Neuro-symbolic knowledge graphs are hybrid models that combine neural representations with symbolic knowledge structures.
Neural networks generate dense vector representations of entities and relations, while symbolic components use explicit logical rules and ontologies.
This enables better reasoning and improved generalization, as symbolic rules support learning and neural models handle uncertainties and noise in the data.
Typical applications include question-answering systems, knowledge completion, and semantic search.
The integration is often done through embedding techniques that map symbolic relations into continuous space, combined with logic-based constraints.