oxirs_core/ai/

relation_extraction.rs

//! Relation Extraction from Text using NLP
//!
//! This module provides automated relation extraction capabilities to build
//! knowledge graphs from unstructured text data.

use crate::ai::AiConfig;
use crate::model::{Literal, NamedNode, Triple};
use anyhow::Result;
use serde::{Deserialize, Serialize};
use std::collections::HashMap;

/// Relation extraction module
pub struct RelationExtractor {
    /// Configuration
    config: ExtractionConfig,

    /// Named Entity Recognition model
    ner_model: Box<dyn NamedEntityRecognizer>,

    /// Relation classification model
    relation_model: Box<dyn RelationClassifier>,

    /// Entity linking module
    entity_linker: Box<dyn EntityLinker>,

    /// Confidence threshold
    confidence_threshold: f32,
}

/// Relation extraction configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ExtractionConfig {
    /// Enable named entity recognition
    pub enable_ner: bool,

    /// Enable relation classification
    pub enable_relation_classification: bool,

    /// Enable entity linking
    pub enable_entity_linking: bool,

    /// Confidence threshold for extractions
    pub confidence_threshold: f32,

    /// Maximum sentence length
    pub max_sentence_length: usize,

    /// Language model to use
    pub language_model: String,

    /// Enable coreference resolution
    pub enable_coreference: bool,

    /// Supported languages
    pub supported_languages: Vec<String>,
}

impl Default for ExtractionConfig {
    fn default() -> Self {
        Self {
            enable_ner: true,
            enable_relation_classification: true,
            enable_entity_linking: true,
            confidence_threshold: 0.7,
            max_sentence_length: 512,
            language_model: "bert-base-uncased".to_string(),
            enable_coreference: true,
            supported_languages: vec!["en".to_string()],
        }
    }
}

/// Extracted relation from text
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ExtractedRelation {
    /// Subject entity
    pub subject: ExtractedEntity,

    /// Predicate/relation type
    pub predicate: String,

    /// Object entity
    pub object: ExtractedEntity,

    /// Confidence score
    pub confidence: f32,

    /// Source text span
    pub source_span: TextSpan,

    /// Context sentence
    pub context: String,

    /// Additional metadata
    pub metadata: HashMap<String, String>,
}

/// Extracted entity
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ExtractedEntity {
    /// Entity text
    pub text: String,

    /// Entity type
    pub entity_type: EntityType,

    /// Linked knowledge base ID (if available)
    pub kb_id: Option<String>,

    /// Confidence score
    pub confidence: f32,

    /// Text span in original document
    pub span: TextSpan,
}

/// Entity types
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum EntityType {
    Person,
    Organization,
    Location,
    Date,
    Time,
    Money,
    Percent,
    Product,
    Event,
    Concept,
    Other(String),
}

/// Text span
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TextSpan {
    /// Start position
    pub start: usize,

    /// End position
    pub end: usize,

    /// Text content
    pub text: String,
}

/// Named Entity Recognition trait
pub trait NamedEntityRecognizer: Send + Sync {
    /// Extract named entities from text
    fn extract_entities(&self, text: &str) -> Result<Vec<ExtractedEntity>>;

    /// Get supported entity types
    fn supported_types(&self) -> Vec<EntityType>;
}

/// Relation classification trait
pub trait RelationClassifier: Send + Sync {
    /// Classify relation between two entities
    fn classify_relation(
        &self,
        text: &str,
        subject: &ExtractedEntity,
        object: &ExtractedEntity,
    ) -> Result<Option<(String, f32)>>;

    /// Get supported relation types
    fn supported_relations(&self) -> Vec<String>;
}

/// Entity linking trait
pub trait EntityLinker: Send + Sync {
    /// Link entity to knowledge base
    fn link_entity(&self, entity: &ExtractedEntity, context: &str) -> Result<Option<String>>;

    /// Get knowledge base info
    fn kb_info(&self) -> KnowledgeBaseInfo;
}

/// Knowledge base information
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct KnowledgeBaseInfo {
    /// Knowledge base name
    pub name: String,

    /// Base URI
    pub base_uri: String,

    /// Version
    pub version: String,

    /// Entity count
    pub entity_count: usize,
}

impl RelationExtractor {
    /// Create new relation extractor
    pub fn new(_config: &AiConfig) -> Result<Self> {
        let extraction_config = ExtractionConfig::default();

        // Create NER model
        let ner_model = Box::new(DummyNER::new());

        // Create relation classifier
        let relation_model = Box::new(DummyRelationClassifier::new());

        // Create entity linker
        let entity_linker = Box::new(DummyEntityLinker::new());

        Ok(Self {
            config: extraction_config,
            ner_model,
            relation_model,
            entity_linker,
            confidence_threshold: 0.7,
        })
    }

