ai-symbolic-neuro

What problem does it solve?

Symbolic reasoning and neural models operate in different representations; this skill provides a coherent framework to combine formal knowledge representation with learned inference for end-to-end reasoning over complex domains.

Core Features & Use Cases

• Hybrid reasoning pipelines that fuse ontologies, description logics, and graph embeddings for coherent inference.
• Workflow guidance for building knowledge graphs, performing symbolic and neural reasoning, and integrating Lean encodings when needed.
• Use Case: combine an OWL ontology with a neural relation extractor to infer new facts in a biomedical knowledge graph.

Quick Start

Generate a starter plan for a symbolic-neural reasoning workflow using a knowledge-graph approach.