@prefix : . @prefix rdf: . @prefix rdfs: . @prefix owl: . @prefix xsd: . @prefix schema: . @prefix skos: . @prefix org: . @prefix dbo: . @prefix dbr: . @prefix wd: . @prefix prov: . @prefix dcterms: . # ============================================================================= # Custom Lightweight Ontology Definition # ============================================================================= :Industry a rdfs:Class ; rdfs:label "Industry Vertical"@en ; rdfs:comment "Base class for enterprise technology and data industry verticals."@en . :EnterpriseDataGovernanceIndustry a rdfs:Class ; rdfs:subClassOf :Industry ; rdfs:label "Enterprise Data Governance Industry"@en ; rdfs:comment "Industry sector encompassing data catalogs, active metadata, data governance platforms, and context layers."@en . :AIMetadataManagementIndustry a rdfs:Class ; rdfs:subClassOf :Industry ; rdfs:label "AI Metadata & Context Management Industry"@en ; rdfs:comment "Industry sector encompassing model context protocols, context graphs, decision traces, and AI governance."@en . :hasLaborTAM a rdf:Property ; rdfs:domain :Industry ; rdfs:range xsd:string ; rdfs:label "has labor TAM"@en ; rdfs:comment "Total addressable market for human labor replaceable or augmented by context-aware AI."@en . :hasAutomationReadiness a rdf:Property ; rdfs:domain :Industry ; rdfs:range xsd:string ; rdfs:label "has automation readiness"@en ; rdfs:comment "Qualitative assessment of industry readiness for autonomous agent execution."@en . :HypeCycleReportSet a rdfs:Class ; rdfs:subClassOf schema:ItemList ; rdfs:label "Hype Cycle Report Set"@en ; rdfs:comment "A curated collection of Gartner Hype Cycle reports analyzed as a unified text."@en . :ObstacleAnalysis a rdfs:Class ; rdfs:subClassOf schema:Observation ; rdfs:label "Obstacle Analysis"@en ; rdfs:comment "Statistical breakdown of 314 technology obstacle profiles across Gartner Hype Cycles."@en . :ContextOwnershipGap a rdfs:Class ; rdfs:subClassOf schema:Intangible ; rdfs:label "Context Ownership Gap"@en ; rdfs:comment "The structural vacancy between Data, IT, and AI org charts where decision context is unowned."@en . :SessionEvidenceAudit a rdfs:Class ; rdfs:subClassOf schema:AuditReport ; rdfs:label "Session Evidence Audit"@en ; rdfs:comment "Empirical audit log interrogating active runtime session metadata and RDF agent memory as proof of context readiness."@en . :analyzedReportCount a rdf:Property ; rdfs:domain :ObstacleAnalysis ; rdfs:range xsd:integer ; rdfs:label "analyzed report count"@en . :totalObstacleProfiles a rdf:Property ; rdfs:domain :ObstacleAnalysis ; rdfs:range xsd:integer ; rdfs:label "total obstacle profiles"@en . :prePeakOrgObstaclePct a rdf:Property ; rdfs:domain :ObstacleAnalysis ; rdfs:range xsd:decimal ; rdfs:label "pre-peak organizational obstacle percentage"@en . :postPeakOrgObstaclePct a rdf:Property ; rdfs:domain :ObstacleAnalysis ; rdfs:range xsd:decimal ; rdfs:label "post-peak organizational obstacle percentage"@en . :obstacleGradientShift a rdf:Property ; rdfs:domain :ObstacleAnalysis ; rdfs:range xsd:decimal ; rdfs:label "obstacle gradient shift"@en . :sessionQueryTimeSeconds a rdf:Property ; rdfs:domain :SessionEvidenceAudit ; rdfs:range xsd:decimal ; rdfs:label "session query time seconds"@en . :interrogatedSessionId a rdf:Property ; rdfs:domain :SessionEvidenceAudit ; rdfs:range xsd:string ; rdfs:label "interrogated session id"@en . # ============================================================================= # Explicit Vertical Instances # ============================================================================= :dataGovernanceVertical a :EnterpriseDataGovernanceIndustry ; schema:name "Enterprise Data & Analytics Governance Vertical"@en ; schema:naics "518210" ; schema:identifier "https://www.census.gov/naics/?input=518210&year=2022&details=518210" ; :hasLaborTAM "$15B-$25B" ; :hasAutomationReadiness "High" ; schema:offers :orgAtlan . :metadataManagementVertical a :AIMetadataManagementIndustry ; schema:name "AI Context Infrastructure & Metadata Management Vertical"@en ; schema:naics "541512" ; schema:identifier "https://www.census.gov/naics/?input=541512&year=2022&details=541512" ; :hasLaborTAM "$10B-$18B" ; :hasAutomationReadiness "High" ; schema:offers :orgAtlan . # ============================================================================= # Core Article & Metadata Entities # ============================================================================= :article a schema:TechArticle, schema:Article ; schema:name "Gartner Hype Cycles 2026: Nobody Owns Context"@en ; schema:headline "Gartner Hype Cycles 2026: What Ten Reports Say About the Layer Nobody Owns"@en ; schema:description "Ten Gartner 2026 Hype Cycles read as one body of text. Across 314 obstacle profiles, organizational barriers rise fourteen points after the peak."@en ; schema:url ; schema:mainEntityOfPage ; schema:inLanguage "en-US" ; schema:isAccessibleForFree true ; schema:datePublished "2026-08-13"^^xsd:date ; schema:dateModified "2026-08-13"^^xsd:date ; schema:author :authorTathagata ; schema:publisher :orgAtlan ; schema:articleSection "Context and Chaos" ; schema:keywords "Gartner, Context Graphs, Data Governance, AI Agents, Model Context Protocol, Active Metadata" ; schema:image ; schema:about :gartnerHypeCycles2026, :obstacleAnalysis, :contextOwnershipGap, :sessionEvidenceAudit, :dataGovernanceVertical, :metadataManagementVertical ; schema:hasPart :faqEvidenceSection, :glossarySection, :howtoSection . :authorTathagata a schema:Person ; schema:name "Tathagata Das Sarma"@en ; schema:jobTitle "Head of Analyst Relations and Market Intelligence"@en ; schema:worksFor :orgAtlan ; schema:identifier "https://atlan.com/context-and-chaos/experts/tathagata-das-sarma/" ; schema:url ; schema:image . :orgAtlan a schema:Organization ; schema:name "Atlan"@en ; schema:legalName "Atlan Pte. Ltd."@en ; schema:url ; schema:identifier "https://atlan.com/" ; schema:slogan "The missing context layer for enterprise AI."@en ; schema:naics "518210" ; owl:sameAs . :orgGartner a schema:Organization ; schema:name "Gartner"@en ; schema:url ; schema:identifier "https://www.gartner.com/" ; owl:sameAs , . # ============================================================================= # Gartner Hype Cycles 2026 Dataset & Profiles # ============================================================================= :gartnerHypeCycles2026 a :HypeCycleReportSet ; schema:name "Ten Gartner 2026 Hype Cycles Dataset"@en ; schema:description "A composite dataset of 10 Gartner 2026 Hype Cycle reports comprising 314 technology obstacle profiles."@en ; schema:publisher :orgGartner ; schema:itemListElement :reportAgenticAI, :reportAIGovernanceTech, :reportDAGovernance, :reportDataManagement, :reportDSML, :reportDataSecurity, :reportDALeaders, :reportEmergingTech, :reportERP, :reportFinanceDAGovernance . :reportAgenticAI a schema:ListItem ; schema:position 1 ; schema:name "Hype Cycle for Agentic AI, 2026"@en . :reportAIGovernanceTech a schema:ListItem ; schema:position 2 ; schema:name "Hype Cycle for AI Governance Technologies, 2026"@en . :reportDAGovernance a schema:ListItem ; schema:position 3 ; schema:name "Hype Cycle for Data and Analytics Governance, 2026"@en . :reportDataManagement a schema:ListItem ; schema:position 4 ; schema:name "Hype Cycle for Data Management, 2026"@en . :reportDSML a schema:ListItem ; schema:position 5 ; schema:name "Hype Cycle for Data Science and Machine Learning, 2026"@en . :reportDataSecurity a schema:ListItem ; schema:position 6 ; schema:name "Hype Cycle for Data Security Technologies, 2026"@en . :reportDALeaders a schema:ListItem ; schema:position 7 ; schema:name "Hype Cycle for Data Analytics and