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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.

POLLO提示词本体OWL2SKOS语义网LLM提示工程知识表示
Published 2026-04-09 03:37Recent activity 2026-04-09 03:51Estimated read 5 min
POLLO: Elevating LLM Prompts to Portable Knowledge Ontologies
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Section 01

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.

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Section 02

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.

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Section 03

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.

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Section 04

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.
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Section 05

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.

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Section 06

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.