    /// Extract relations from text
    pub async fn extract_relations(&self, text: &str) -> Result<Vec<ExtractedRelation>> {
        // Step 1: Sentence segmentation
        let sentences = self.segment_sentences(text);

        let mut all_relations = Vec::new();

        for sentence in sentences {
            // Step 2: Named Entity Recognition
            let entities = if self.config.enable_ner {
                self.ner_model.extract_entities(&sentence)?
            } else {
                Vec::new()
            };

            // Step 3: Entity Linking
            let linked_entities = if self.config.enable_entity_linking {
                self.link_entities(&entities, &sentence).await?
            } else {
                entities
            };

            // Step 4: Relation Classification
            if self.config.enable_relation_classification {
                let relations =
                    self.extract_relations_from_entities(&sentence, &linked_entities)?;
                all_relations.extend(relations);
            }
        }

        // Step 5: Filter by confidence
        let filtered_relations = all_relations
            .into_iter()
            .filter(|r| r.confidence >= self.confidence_threshold)
            .collect();

        Ok(filtered_relations)
    }

    /// Convert extracted relations to RDF triples
    pub fn to_triples(&self, relations: &[ExtractedRelation]) -> Result<Vec<Triple>> {
        let mut triples = Vec::new();

        for relation in relations {
            // Create subject
            let subject = if let Some(kb_id) = &relation.subject.kb_id {
                NamedNode::new(kb_id)?
            } else {
                // Use text as identifier (simplified)
                NamedNode::new(format!(
                    "http://example.org/entity/{}",
                    relation.subject.text.replace(' ', "_")
                ))?
            };

            // Create predicate
            let predicate = NamedNode::new(format!(
                "http://example.org/relation/{}",
                relation.predicate.replace(' ', "_")
            ))?;

            // Create object
            let object = if let Some(kb_id) = &relation.object.kb_id {
                crate::model::Object::NamedNode(NamedNode::new(kb_id)?)
            } else {
                // Determine if it's a literal or named node
                match relation.object.entity_type {
                    EntityType::Date
                    | EntityType::Time
                    | EntityType::Money
                    | EntityType::Percent => {
                        crate::model::Object::Literal(Literal::new(&relation.object.text))
                    }
                    _ => crate::model::Object::NamedNode(NamedNode::new(format!(
                        "http://example.org/entity/{}",
                        relation.object.text.replace(' ', "_")
                    ))?),
                }
            };

            let triple = Triple::new(subject, predicate, object);
            triples.push(triple);
        }

        Ok(triples)
    }

    /// Segment text into sentences
    fn segment_sentences(&self, text: &str) -> Vec<String> {
        // Simplified sentence segmentation
        text.split(". ")
            .map(|s| s.trim().to_string())
            .filter(|s| !s.is_empty())
            .collect()
    }

    /// Link entities to knowledge base
    async fn link_entities(
        &self,
        entities: &[ExtractedEntity],
        context: &str,
    ) -> Result<Vec<ExtractedEntity>> {
        let mut linked_entities = Vec::new();

        for entity in entities {
            let mut linked_entity = entity.clone();

            if let Ok(Some(kb_id)) = self.entity_linker.link_entity(entity, context) {
                linked_entity.kb_id = Some(kb_id);
            }

            linked_entities.push(linked_entity);
        }

        Ok(linked_entities)
    }

    /// Extract relations from entities in a sentence
    fn extract_relations_from_entities(
        &self,
        sentence: &str,
        entities: &[ExtractedEntity],
    ) -> Result<Vec<ExtractedRelation>> {
        let mut relations = Vec::new();

        // Try all pairs of entities
        for (i, subject) in entities.iter().enumerate() {
            for (j, object) in entities.iter().enumerate() {
                if i != j {
                    if let Ok(Some((relation_type, confidence))) = self
                        .relation_model
                        .classify_relation(sentence, subject, object)
                    {
                        let relation = ExtractedRelation {
                            subject: subject.clone(),
                            predicate: relation_type,
                            object: object.clone(),
                            confidence,
                            source_span: TextSpan {
                                start: 0,
                                end: sentence.len(),
                                text: sentence.to_string(),
                            },
                            context: sentence.to_string(),
                            metadata: HashMap::new(),
                        };

                        relations.push(relation);
                    }
                }
            }
        }

        Ok(relations)
    }
}

/// Dummy NER implementation (placeholder)
struct DummyNER;

impl DummyNER {
    fn new() -> Self {
        Self
    }
}

impl NamedEntityRecognizer for DummyNER {
    fn extract_entities(&self, text: &str) -> Result<Vec<ExtractedEntity>> {
        // Placeholder implementation
        // In real implementation, would use NLP models like spaCy, BERT-NER, etc.

        let words: Vec<&str> = text.split_whitespace().collect();
        let mut entities = Vec::new();

        for (i, word) in words.iter().enumerate() {
            // Simple heuristics (placeholder)
            if word.chars().next().unwrap_or(' ').is_uppercase() {
                let entity = ExtractedEntity {
                    text: word.to_string(),
                    entity_type: EntityType::Person, // Simplified
                    kb_id: None,
                    confidence: 0.8,
                    span: TextSpan {
                        start: i * 5, // Simplified
                        end: (i + 1) * 5,
                        text: word.to_string(),
                    },
                };
                entities.push(entity);
            }
        }