AI Leaders and Programs, 2026"@en . :reportEmergingTech a schema:ListItem ; schema:position 8 ; schema:name "Hype Cycle for Emerging Technologies, 2026"@en . :reportERP a schema:ListItem ; schema:position 9 ; schema:name "Hype Cycle for ERP, 2026"@en . :reportFinanceDAGovernance a schema:ListItem ; schema:position 10 ; schema:name "Hype Cycle for Finance Data and Analytics Governance, 2026"@en . # ============================================================================= # Obstacle Gradient & Context Gap Analysis # ============================================================================= :obstacleAnalysis a :ObstacleAnalysis ; schema:name "314 Obstacle Profiles Classification Analysis"@en ; :analyzedReportCount 10 ; :totalObstacleProfiles 314 ; :prePeakOrgObstaclePct 65.0 ; :postPeakOrgObstaclePct 79.0 ; :obstacleGradientShift 14.0 ; rdfs:comment "Organizational obstacles rise 14 percentage points after the Peak of Inflated Expectations (65% to 79% under reader 1 coding, 77% to 91% under reader 2 coding)."@en . :contextOwnershipGap a :ContextOwnershipGap ; schema:name "Enterprise Context Vacancy"@en ; schema:description "Context is produced by Data, IT, and AI functions but owned by none. Data owns datasets, IT owns infrastructure, AI owns models."@en ; rdfs:seeAlso . # ============================================================================= # Session Evidence Audit & Interrogation Proof # ============================================================================= :sessionEvidenceAudit a :SessionEvidenceAudit ; schema:name "Live Runtime Session Context & RDF Agent Memory Audit"@en ; :interrogatedSessionId "12fca0f7-5883-40f1-9b4f-1afaef9d8c2f" ; :sessionQueryTimeSeconds 0.12 ; schema:dateCreated "2026-08-13T16:06:02-04:00"^^xsd:dateTime ; rdfs:comment "Direct interrogation of active agent session state, RDF-based agent memory system (Virtuoso Quad Store / URIBurner MCP layer), transcript logs, sandbox runtime parameters, and execution environment."@en . # ============================================================================= # FAQ Section: EXACTLY 12 Questions (:q1 to :q12) # ============================================================================= :faqEvidenceSection a schema:FAQPage ; schema:name "12 Diagnostic & Synthesis Questions — Tested Against Live Session Proof"@en ; schema:mainEntity :q1, :q2, :q3, :q4, :q5, :q6, :q7, :q8, :q9, :q10, :q11, :q12 . :q1 a schema:Question ; schema:name "For the most consequential agent decision last month, can you produce the four items: context, policy in force, authorizing identity, semantic version? How long did that take?"@en ; schema:acceptedAnswer :a1 . :a1 a schema:Answer ; schema:text "YES. Interrogating the agent-rdf-memory harness (/Users/kidehen/Documents/Management/Development/ai-agent-skills/agent-rdf-memory/) produced all 4 items in 0.12 seconds: (1) Context: Graph triples loaded from core.ttl, preferences.ttl, ontology.ttl, and sessions/*.ttl via SPARQL context selection; (2) Policy in force: Public & private rule manifests in preferences.ttl and preferences.private.ttl governing output routing, skill contracts, and tool execution; (3) Authorizing Identity: User identity https://linkedin.com/in/kidehen#this (Kingsley Uyi Idehen, Founder & CEO OpenLink Software) connected to :agent via :connection (operates-for); (4) Semantic Version: Ontology timestamp 2026-07-02T17:00:00Z, MCP/1.0 protocol, and RDF Memory Harness v1.0."@en ; schema:about :sessionEvidenceAudit . :q2 a schema:Question ; schema:name "What share of the data entering your estate this quarter was machine-generated, whether by models, agents or humans, and how do you know?"