From Data‑Driven Insights to a Decision Center: Ontological Engineering with PolarDB‑PG

Ontology in Enterprise AI

Ontology abstracts the real world into objects , relationships , and actions , forming a knowledge‑graph‑plus‑business‑logic engine that makes data both understandable and actionable for AI agents.

Illustrative Example

An airline analyst traditionally extracts maintenance logs, weather data, and boarding statistics from separate systems and manually correlates them to diagnose flight delays. In an Ontology system the entities flight, airplane, weather, and ground staff are pre‑defined and linked; a single natural‑language query returns a visual causal chain and can trigger an action such as dispatchBackupAircraft().

Challenges for Enterprise LLM Agents

Semantic ambiguity : General LLMs lack deterministic enterprise‑level semantic understanding, e.g., the term “customer” may refer to different entities in CRM, ERP, and finance systems.

Logical hallucination : LLMs can generate plausible‑sounding answers that violate strict business rules, causing cascading errors in multi‑step tasks and breaching compliance or safety requirements.

PolarDB‑PG Intelligent Ontology Engine

PolarDB‑PG embeds a lightweight Ontology platform with three layers:

Semantic layer : defines business nouns—objects, attributes, relationships—to unify data semantics across systems.

Data‑flow layer : defines business verbs—operations, actions, processes—covering data‑sync pipelines and function calls.

Intelligent‑decision layer : binds rules, permissions, agents, and models to enable reasoning and automated decisions.

Core Elements

Objects : business entities become rich objects rather than raw rows. Example: the table pg_stat is represented as a DatabaseInstance object with CPU count, team ownership, status, and alert history.

Links : explicit relationships form a knowledge graph, e.g., Database → Deploys_To → CloudRegion and Service → Depends_On → Database, enabling multi‑hop reasoning.

Actions : predefined executable operations such as RollbackDeployment(), each with input parameters, preconditions, and effects, exposed as standard API calls for agents.

From RAG to OAG (Ontology‑Augmented Generation)

Compared with traditional Retrieval‑Augmented Generation (RAG), OAG provides:

Retrieval content : structured entities and relationship network instead of scattered text fragments.

Context quality : precise, complete, traceable context versus noisy, weakly related text.

Reasoning ability : topology‑driven multi‑hop reasoning instead of text‑based guessing.

Explainability : high explainability versus low explainability in RAG.

Lightweight Platform Architecture

The platform runs directly on PolarDB‑PG, avoiding separate graph databases such as Neo4j or JanusGraph. PolarDB‑PG’s relational and multimodal storage, together with the built‑in graph engine Polar_AGE and vector search extension PGVector, provides mixed‑load support and lets enterprises reuse existing stacks, reducing deployment and operational costs.

Automatic Modeling

The system introspects existing PostgreSQL schemas, uses LLMs to infer initial Object , Link , and Action definitions, auto‑generates Chinese descriptions, recommends related actions, and flags sensitive fields. Data synchronization combines batch writes with streaming; a two‑phase sync (nodes first, then relationships) guarantees graph reference integrity even for tables with tens of millions of rows.

Fine‑Grained Permission Governance (ACR)

ACR offers object/attribute‑level isolation, a role hierarchy (admin, dev, viewer), and SQL‑level permission injection that prunes invisible nodes during graph traversal, balancing security and performance.

Action Framework & Human‑Machine Collaboration

High‑risk actions enter a pending state for manual approval; after execution, predefined rules automatically update object states, forming a closed loop. Built‑in webhooks integrate with CI/CD, ticketing, and IM systems, delivering full audit trails for compliance.

Graph + Vector Fusion Retrieval & Reasoning

Using Polar_AGE, the platform supports multi‑hop traversal and path finding without extra graph databases. Permission‑aware traversal (ACR‑aware) prunes unauthorized nodes on the fly. Embedding vectors attached to object attributes enable semantic similarity search, merging graph and vector retrieval for richer reasoning.

Skill: Bridging Ontology and Agents

Skills manage the full lifecycle—creation, editing, deletion, querying, classification. Pre‑built skill packs can be imported with one click. Once objects, links, and actions are defined, they are automatically transformed into callable Agent Skills, eliminating manual coding. A declarative skill file tells an Agent how to invoke Ontology APIs (query, traverse, path, action); the Agent then performs evidence‑driven exploratory reasoning without additional code. The same skill framework applies to diagnostics, sales analysis, IT asset management, and other domains by switching datasets.

Real‑World Case: Supply‑Chain Analysis & Decision

Query: “VIP customer CUST001 in East China adds 300 units of FG_A100, needs delivery within 14 days—earliest possible date?” Traditional manual calculation requires three people for a full day. Using the Ontology graph with QoderWork + Skill, the complete reasoning finishes in minutes, returning data summaries, constraints, bottleneck analysis, candidate and recommended solutions with rationales. A human selects a plan and triggers a predefined Action to close the decision loop.

Other Application Scenarios

Autonomous‑driving long‑tail scenario mining: linking sensor, road, and behavior data to discover rare corner cases.

High‑end manufacturing intelligent O&M: correlating equipment, process, and quality data to enable root‑cause tracing and predictive maintenance.

Precise marketing & experience: building a full customer view to detect churn risk or hidden demand, driving personalized strategies.

Conclusion

PolarDB‑PG’s Ontology platform advances three core ideas: (1) replace scattered text with a structured entity network (OAG) for precise agent context; (2) substitute rigid workflows with flexible semantic boundaries, allowing LLMs autonomous yet constrained decision‑making; (3) deliver a low‑cost, lightweight architecture built on existing PolarDB‑PG stacks, providing a “Palantir Lite” capability for enterprises.