managers and founders building or evaluating products in the enterprise AI space who want a framework for where context graphs create durable competitive advantage. The book assumes comfort reading technical content and some exposure to software systems. It does not require a deep background in machine learning or graph databases — knowledge graphs, labeled property graphs, and formal ontologies are all introduced from first principles. The most expensive problem in enterprise AI is no longer the model — it is the context the model is given. Large language models can summarize, reason, and draft fluent text, but only when they are handed the right organizational knowledge at the right moment. Today most organizations solve that problem one prompt at a time, stuffing whatever they can fit into a context window and hoping for the best. There is no canonical reference for the discipline that should sit underneath those prompts. This book exists to define that discipline and to give practitioners a working blueprint. The opportunity is genuinely large: McKinsey estimates generative AI could add $2.6 to $4.4 trillion in value annually across industries — but the majority of enterprise deployments remain stuck in pilot stages, blocked by hallucinations, missing context, and brittle integrations. Foundation Capital's analysis of the AI market frames the trillion-dollar enterprise opportunity not as building better foundation models, but as solving the context problem — giving models the right organizational knowledge at the moment of decision. Gartner predicts that over 40% of agentic AI projects will be cancelled by the end of 2027, citing escalating costs, unclear business value, and inadequate risk controls — failure modes that largely trace back to missing or unreliable context. The Stanford AI Index reports that enterprise generative AI adoption has crossed a majority threshold, yet only a small fraction of organizations report consistent, production-grade results — a gap that reflects the missing layer of persistent, structured context. The literature gap is the reason for this book: Context graphs as a discipline sit between three mature fields — knowledge graphs, retrieval-augmented generation (RAG), and process mining — but no widely available textbook treats them as a unified practice. Practitioners building this layer today usually stitch together blog posts, vendor documentation, and trial-and-error.  Context graphs deserve a foundational text, and this book aims to become a pillar reference that future books, courses, and products build on. What makes this book different: Most books on enterprise AI stop at architectural sketches or vendor walkthroughs. This one is built on a validated learning graph of 496 interconnected concepts organized into 12 taxonomy categories, introduced across 22 chapters in strict prerequisite order so the ideas compound rather than collide. It pairs an extensive background on graph fundamentals, semantic layers, metadata standards, and decision traces with precise, queryable graph models — schemas you can implement and query directly, not just diagrams to admire. The entire textbook is open source and free — no paywalls, no access codes, no subscription — because a foundational discipline needs an accessible foundational reference. By Dan McCreary dmccreary.github.io/context-… -- Connected Data London 2026 | 11–12 November | Leonardo Royal Hotel London Tower Bridge 🎤 Share your work with the world's most passionate data community. The Call for Submissions is open. connected-data.london/2026-c… 🎟 Tickets on sale now. Early bird discounts up to 30%. 2026.connected-data.london?u… 📺 Sponsorship opportunities available. Contact info@connected-data.london for details. #KnowledgeGraph #GraphRAG #Ontology #Graph #AI #DataScience #GraphDB #SemTech

Fabric IQ: The shared context layer for AI agents and real-time applications AI agents fail without context-rich data. A June 2026 Forrester report puts it plainly: "No matter how well modeled, data without the context provided by shared semantics and mature ontologies will fall short for agentic AI use cases." Microsoft's answer at Build 2026 is Fabric IQ, now generally available as the shared context layer sitting at the heart of the Microsoft data stack. The architecture is what makes this interesting from a graph and semantics perspective. Fabric IQ is built on three connected layers: Unified data in OneLake (structured, unstructured, event, and graph), business intelligence through semantic models (business meaning defined once, applied everywhere), and operational intelligence through ontologies and real-time signals. Ontologies are doing the heavy lifting here. They model the entities, relationships, rules, and constraints that semantic models were never designed to represent.  According to a Microsoft-controlled A/B study, ontology-grounded agents produced 2.2x more excellent responses, a 4.5x win rate in side-by-side comparisons, and a 30% reduction in tool calls. Without ontology grounding, agents explore the wrong paths. With it, they start with the map. Graph in Fabric is also now generally available, built as a relationship-first data modeling engine natively on OneLake.  It runs on GQL (ISO/IEC 39075), supports billions of relationships with sharded scale-out processing, and includes natural-language-to-GQL for non-technical users.  Paired with Ontology, Graph is the execution layer that lets AI agents reason consistently across connected business domains, from supply chain and fraud to customer intelligence and cybersecurity. The connective tissue extends further: Ontology MCP support lets external agents and tools connect through the Model Context Protocol. Foundry IQ agents can use the ontology as a governed knowledge source. Copilot Studio can call it as a tool.  Define business context once in Fabric IQ, reuse it across the full spectrum of agent experiences. By Yitzhak Kesselman and Chafia Aouissi Fabric IQ: The shared context layer for AI agents and real-time applications community.fabric.microsoft.c… Fabric IQ: The semantic layer powering trusted AI agents at enterprise scale community.fabric.microsoft.c… #FabricIQ #KnowledgeGraph #SemanticLayer #Ontology #GraphData -- Connected Data London 2026 | 11–12 November | Leonardo Royal Hotel London Tower Bridge 🎤 Share your work with the world's most passionate data community. The Call for Submissions is open.  connected-data.london/2026-c… 🎟 Tickets on sale now. Early bird discounts up to 30%. 2026.connected-data.london?u… 📺 Sponsorship opportunities available. Contact info@connected-data.london for details. #KnowledgeGraph #GraphRAG #Ontology #Graph #AI #DataScience #GraphDB #SemTech