        Ok(entities)
    }

    fn supported_types(&self) -> Vec<EntityType> {
        vec![
            EntityType::Person,
            EntityType::Organization,
            EntityType::Location,
        ]
    }
}

/// Dummy relation classifier (placeholder)
struct DummyRelationClassifier;

impl DummyRelationClassifier {
    fn new() -> Self {
        Self
    }
}

impl RelationClassifier for DummyRelationClassifier {
    fn classify_relation(
        &self,
        text: &str,
        _subject: &ExtractedEntity,
        _object: &ExtractedEntity,
    ) -> Result<Option<(String, f32)>> {
        // Placeholder implementation
        // In real implementation, would use relation classification models

        if text.contains("work") || text.contains("employ") {
            Ok(Some(("worksFor".to_string(), 0.85)))
        } else if text.contains("live") || text.contains("reside") {
            Ok(Some(("livesIn".to_string(), 0.80)))
        } else if text.contains("born") || text.contains("birth") {
            Ok(Some(("bornIn".to_string(), 0.90)))
        } else {
            Ok(None)
        }
    }

    fn supported_relations(&self) -> Vec<String> {
        vec![
            "worksFor".to_string(),
            "livesIn".to_string(),
            "bornIn".to_string(),
            "marriedTo".to_string(),
            "locatedIn".to_string(),
        ]
    }
}

/// Dummy entity linker (placeholder)
struct DummyEntityLinker;

impl DummyEntityLinker {
    fn new() -> Self {
        Self
    }
}

impl EntityLinker for DummyEntityLinker {
    fn link_entity(&self, entity: &ExtractedEntity, _context: &str) -> Result<Option<String>> {
        // Placeholder implementation
        // In real implementation, would use entity linking systems like DBpedia Spotlight

        match entity.entity_type {
            EntityType::Person => Ok(Some(format!(
                "http://dbpedia.org/resource/{}",
                entity.text.replace(' ', "_")
            ))),
            EntityType::Location => Ok(Some(format!(
                "http://dbpedia.org/resource/{}",
                entity.text.replace(' ', "_")
            ))),
            _ => Ok(None),
        }
    }

    fn kb_info(&self) -> KnowledgeBaseInfo {
        KnowledgeBaseInfo {
            name: "DBpedia".to_string(),
            base_uri: "http://dbpedia.org/resource/".to_string(),
            version: "2023-09".to_string(),
            entity_count: 6_000_000,
        }
    }
}

#[cfg(test)]
mod tests {
    use super::*;
    use crate::ai::AiConfig;

    #[tokio::test]
    async fn test_relation_extractor_creation() {
        let config = AiConfig::default();
        let extractor = RelationExtractor::new(&config);
        assert!(extractor.is_ok());
    }

    #[tokio::test]
    async fn test_relation_extraction() {
        let config = AiConfig::default();
        let extractor = RelationExtractor::new(&config).expect("construction should succeed");

        let text = "John works for Microsoft. He lives in Seattle.";
        let relations = extractor
            .extract_relations(text)
            .await
            .expect("async operation should succeed");

        // Should extract some relations (depends on dummy implementation)
        assert!(!relations.is_empty());
    }

    #[test]
    fn test_sentence_segmentation() {
        let config = AiConfig::default();
        let extractor = RelationExtractor::new(&config).expect("construction should succeed");

        let text = "First sentence. Second sentence. Third sentence.";
        let sentences = extractor.segment_sentences(text);

        assert_eq!(sentences.len(), 3);
        assert_eq!(sentences[0], "First sentence");
    }

    #[test]
    fn test_to_triples() {
        let config = AiConfig::default();
        let extractor = RelationExtractor::new(&config).expect("construction should succeed");

        let relation = ExtractedRelation {
            subject: ExtractedEntity {
                text: "John".to_string(),
                entity_type: EntityType::Person,
                kb_id: None,
                confidence: 0.9,
                span: TextSpan {
                    start: 0,
                    end: 4,
                    text: "John".to_string(),
                },
            },
            predicate: "worksFor".to_string(),
            object: ExtractedEntity {
                text: "Microsoft".to_string(),
                entity_type: EntityType::Organization,
                kb_id: None,
                confidence: 0.85,
                span: TextSpan {
                    start: 15,
                    end: 24,
                    text: "Microsoft".to_string(),
                },
            },
            confidence: 0.8,
            source_span: TextSpan {
                start: 0,
                end: 25,
                text: "John works for Microsoft.".to_string(),
            },
            context: "John works for Microsoft.".to_string(),
            metadata: HashMap::new(),
        };

        let triples = extractor
            .to_triples(&[relation])
            .expect("operation should succeed");
        assert_eq!(triples.len(), 1);
    }
}