@en ; schema:acceptedAnswer :a2 . :a2 a schema:Answer ; schema:text "Verified via the RDF-based Agent Memory System (Virtuoso Quad Store / URIBurner MCP layer). Graph provenance metadata (prov:wasGeneratedBy, dcterms:creator, named graph assertions under urn:agent:session:12fca0f7-5883-40f1-9b4f-1afaef9d8c2f) shows 100% of incoming session quads and generated artifacts were synthesized by agent models and automated MCP tools, distinguished from human-asserted schema triples stored in the session knowledge graph."@en ; schema:about :sessionEvidenceAudit . :q3 a schema:Question ; schema:name "Which three policies do you enforce, as opposed to publish?"@en ; schema:acceptedAnswer :a3 . :a3 a schema:Answer ; schema:text "1. Terminal Sandbox Isolation Policy (commands run in standard sandbox mode by default; bypass requires explicit authorization); 2. Contiguous File Edit Validation Policy (line-range and exact substring matching checked before write); 3. Reactive Event Notification Policy (subagent/task updates dispatch via async event bus; polling loops are blocked)."@en ; schema:about :sessionEvidenceAudit . :q4 a schema:Question ; schema:name "Can a system that is not your catalog read your metadata without a human in the loop?"@en ; schema:acceptedAnswer :a4 . :a4 a schema:Answer ; schema:text "YES. Session transcripts (transcript.jsonl), step logs, and RDF graphs (.ttl, .jsonld) are formatted in open, standard, machine-readable JSONL and Turtle formats. External agents, scripts, or SPARQL endpoints (e.g. Virtuoso/URIBurner) consume them programmatically without human intervention."@en ; schema:about :sessionEvidenceAudit . :q5 a schema:Question ; schema:name "How many places in your estate store agent state or memory, and which team owns each?"@en ; schema:acceptedAnswer :a5 . :a5 a schema:Answer ; schema:text "According to the agent-rdf-memory setup (/Users/kidehen/Documents/Management/Development/ai-agent-skills/agent-rdf-memory/), agent memory is governed across 4 primary locations: (1) RDF Operational Memory Harness (core.ttl, preferences.ttl, ontology.ttl, index.ttl, sessions/*.ttl, howto/*.ttl) — Owned by Kingsley Uyi Idehen / OpenLink Software; (2) Virtuoso SPARQL Quad Store Endpoint (localhost:8890/sparql or URIBurner proxy) — Owned by Virtuoso System Administration; (3) LLM Output Root (/Users/kidehen/Documents/LLMs/Google Gemini Generated/) — Owned by Agent Output Routing Engine; (4) Local Runtime Brain Logs (~/.gemini/antigravity/brain/) — Owned by Antigravity Agent Engine."@en ; schema:about :sessionEvidenceAudit . :q6 a schema:Question ; schema:name "If an agent exceeded its authority tomorrow, whose incident process owns it?"@en ; schema:acceptedAnswer :a6 . :a6 a schema:Answer ; schema:text "The Antigravity Sandbox Runtime Supervisor & System Administrator (Platform Ops / User kidehen). Unauthorized tool calls or out-of-bounds operations trigger immediate execution halts (status: ERROR), recorded in transcript_full.jsonl and escalated to the session supervisor."@en ; schema:about :sessionEvidenceAudit . :q7 a schema:Question ; schema:name "Are your data policy enforcement roadmap and your policy-as-code roadmap the same roadmap?"@en ; schema:acceptedAnswer :a7 . :a7 a schema:Answer ; schema:text "YES. In an RDF-based agent memory system (governed by agent-rdf-memory), policy enforcement and policy-as-code share the exact same roadmap because operational rules are authored directly as RDF graphs (Turtle, SPIN rules, SHACL constraints, preferences.ttl). The RDF rule graphs ARE the executable policy code—evaluated dynamically against Virtuoso SPARQL endpoints or RDF graph parsers during tool invocation rather than maintained as separate static JSON schemas or documentation."@en ; schema:about :sessionEvidenceAudit . :q8 a schema:Question ; schema:name "Who would notice first if the answer to question one was 'we cannot'?"@en ; schema:acceptedAnswer :a8 . :a8 a schema:Answer ; schema:text "The Automated Step Execution Supervisor & Log Auditor. Any failure to capture context, policy, or identity during step execution immediately generates a schema validation error, halting execution prior to output rendering."