Organising the World's Information
Core Hierarchy Backbone (Structural Taxonomy)
The backbone of the system is a five-level hierarchical “address” structure:
Industry → Country → Company → Department → Job Role.
is a highly pragmatic and scalable way to organize the world’s information for AI applications, especially when the goal is operational alignment, decision support, and personalization.
Here’s how each layer can be defined and leveraged:
1. Industry
Definition: Macro-level categorization of economic sectors (e.g., Healthcare, Finance, Retail, Manufacturing).
Why it matters for AI:
Enables vertical-specific AI models and agents.
Determines regulatory frameworks and data sensitivity.
Provides domain vocabulary and problem types.
AI leverage:
Pre-trained domain-specific LLMs (e.g., legal, medical).
Benchmarking and industry-specific analytics.
Tailored recommendation and automation systems.
2. Country
Definition: Geographic and jurisdictional layer that defines legal, cultural, and infrastructural context.
Why it matters for AI:
Laws, regulations, and compliance requirements vary (e.g., GDPR vs. CCPA).
Language, cultural preferences, and infrastructure differ.
Taxonomies, educational standards, and procurement systems vary by region.
AI leverage:
Localization of AI models and interfaces.
Compliance-driven data management and AI governance.
Regional strategy and market adaptation.
3. Company
Definition: Specific organizational entity with unique systems, processes, tools, and goals.
Why it matters for AI:
AI use cases are influenced by company size, maturity, and digital transformation level.
Internal knowledge bases, workflows, and data infrastructure are company-specific.
AI integration varies depending on tech stack and culture.
AI leverage:
Custom agents aligned with enterprise processes.
Enterprise knowledge graph construction.
Internal workflow automation and decision support.
4. Department
Definition: Functional unit within the company (e.g., Sales, Finance, HR, Legal, R&D).
Why it matters for AI:
Each department has specific goals, data types, and key performance indicators (KPIs).
Role of AI varies — e.g., forecasting in Finance vs. candidate screening in HR.
AI leverage:
Department-specific AI copilots.
Process mining and optimization per function.
Tailored dashboards and reporting.
5. Job Role
Definition: Individual responsibilities and tasks assigned to a person or role (e.g., Data Scientist, CFO, Procurement Officer).
Why it matters for AI:
Defines the human-machine interaction level.
Clarifies the scope of augmentation or automation.
Allows for personalized tools, dashboards, and workflows.
AI leverage:
Role-specific AI assistants and copilots.
Skill-based recommendations and upskilling.
Automation of routine tasks based on RACI matrices.
Mapping Ontologies into Each Hierarchy Layer
At each level of the hierarchy we integrate domain ontologies or controlled vocabularies that encode the relevant concepts and rules:
Industry (Domain Ontologies): We attach a formal ontology for each industry. For example, in Healthcare we might use SNOMED CT, a comprehensive clinical terminology, to tag medical concepts (diagnoses, procedures, anatomy). In Finance, we could apply the Financial Industry Business Ontology (FIBO) to standardize financial instruments, entities and transactions. These ontologies provide a shared vocabulary and structure so that all entities (e.g. diseases, financial products) are represented uniformly across the organization.
Country (Regulatory/Standards Ontologies): Each country (or region) contributes its own set of rules or schemas. For example, EU data handled under Country = Germany would inherit GDPR constraints (data protection, consent rules), while Country = USA in healthcare might enforce HIPAA privacy regulations. In addition, we use country-level standards (e.g. ISO 3166 country codes) to tag data by geography. Thus the Country layer encodes location-specific policies and standards that govern the data.
Company (Enterprise Ontologies): Within each company we incorporate its internal knowledge models. Large enterprises often maintain internal taxonomies or knowledge graphs (e.g. product catalogs, organizational charts, business glossaries). We align these with standard ontologies where possible. For instance, Siemens Healthineers might have an ontology for medical devices and services; we embed those concepts at the Company node. These corporate ontologies capture proprietary entity definitions and processes, linking the universal industry and country context to company-specific knowledge.
Department (Domain-Specific Ontologies): Each department (function or discipline) adds finer-grained ontologies. For instance, a Radiology department could use RadLex (a radiology lexicon) to tag imaging findings and procedures, and HL7/DICOM standards for medical imaging data. A Pharmaceutical R&D department might use ontologies like ChEBI or MESH for chemical/drug entities. These specialized vocabularies ensure that departmental documents and data are categorized by expert terminology.
