Enterprise Knowledge Graph System

System Architecture

A comprehensive knowledge graph system that combines entity extraction, relationship mining, and semantic reasoning capabilities.

Core Components

1. Entity Extraction Pipeline

class EntityExtractor:
    def __init__(self, config: Dict[str, Any]):
        self.ner_model = TransformerNER(config['ner_config'])
        self.entity_linker = EntityLinker(config['linking_config'])
        self.type_classifier = EntityTypeClassifier(
            config['type_config']
        )
        self.disambiguator = EntityDisambiguator(
            config['disambiguation_config']
        )
        
    async def extract_entities(
        self,
        text: str
    ) -> List[Entity]:
        # Extract entity mentions
        mentions = await self.ner_model.extract_mentions(text)
        
        # Link to knowledge base
        linked_entities = await self.entity_linker.link_entities(
            mentions
        )
        
        # Classify entity types
        typed_entities = await self.type_classifier.classify(
            linked_entities
        )
        
        # Disambiguate entities
        disambiguated = await self.disambiguator.disambiguate(
            typed_entities,
            context=text
        )
        
        return disambiguated

class EntityLinker:
    def __init__(self, config: Dict[str, Any]):
        self.kb_index = ElasticsearchIndex(config['kb_config'])
        self.candidate_generator = CandidateGenerator(
            config['candidate_config']
        )
        self.ranker = CandidateRanker(config['ranking_config'])

2. Relationship Mining

class RelationshipMiner:
    def __init__(self, config: Dict[str, Any]):
        self.relation_extractor = RelationExtractor(
            config['extraction_config']
        )
        self.pattern_miner = PatternMiner(config['pattern_config'])
        self.schema_aligner = SchemaAligner(
            config['schema_config']
        )
        
    async def mine_relationships(
        self,
        entities: List[Entity],
        context: str
    ) -> List[Relationship]:
        # Extract relationships from text
        extracted_relations = await self.relation_extractor.extract(
            entities,
            context
        )
        
        # Mine patterns
        patterns = await self.pattern_miner.mine_patterns(
            extracted_relations
        )
        
        # Align with schema
        aligned_relations = await self.schema_aligner.align(
            extracted_relations,
            patterns
        )
        
        return aligned_relations

class RelationExtractor:
    def __init__(self, config: Dict[str, Any]):
        self.model = TransformerRelationExtractor(
            config['model_name']
        )
        self.rules = RelationRules(config['rules_config'])
        self.validator = RelationValidator(
            config['validation_config']
        )

3. Graph Construction and Management

class GraphManager:
    def __init__(self, config: Dict[str, Any]):
        self.graph_store = