Struct AiEngine 

pub struct AiEngine { /* private fields */ }

AI-powered RDF processing engine

Implementations

impl AiEngine

pub fn new(config: AiConfig) -> Result<Self>

Create a new AI engine

pub async fn initialize_gnn(&mut self, gnn_config: GnnConfig) -> Result<()>

Initialize Graph Neural Network

pub async fn add_embedding_model( &mut self, name: String, model: Arc<dyn KnowledgeGraphEmbedding>, ) -> Result<()>

Add knowledge graph embedding model

pub async fn generate_embeddings( &self, model_name: &str, triples: &[Triple], ) -> Result<Vec<Vec<f32>>>

Generate embeddings for RDF graph

pub async fn find_similar_entities( &self, entity_vector: &[f32], top_k: usize, ) -> Result<Vec<(String, f32)>>

Find similar entities using vector similarity

pub async fn predict_links( &self, model_name: &str, entities: &[String], relations: &[String], ) -> Result<Vec<(String, String, String, f32)>>

Predict missing links in knowledge graph

pub async fn resolve_entities( &self, entities: &[Triple], ) -> Result<Vec<EntityCluster>>

Resolve entity identity across different sources

pub async fn extract_relations_from_text( &self, text: &str, ) -> Result<Vec<ExtractedRelation>>

Extract relations from text using NLP

pub async fn temporal_reasoning( &self, query: &TemporalQuery, ) -> Result<TemporalResult>

Perform temporal reasoning on knowledge graph

pub async fn train_embedding_model( &self, model_name: &str, training_data: &[Triple], validation_data: &[Triple], ) -> Result<TrainingMetrics>

Train embedding model on knowledge graph

pub async fn evaluate_model( &self, model_name: &str, test_data: &[Triple], ) -> Result<EvaluationMetrics>

Evaluate model performance

pub async fn get_statistics(&self) -> Result<AiStatistics>

Get AI engine statistics