Job Role (Occupational Ontologies): Finally, at the job level we associate roles with standardized occupational and competency schemas. For example, ISCO-08 (International Standard Classification of Occupations) provides a 4‑level hierarchy of job groupsilostat.ilo.org. Many roles are also described in systems like ESCO (EU Skills/Occupations) or O*NET, which link job titles to required skills and qualifications. We tag each role node with these standard codes (e.g. “Radiologist – ISCO code 2211”) and embed related skill/competency ontologies. This allows the system to reason about roles (and people in those roles) in a way that is consistent across companies and countries.
Each hierarchy layer thus brings its own ontology or standards into the model. This multilevel ontological mapping (industry-domain ontologies at the top, regulatory rules next, corporate schemas further down, and skill/job taxonomies at the bottom) ensures that data is semantically organized at all scopes. It also enables cross-walking between levels: for instance, linking a Radiologist (job) to Medical Devices (company products) to Healthcare (industry) to Germany (country regulation) in a unified schema.
Cross-Layer Embedding of Metadata
Beyond the structured taxonomy, each node is richly annotated with multidimensional metadata. Key additional dimensions include:
Temporal/Spatial Metadata: Every node (or piece of content) carries timestamps and location tags. For example, a dataset entry might include the date it was created, updated, or when a clinical event occurred, as well as coordinates or regional tags for geographical context. This “operational metadata” (who/when/where data is produced or changed) helps monitor data lineage and flow. Spatial context (e.g. country/city) can also be embedded to correlate activities with regions.
Purpose-Driven Goals (Business Context): We attach business and mission context to nodes. For instance, an Industry or Department node might include the organization’s strategic objectives or regulatory compliance goals (e.g. “Improve patient outcomes,” “Achieve ISO9001 quality”). Business metadata such as definitions and rules for data usage is captured here. This connects each unit of data to organizational purpose.
Personas and Role Embeddings: Information on relevant user personas is linked at appropriate levels. For example, Radiologist might be associated with a “Radiologist persona” profile (background, needs, preferences). Similarly, Departments can tag content by stakeholder groups (e.g. “patients,” “clinicians,” “data scientists”). Capturing user roles in the graph enables personalization. In practice, enterprise knowledge catalogs often model user personas so that different stakeholders can query the data relevant to their role.
Multimodal Links: Each node can link to related data across modalities: documents, images, audio clips, sensor data, code, etc. For example, a Department node could link to all relevant procedure manuals (PDFs), training videos, and recorded meetings. These multimodal references are simply edges in the graph, integrating disparate information types.
Value Scores and Metrics: Nodes carry quantitative metadata like performance metrics or “value” scores. Examples include customer satisfaction ratings for a Company, quality scores for a Department, or skill proficiency levels for a Job Role. These metrics (which might come from surveys, KPIs, or ML models) help rank or prioritize content.
Feedback Loops: User interactions feed back into the graph. For instance, if an AI assistant answers a question about a Company, the user’s feedback (thumbs up/down, corrections) is recorded and attached to that Company’s node. This “feedback metadata” can weight the reliability or relevance of nodes over time.
Sentiment and Polarity: We annotate nodes (especially those representing documents or communications) with sentiment analysis scores or polarity. For example, forum posts or support tickets linked to a Department might carry a “sentiment” tag (positive, negative, neutral). Emotional indexing (e.g. tagging content with detected emotions) can also be applied to customer feedback or social media content associated with Company nodes.
Systemic Crosslinks: Finally, the architecture explicitly allows linking nodes across branches of the hierarchy. For example, an “AI Ethics” Department node in one company might be linked to similar nodes in other companies, or to an “Industry Regulation” node. These cross-domain edges enable reasoning across industries and regions, not just within one path of the taxonomy.
By embedding these dimensions at each node, the graph becomes a multilayered knowledge map. It is no longer just a tree of categories, but a richly annotated network where each Industry/Country/Company/etc. carries contextual clues (time, people, sentiment, purpose) that AI agents or queries can exploit. For instance, knowing when a policy changed (temporal tag) or who (persona) raised an issue can drastically improve search relevance and decision-making.
Conceptual Data Model Architecture
The overall data model is a nested graph reflecting the Industry→Country→…→Job hierarchy, with metadata sub-nodes and cross-links attached at each level. Conceptually:
Industry Node (e.g. Healthcare): has child edges to Country nodes (e.g. Germany, USA) and is tagged with domain ontology references (SNOMED, FIBO), industry KPIs (market size, standards), and links to related industries.
Country Node (e.g. Germany): under Healthcare has edges to specific Companies in that industry (e.g. Siemens Healthineers). It carries metadata like GDPR compliance rules, country statistics (demographics, market regs), and language/cultural attributes.
Company Node (Siemens Healthineers): under Germany contains edges to its Departments. The company node includes corporate taxonomy (product lines, certifications), org chart info, and business metrics (revenue, market share). It links to peer companies and parent industry.
Department Node (Radiology): child of Siemens, with edges to Job Role nodes. It is annotated with specialized ontologies (RadLex terms, imaging equipment standards), SOP documents, team performance metrics, and location within hospitals.
Job Role Node (Radiologist): under Radiology. It carries skill/competency tags (linked to ISCO-08 or ESCO), training requirements, typical workflows, and persona attributes (e.g. seniority). It may link to actual employee profiles holding that role.
Within this nested schema, we embed additional metadata nodes. For example, each node can have child sub-nodes or attributes for ontology tags (pointers to concepts in SNOMED/ISO/ESCO), time events (founding date, last audited), sentiment scores, user-persona vectors, etc. (See table below.) The structure is thus a tree of categories with property graphs at each node capturing the extra dimensions.
LevelExample EntityOntology/StandardsMetadata/AnnotationsIndustryHealthcareSNOMED CT; FIBO; HL7Global health stats, trend events, industry KPIsCountryGermany (EU)GDPR rules; ISO 3166 country codeNational regs, population/language dataCompanySiemens HealthineersInternal product ontology; W3C schema.orgOrg chart, product lines, financial KPIs, data use policiesDepartmentRadiologyRadLex; DICOM; ISO 9001Equipment inventory, throughput metrics, quality scoresJob RoleRadiologistISCO-08/ESCO (occupations); skill taxonomyCertifications, persona profile, performance ratings
Table: Example nested hierarchy with typical ontology tags and metadata at each level.
Each cell in the above hierarchy is a graph node connected by “child” edges (Industry→Country→…). In addition, cross-links connect similar nodes (e.g. linking all “Radiology” departments across companies). Ontology tags (SNOMED code for “fracture”, ISO code for “DE”) are stored as node properties or edges. Personas, time-events, and sentiment might be modeled as separate metadata sub-nodes or as annotated graph edges. This conceptual schema ensures that every piece of data (no matter how granular) can be referenced back to its place in the global structure, while carrying rich context.
Practical Application Examples
This AI-ready architecture unlocks numerous enterprise use-cases:
Semantic Enterprise Search: By organizing content under a unified taxonomy and ontologies, search can move beyond simple keyword matching to meaning-based retrieval. The system understands that a query for “lung scan Siemens” should surface content from the Siemens Healthineers Radiology department (knowing “lung” relates to healthcare and radiology). In practice, companies build semantic search over knowledge graphs to exploit such relationships. For example, Neo4j notes that semantic search systems use knowledge graphs to understand entity relationships and context, yielding more relevant results. In our model, a user could search by industry, location, or role (“Find all regulatory updates for Healthcare in Germany”) and the graph precisely filters the right data.
Intelligent Agent Deployment: Virtual assistants (chatbots or voice agents) leverage the graph for context. An AI agent can navigate the hierarchy (knowing what “country” an employee is in, what department, etc.) to give tailored answers. For instance, a service desk bot can use the company’s internal graph to guide a Radiologist in Germany through a German-language workflow, automatically incorporating GDPR rules. As one industry blog observes, embedding enterprise knowledge in a graph makes AI agents “informed insiders” that “plan and take action specific to your organizational goals” rather than generic helpers. In short, agents connected to this unified data layer deliver more accurate, context-aware support.
Governance & Compliance Audits: Since each node carries regulatory and provenance metadata, auditing becomes far simpler. For example, a compliance engine can traverse all nodes where Country=Germany and Industry=Healthcare to check if GDPR and HIPAA tags are applied to personal data. Similarly, recording ownership and change history on nodes provides a traceable audit trail. Enterprise knowledge graphs are often used for exactly this purpose: aggregating metadata and usage info to support regulatory reporting. In our architecture, any query about “Who accessed patient data on DATE X in Company Y?” can be answered by following the graph’s edges (Country→Company→data) and inspecting the attached metadata.
Upskilling & Hiring Matching: With roles and skills ontologized, the system can match people to jobs or training. For example, an HR application can compare a candidate’s skill profile (from ESCO/ISCO tags on their resume) against the competency requirements embedded in the relevant Job-Role node. As the ESCO framework describes, each occupation has a defined set of typical skills and competences, allowing systems to compute the best fit between a jobseeker and a role. Similarly, the organization can identify internal skill gaps: “We need X-certified radiologists in Germany,” the graph shows which current employees meet the criteria. The graph’s persona embeddings also allow personalized learning recommendations for career development.
Predictive Strategy Engines: The rich, connected data enables advanced analytics and forecasting. For instance, by modeling the supply chain (linking Industry→Company→products→suppliers), an AI model can predict disruptions (e.g. a shortage of a component) and suggest alternatives. Research shows that incorporating supply-chain data into knowledge graphs lets AI “produce more accurate and insightful responses” about disruptions or optimization strategies. Similarly, in HR (a “people” graph), connected data on employees’ skills and projects can be used to predict attrition risks, future performance or training needs. Indeed, knowledge-graph‑driven people analytics can “identify high-potential individuals [and] predict future performance” by linking roles, skills and outcomes. In strategy planning, executives can query this graph-powered BI layer for forecasts (“What will be our skill shortage in 2 years?”) and the system can reason across domains (finance, people, operations) to inform decision-making.
Figure: Sample data applications enabled by the unified AI-ready architecture – e.g. semantic search, NLP analysis, conversational assistants, and predictive analytics. The integrated taxonomy and ontologies allow AI/ML tools to traverse this knowledge layer rather than isolated silos.
Summary of Benefits
This unified architecture yields multiple advantages:
Intuitive Navigation: The strict hierarchy provides a natural “find path” for humans and machines. Users can drill down from Industry→Country→Company and so on to locate data. The use of standard codes (ISO countries, NAICS/SIC industries) makes the structure predictable and interoperable. Navigation follows the organization chart, so domain experts immediately find the data in their domain, reducing confusion and search time.
Integrated Ontologies: By embedding industry-specific and regulatory ontologies at each level, the architecture ensures semantic consistency. For example, all healthcare concepts are referenced to SNOMED terms, all financial terms to FIBO, etc. This eliminates synonyms and ambiguities across data sources. In practice, knowledge graphs encode domain knowledge so that disparate data (documents, databases, messages) can be meaningfully connected.
Metadata-Rich Context: Each node’s annotations (time, location, version, usage, etc.) add layers of context that traditional databases lack. A metadata graph “organizes, connects, and contextualizes” data assets across the enterprise. This richness enables richer analytics – e.g. trending “when did quality metrics change” or “which departments are most interrelated.” In short, it transforms raw records into self-describing knowledge.
Personalization & Role-awareness: With persona profiles and role-based tags, the architecture can deliver personalized outputs. Different user personas (e.g. engineer vs. manager) implicitly query different parts of the graph. Enterprise case studies show that capturing business context and user perspective in the metadata graph helps people search and query data aligned with their needs. In practical terms, a sales rep will see sales content first; a technician will see maintenance info – all pulled correctly by following the persona and role metadata.
Cross-Domain Reasoning: Because all data is connected in one graph, AI can infer across silos. For example, linking the Finance and HR branches of the graph could reveal how R&D budget trends correlate with engineering talent gaps. Knowledge graphs excel at finding non-obvious relationships. The unified view (“single pane of glass” across systems) means queries can join across Industry/Country/Company boundaries. As noted in industry analysis, knowledge graphs allow a consolidated view of enterprise data from multiple sources, enabling holistic insights (e.g. evaluating loan risk by combining customer data from banks and third-party providers).
Overall, this architecture makes enterprise data AI-ready: it is highly structured yet semantically rich, enabling faster discovery, better governance, and powerful AI/ML over the complete knowledge space. By combining an intuitive navigation taxonomy with deep ontology integration and abundant metadata, organizations gain explainable, personalized AI applications and cross-domain analytic capabilities without manually stitching together disparate systems.
Sources: Authoritative industry and standards literature as cited above (SNOMED CT en.wikipedia.org, GICS en.wikipedia.org, ISO/SIC codes salesforce.com, RadLex rsna.org, ISCO/ESCOilostat.ilo.org esco.ec.europa.eu, etc.) and best-practice knowledge-graph/metadata references enterprise-knowledge.com stardog.com neo4j.com medium.com.