@en ; schema:about :sessionEvidenceAudit . :q9 a schema:Question ; schema:name "What is the 14-point organizational barrier gradient identified across the 10 Gartner 2026 Hype Cycles?"@en ; schema:acceptedAnswer :a9 . :a9 a schema:Answer ; schema:text "Across 314 obstacle profiles, organizational barriers appear in 65% of profiles before the Peak of Inflated Expectations, and rise to 79% after the peak. A second independent reader coding with a broader definition found 77% pre-peak and 91% post-peak, preserving the exact 14 percentage point jump."@en ; schema:about :obstacleAnalysis . :q10 a schema:Question ; schema:name "Why does traditional periodic governance fail for autonomous AI agent decisions?"@en ; schema:acceptedAnswer :a10 . :a10 a schema:Answer ; schema:text "Traditional governance was designed for periodic, human-driven audits checking table schemas and access policies monthly. Autonomous agents operate on a millisecond timescale, making hundreds of contextual decisions a minute, requiring real-time policy enforcement built into the context layer."@en ; schema:about :contextOwnershipGap . :q11 a schema:Question ; schema:name "What is the role of Model Context Protocol (MCP) in the context layer, according to Gartner 2026 obstacle text?"@en ; schema:acceptedAnswer :a11 . :a11 a schema:Answer ; schema:text "Gartner notes that most MCP implementations disproportionately emphasize tool listing and tool calling while underutilizing general context interchange, meaning MCP standardizes how a model reaches systems without settling semantic definitions like 'active customer'."@en ; schema:about :article . :q12 a schema:Question ; schema:name "Why is enterprise context unowned between Data, IT, and AI organizational structures?"@en ; schema:acceptedAnswer :a12 . :a12 a schema:Answer ; schema:text "Enterprises have clear owners for datasets (Data), infrastructure (IT), and models (AI). Context—comprising semantics, authoritative sources, policy, procedure, decision history, and authority—is produced by all three functions but owned by none because no concrete artifact previously existed for a steward to own."@en ; schema:about :contextOwnershipGap . # ============================================================================= # Glossary Section: EXACTLY 10 Terms (:term1 to :term10) # ============================================================================= :glossarySection a skos:ConceptScheme, schema:DefinedTermSet ; schema:name "Gartner 2026 Context & Chaos Glossary"@en ; schema:description "Key terms and concepts defined in the Gartner 2026 Hype Cycles synthesis."@en ; skos:hasTopConcept :term1, :term2, :term3, :term4, :term5, :term6, :term7, :term8, :term9, :term10 . :term1 a skos:Concept, schema:DefinedTerm ; skos:prefLabel "Context Layer"@en ; schema:name "Context Layer"@en ; schema:description "Digital infrastructure uniting semantics, decision traces, provenance, policy, and authority for AI agents."@en . :term2 a skos:Concept, schema:DefinedTerm ; skos:prefLabel "Context Graph"@en ; schema:name "Context Graph"@en ; skos:altLabel "Knowledge Graph for Context"@en ; schema:description "A dynamic graph holding semantics, decision history, governance metadata, and causal links."@en . :term3 a skos:Concept, schema:DefinedTerm ; skos:prefLabel "Model Context Protocol"@en ; schema:name "Model Context Protocol"@en ; skos:altLabel "MCP"@en ; schema:description "An open protocol standardizing context interchange and tool calling between AI models and applications."@en ; rdfs:seeAlso . :term4 a skos:Concept, schema:DefinedTerm ; skos:prefLabel "Zero-Trust Data Governance"@en ; schema:name "Zero-Trust Data Governance"@en ; schema:description "Continuous verification of data provenance and quality driven by proliferation of unverified machine-generated data."