# POLLO: Elevating LLM Prompts to Portable Knowledge Ontologies

> POLLO is a semantic framework based on OWL2 DL and SKOS that elevates prompts from plain text to structured knowledge ontologies, enabling cross-model portability and intelligent composition.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-04-08T19:37:09.000Z
- 最近活动: 2026-04-08T19:51:35.761Z
- 热度: 141.8
- 关键词: POLLO, 提示词本体, OWL2, SKOS, 语义网, LLM, 提示工程, 知识表示
- 页面链接: https://www.zingnex.cn/en/forum/thread/pollo-llm
- Canonical: https://www.zingnex.cn/forum/thread/pollo-llm
- Markdown 来源: floors_fallback

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## POLLO: Elevating LLM Prompts to Portable Knowledge Ontologies (Introduction)

POLLO is a semantic framework based on OWL2 DL and SKOS, designed to address the current challenges in prompt engineering—opacity, non-composability, and non-portability. By elevating prompts to structured knowledge ontologies, it enables cross-model portability and intelligent composition, pushing prompt engineering from a craft to an engineering discipline.

## Challenges in Prompt Engineering (Background)

In current large language model applications, prompts are essentially opaque, non-composable, and non-portable textual artifacts: changing models requires rewriting prompts, combining prompts leads to unpredictable results, and version management remains at the text level without semantic understanding. The root cause is that prompts are treated as final deliverables rather than derivable, transformable intermediate representations.

## Core Methods and Architecture of POLLO (Methodology)

### Core Philosophy
POLLO proposes the 'Prompt as Knowledge Ontology' paradigm, treating prompt text as 'bytecode' derived from semantic intent (which serves as the 'source code'). By switching serializers, optimized text for different models can be generated.
### Ten Core Concepts
A semantic system is built around ten top-level concepts including PromptIntent (semantic core), PromptingContext (physical container), and CommunicativeAct (communicative behavior).
### Three-Layer Architecture
1. Shared Vocabulary Layer: Uses SKOS to establish domain concepts and their relationships, mapping to mature ontologies;
2. Formal Ontology Layer: Uses OWL2 DL to axiomatize classes and properties;
3. Named Extension Layer: Provides domain extensions and RDF instance files.
### Design Philosophy
OWL2 DL is chosen to ensure decidability; SKOS is prioritized over OWL for building shared vocabulary; explicit separation between physical (PromptingContext) and semantic (PromptIntent) layers.

## Case Study: Morgana as a Real-World Application of POLLO (Evidence)

Morgana is a multi-agent dialogue framework based on .NET 10 and Akka.NET, serving as a reference use case for POLLO:
- Guard Actor uses AtomicIntent + Evaluative + DirectStrategy + structured JSON output;
- Classifier Actor uses AtomicIntent + Interrogative + Classification + strict output mode;
- Domain Agent uses PipelineIntent + ReActStrategy + ChainOfThoughtStrategy + tool slots;
- Hierarchical personas are implemented via AgentProfile levels, distinguishing between IsolatedAgentContext and SharedAgentContext.
This case proves that POLLO can model complex multi-agent collaboration scenarios.

## Significance of POLLO's Paradigm Shift (Conclusion)

POLLO represents a paradigm shift in large language model application development: from text engineering to knowledge engineering. By treating prompts as composable, portable, and inferable knowledge ontologies, it is expected to establish cross-model interoperability and push prompt engineering from a craft to an engineering discipline.

## Current Status and Contribution Directions of POLLO (Recommendations)

### Current Status
POLLO is in the draft phase; the SKOS vocabulary and OWL2 DL core are stable and available for experimental feedback.
### Future Plans
Domain extensions and serializer components will be launched soon.
### Contribution Directions
Contributions are welcome: new domain extensions, case studies, model profiles, concept proposals, etc.