NeoOLAF：融合符号语义与大语言模型的下一代本体学习框架

NeoOLAF is an innovative open-source ontology learning framework that combines symbolic semantics, large language models (LLM), and agent reasoning workflows. Its core goal is to automate the construction of application-oriented ontology knowledge bases from unstructured text, addressing traditional ontology building's inefficiency and LLM's hallucination and lack of interpretability issues. Hosted on GitHub and implemented in Python, it features modularity and scalability.

Ontology is a core foundation for intelligent systems, but traditional manual construction is time-consuming and hard to scale. While LLMs have enabled automated ontology learning exploration, they suffer from hallucination, poor interpretability, and lack of symbolic reasoning. NeoOLAF emerged to fuse symbolic semantics and neural network advantages, solving these pain points.

NeoOLAF's architecture consists of three key components: 1) Symbolic Semantic Layer (provides formal knowledge representation via OWL for logical consistency), 2) LLM Layer (handles natural language understanding tasks like entity extraction and context comprehension), and 3) Agent Reasoning Workflow (coordinates symbolic and neural reasoning dynamically).

The Symbolic Semantic Layer uses OWL to define concept hierarchies, constraints, and rules, ensuring ontology consistency and interpretability. The LLM Layer leverages strong semantic understanding to process complex language from unstructured text. The Agent Reasoning Workflow uses multi-agent collaboration to choose strategies: symbolic reasoning for precise logic, LLM for semantic generalization.

NeoOLAF applies to multiple scenarios:

• Enterprise Knowledge Management: Build enterprise knowledge graphs from internal docs/reports.
• Academic Research: Help researchers organize literature concepts and research脉络.
• Medical Info Extraction: Construct medical ontologies from clinical records/literature.
• Legal Text Analysis: Extract legal concepts/relations from laws and cases.

NeoOLAF's hybrid reasoning architecture offers four main advantages:

1. Interpretability: Symbolic layer ensures traceable and verifiable reasoning.
2. Robustness: LLM handles noise and incomplete data.
3. Flexibility: Agent architecture customizes reasoning strategies for tasks.
4. Scalability: Modular design allows integrating new algorithms/models.

NeoOLAF represents a key direction in ontology learning—neuro-symbolic fusion. It provides an extensible platform for developers to explore new algorithms and lowers the threshold for building domain knowledge graphs. Future prospects include leveraging advancing LLM and agent technologies to expand its role in knowledge engineering and NLP.