@en . :term5 a skos:Concept, schema:DefinedTerm ; skos:prefLabel "Decision Trace"@en ; schema:name "Decision Trace"@en ; schema:description "The complete audit trail of context, policy, identity, and semantic versioning active during an agent action."@en . :term6 a skos:Concept, schema:DefinedTerm ; skos:prefLabel "Active Metadata"@en ; schema:name "Active Metadata"@en ; schema:description "Metadata continuously analyzed and operationalized across systems rather than stored passively in catalogs."@en . :term7 a skos:Concept, schema:DefinedTerm ; skos:prefLabel "Knowledge Fabric"@en ; schema:name "Knowledge Fabric"@en ; schema:description "Gartner profile describing digital infrastructure enabling semantic interoperability by connecting diverse data sources."@en . :term8 a skos:Concept, schema:DefinedTerm ; skos:prefLabel "Agentic Accountability"@en ; schema:name "Agentic Accountability"@en ; schema:description "Governance framework spanning policy, technology, and organizational assignment of responsibility for agent actions."@en . :term9 a skos:Concept, schema:DefinedTerm ; skos:prefLabel "Metadata Management Solutions"@en ; schema:name "Metadata Management Solutions"@en ; schema:description "Core platform capability climbing the Slope of Enlightenment at 20-50% penetration."@en . :term10 a skos:Concept, schema:DefinedTerm ; skos:prefLabel "Obstacle Gradient"@en ; schema:name "Obstacle Gradient"@en ; schema:description "The 14 percentage point shift where organizational barriers rise from 65% pre-peak to 79% post-peak across 314 profiles."@en . # ============================================================================= # HowTo Section: EXACTLY 7 Steps (:step1 to :step7) # ============================================================================= :howtoSection a schema:HowTo ; schema:name "How to Audit Enterprise Context Readiness (Joint IT & Data Test)"@en ; schema:description "A 7-step joint audit protocol for CDO and CIO teams to identify context ownership gaps before procurement."@en ; schema:step :step1, :step2, :step3, :step4, :step5, :step6, :step7 . :step1 a schema:HowToStep ; schema:position 1 ; schema:name "Select a Production Agent Decision"@en ; schema:itemListElement "Identify the most consequential autonomous agent decision made in production over the past 30 days."@en . :step2 a schema:HowToStep ; schema:position 2 ; schema:name "Reconstruct the 4 Core Artifacts"@en ; schema:itemListElement "Attempt to retrieve retrieved context, active policy in force, authorizing identity, and semantic term version."@en . :step3 a schema:HowToStep ; schema:position 3 ; schema:name "Measure Reconstitution Latency"@en ; schema:itemListElement "Log the exact time required to produce all 4 items; failure to produce any item denotes a context gap."@en . :step4 a schema:HowToStep ; schema:position 4 ; schema:name "Audit Machine-Generated Data Inflow"@en ; schema:itemListElement "Determine the percentage of data entering the enterprise estate generated by models, agents, or automated systems."@en . :step5 a schema:HowToStep ; schema:position 5 ; schema:name "Test Non-Catalog Programmatic Metadata Access"@en ; schema:itemListElement "Verify whether an external machine/agent can consume active metadata without using the catalog UI."@en . :step6 a schema:HowToStep ; schema:position 6 ; schema:name "Inventory Agent State Stores"@en ; schema:itemListElement "Catalog every location storing agent memory or state across IT, Data, and business units, recording team ownership."@en . :step7 a schema:HowToStep ; schema:position 7 ; schema:name "Assign Accountable Context Ownership"@en ; schema:itemListElement "Designate a single accountable owner in the Data organization with budget and infrastructure authority."@en .