Context Is King: Knowledge Graph Identifiers and Ontology as the Shared Layer Beneath Every Agent Ask three agents for the on-time delivery rate and get three answers. One reads a BI model, one its own prompt, one a vendor application. Each definition is reasonable alone. Together they form a hydra: fix one inconsistency and two more grow back in the next agent. The terms are often used interchangeably. They are different things, and confusing them leads teams to build the wrong one. * Context engineering is a chef choosing ingredients for one dish. Scope: a single agent and its prompt * Context layer is the pantry, labelled and stocked. Scope: the shared definitions of meaning and identity Five shared elements, with the ontology as the spine. Every other component either references it or is typed by it * Context fabric is the labelling standard and inventory system. Scope: any chef in any kitchen finds the same ingredient by the same name Federated ownership of context, with centralised meaning and access Not a product. Fix the shared contracts and the tools underneath can change without breaking agents * Where knowledge graphs come in: The ontology defines what “delivery risk” or “strategic supplier” means, once Every entity gets one enterprise IRI. Source-system IDs from the ERP, the procurement system and contract PDFs map to it through a key crosswalk That is linked data principles applied directly: globally unique identifiers, shared by every source, that embed no vendor or system The shared IRI, not a shared store, joins live status, lakehouse history and a contract clause into one answer When a source system is replaced, only the mappings are re-pointed. Agents, prompts and dashboards stay as they are The same logic reaches the edges: NER label sets derived from the ontology, so entities found in documents link to the same IRIs as the data Vendor agents kept inside the fabric: A2A for actions, MCP for shared meaning Corrections routed to the owner of the context item, so every agent using it improves Past a point, more context lowers answer quality. The aim is the right context, described by metadata on what each item means and how fresh it is. One body, many owners. Part 1 of 3. By Suman Banerjee medium.com/data-science-coll… #Ontology #LinkedData #ContextEngineering #ContextFabric #AgenticAI -- 📩 The Year of the Graph Autumn 2026 newsletter issue is out! Meaning, Traversal, Reasoning, Action: The Graph Stack for Agentic AI. 👇 yearofthegraph.xyz/newslette… All things #KnowledgeGraph, #GraphDB, Graph #Analytics / #DataScience / #AI and #SemTech. Subscribe and follow to be in the know. Reach out if you’d like to be featured

Teaching agents to walk the graph: Wikidata Search Traces 🕸️ The semantic web was the original web of agents. Agentic LLMs have barely touched it. A new paper from PleIAs, in collaboration with AI-BRIDGES and Wikimedia Deutschland, tests a convergence between two kinds of depth: graph traversal in a knowledge graph, and recursion in Recursive Language Models (RLMs). In an RLM, the model works in a persistent Python environment, keeps intermediate results in state, and delegates selected evidence to sub-calls. What they built: A method for growing multi-hop questions over a frozen Wikidata snapshot (about 48M entities, 262M facts). Named entities are replaced with nested conditions, and each expansion is certified so the target stays unique and every condition is necessary 10,235 solving traces over single-entity and multi-hop questions An open RLM harness with 13 graph functions What the first evaluation shows, on 100 questions: The harness improved both models tested, compared with direct tool calling over the same functions gpt-6-luna went from 49 to 61 correct answers, doubling multi-hop accuracy (9 to 19 out of 50) Qwen3.8-27B, open-weight and served on a single H100, went from 60 to 74, the top score of the evaluation The gain comes from carrying the search further along the chain of conditions: models reach the target entity more often The traces are meant for training. The next step is fine-tuning a smaller model, around 12B parameters, on them. The authors see wider potential here: recursively exploring graphs and texts may be a skill in itself, one that can be learned with far fewer parameters. Is that a path to a cognitive core for search? Beyond Wikidata, they plan to study RLMs as a general approach to semantic data search across sources and ontologies. By Mohamed Chenene, Carlos Rosas-Hinostroza, Anastasia Stasenko, Shani Evenstein Sigalov and Pierre-Carl Langlais arxiv.org/abs/2610.06650 #Wikidata #RecursiveLanguageModels #AIAgents #OpenSourceAI #SemanticWeb -- 📩 The Year of the Graph Autumn 2026 newsletter issue is out! Meaning, Traversal, Reasoning, Action: The Graph Stack for Agentic AI. 👇 yearofthegraph.xyz/newslette… All things #KnowledgeGraph, #GraphDB, Graph #Analytics / #DataScience / #AI and #SemTech. Subscribe and follow to be in the know. Reach out if you’d like to be featured

How Enterprise Information Architecture unlocks reliable Graph AI Data silos, compliance risks, and AI hallucinations costing enterprises millions annually are not inevitable. You can fix your architecture without replacing existing systems. An Enterprise Information Architecture anchored by a semantic knowledge layer connects disparate databases into an interconnected, machine-actionable fabric—powering trustworthy Graph AI and natural language querying. Rather than complex “rip-and-replace” projects, it unlocks siloed data into AI-ready enterprise knowledge with strict governance built in. Discover how semantic knowledge graphs bridge enterprise data and trustworthy AI in metaphacts’ free white paper. #InformationArchitecture #AI #EnterpriseKnowledge #KnowledgeGraph #TrustworthyAI Get the whitepaper 👇 go.linkeddataorchestration.c… By Metaphacts - helping bring Year of the Graph to life