Evaluating Knowledge Graph Construction Methods and Graph Neural Networks Knowledge graphs play an increasingly central role in a wide range of artificial intelligence applications. When combined with Graph Neural Networks (GNNs), they have demonstrated strong performance across numerous tasks, including node classification, relation prediction, and knowledge graph completion. Knowledge graphs automatically constructed from text are increasingly used in real-world applications. However, their inherent noise, fragmentation, and semantic inconsistencies significantly affect the performance of GNNs on downstream tasks. Assessing their performance and robustness remains difficult, as it is often unclear whether observed results stem from the learning model or from the quality of the constructed graph itself.  This work introduces a dual-purpose benchmark designed to jointly evaluate (i) the performance of GNNs on noisy, text-derived graphs and (ii) the effectiveness of graph construction methods on a downstream task.  The benchmark is built in the biomedical domain from a single textual corpus and includes two automatically constructed graphs generated using different extraction methods, alongside a high-quality reference graph curated by experts that serves as an upper performance bound.  This design enables controlled comparison of construction methods and systematic evaluation of GNN robustness through semi-supervised node classification.  A standardized, reproducible, and extensible evaluation framework is provided, facilitating the integration of new graph extraction methods and learning models. arxiv.org/abs/2605.05476 #EmergingTech #GraphAI #Research #DataEngineering #GNN #DeepLearning #NeuralNetwork -- 💬 ‘An indispensable summary’ - Mark Underwood, Synchrony.  Join readers from Amazon, Capgemini, Michelin, Neo4j & more Subscribe to the Year of the Graph newsletter for quarterly updates and insights on all things #KnowledgeGraph, #GraphDB, Graph #Analytics / #DataScience / #AI and #SemTech 👇 yearofthegraph.xyz/newslette…

The Ultimate Guide to Semantic Reasoning: How to enrich your data for practical applications including use with RAG & LLMs Semantic reasoning is fast becoming a must-have for anyone running a knowledge graph application as a route to better data, faster queries, and ultimately greater insights. With ever-improving technology, these benefits are no longer for an exclusive few and are instead widely accessible, and yet, many in the industry still lack the knowledge and understanding to fully capture the power of reasoning. Join this Ultimate Guide to Semantic Reasoning to learn the best practices of rule writing and how to use it to supercharge applications. Use the W3C standard, OWL, for ontological reasoning, and the widely used Datalog for more advanced functionality such as aggregation, negation, and filtering to build a working solution live in the workshop. Discover how this enriched data, coupled with the advanced automation feature of incremental reasoning, is enabling real-world applications, including how to enhance RAG and LLM-based solutions. Key Topics Semantic Reasoning & Rules-based AI Knowledge Graphs Querying Graph Databases Writing Rules Writing Ontologies Target Audience Knowledge Engineers Data Scientists Data Engineering Data Analysts Managers of the above Aimed at a spectrum of users, from the non-technical to technically minded but unfamiliar. We cover the basics so that everyone has the tools to follow along with the rest of the tutorial but move quickly onto the more advanced sections. Experts in the field will not find this useful. Goals Get hands-on experience using Semantic Reasoning and Knowledge Graphs in order to understand the extent to which it can be used to empower real-world use cases. Session outline: In this Masterclass, attendees will learn what reasoning is, what it can to do transform data, and crucially, how to do that themselves and how it supports real-world applications. After a brief theoretical introduction to the subject, attendees will be expected to get hands-on with a reasoning engine in order to learn how to setup a reasoning-ready datastore, and how to reason over it using OWL and Datalog rules. No prior experience is required as we will run through the process step-by-step, from start to finish. Over the course of the tutorial attendees will learn: How to write and run a SPARQL query The importance of reasoning and its application How to write a Datalog rule How to apply and verify the rules they write The extended opportunities with reasoning How to create a solution that relies on reasoning How to achieve RAG (Retrieval-Augmented Generation) with enriched data, KGs and LLMs Format This class is very hands-on. Each topic will first be demonstrated to the students so they can copy an ideal example and see the intended results in an informal teacher-student format. Then they will be given the opportunity to apply their new learned skills without immediate direction, writing rules and queries by themselves. If at any point a participant requires some assistance, the lecturers will be on hand to help, whether that requires a minor hint, a refresh of the material, or gentle guidance. Anyone of any skill level should leave this class knowing what reasoning is and how to implement it, so individual support is flexible depending on the needs of the group. Level Beginner - Intermediate Prerequisite Knowledge None youtube.com/watch?v=s7iAsR1Z… -- Peter Crocker. Co-founder, CEO, Oxford Semantic Technologies Peter Crocker is the co-founder and CEO of Oxford Semantic Technologies (OST), developers of the industry-leading knowledge graph and reasoner RDFox. Tom Vout. Knowledge Engineer, Oxford Semantic Technologies Tom help OST transform data into actionable insights using semantic reasoning and knowledge representation technologies -- Welcome to Connected Data London's #ThrowbackThursday Every Thursday at 3pm GMT, we are releasing gems from our vault on #YouTube Tune in and learn from leaders and innovators; subscribe to our channel and watch premieres as they are released!  #knowledgegraph #graphdatabase #graph #AI #datascience #analytics #semtech #ontology

How to Keep Your AI Agent's Knowledge Graph Clean Most tutorials skip the part that actually keeps a graph usable as it grows: separating naming from identity. Paul Iusztin built unified memory layers on top of knowledge graphs and kept running into the same reader question: how do you handle entity resolution and deduplication without corrupting the graph? The answer is a 5-step pipeline: LLM extraction reads the text and emits typed entity/relationship triplets anchored to a POLE O ontology Entity resolution normalizes the name against existing nodes using exact, fuzzy, and semantic matching in a short-circuit chain. No merges yet, just canonical naming Full-context embedding captures the entity's name, type, and attributes for a richer identity signal Deduplication compares that embedding against existing nodes and routes to one of three outcomes: auto-merge (>=0.95), human review (0.85-0.95), or a new node (<0.85) A nightly "dream pass" re-runs deduplication on recently ingested nodes to catch duplicates that were processed in parallel and never compared The key insight: entity resolution and deduplication are two distinct decisions. Resolution asks "what should we call this?" Deduplication asks "is this the same real-world entity?" Conflating them is what silently corrupts graphs. Jensen Huang the NVIDIA CEO and a same-named doctor in Taipei have the same name and the same entity type. Resolution cannot tell them apart. Only full-context deduplication can. False merges are invisible until they are expensive to undo. The pipeline is designed to make irreversible operations earn their way in. By Paul Iusztin decodingai.com/p/keep-knowle… #KnowledgeGraphs #GraphAI #AgentMemory #EntityResolution #AIEngineering -- Connected Data London 2026 | 11–12 November | Leonardo Royal Hotel London Tower Bridge 🎤 Share your work with the world's most passionate data community. The Call for Submissions is open.  connected-data.london/2026-c… 🎟 Tickets on sale now. Early bird discounts up to 30%. 2026.connected-data.london 📺 Sponsorship opportunities available. Contact info@connected-data.london for details. #KnowledgeGraph #GraphRAG #Ontology #Graph #AI #DataScience #GraphDB #SemTech

Knowledge Graph Tools and Platforms If you are looking for knowledge graph tools and platforms you can use, there are some resources to help there. knwler turns documents into structured knowledge, extracting entities, relationships, and topics. Knowledge Graph Toolkit (KGTK) is a Python library for easy manipulation with knowledge graphs. graflo is a framework for transforming tabular and hierarchical data into property graphs and ingesting them into graph databases. The State of the Graph is a comprehensive, up-to-date repository, visualization, and analysis of offerings across the graph technology space. The State of the Graph knowledge graph catalog brings together dedicated platforms, infrastructure providers, and knowledge‑centric search and management tools so you can see who is doing what, where they overlap, and where they differ. TopBraid’s Steve Hedden created Open Knowledge Graphs – a search engine for ontologies, controlled vocabularies, and Semantic Web tools. Ítalo Oliveira created a shortlist of Conceptual Modeling and Linked Data Tools. And Michael Hoogkamer created an interactive taxonomy of semantic modeling concepts. Taxonomies can be considered as stepping stones for ontologies and knowledge graphs. Heather Hedden shared her insights on what taxonomies are not and taxonomy sources, and Kurt Cagle shows how to use public taxonomies. Taxonomists have a role in the new world of Generative AI, and Yumiko Saito reflects on it. Kurt Cagle explores how to make taxonomies (and knowledge graphs in general) more friendly for LLMs. To use LLMs with knowledge graphs, Fanghua (Joshua) Yu proposes Generative Knowledge Modeling (GenKM): a comprehensive methodology introducing a modular four-stage architecture that unifies 40 existing Graph RAG systems under a common formalism, a generative operator algebra, and the GenKG Lifecycle for end-to-end knowledge graph governance. -- 📩 Excerpt from The Year of the Graph Spring 2026 newsletter Read "Beyond Context Graphs: How Ontology, Semantics, and Knowledge Graphs Define Context" with more sections, references and attribution here 👇 yearofthegraph.xyz/newslette… All things #KnowledgeGraph, #GraphDB, Graph #Analytics / #DataScience / #AI and #SemTech.

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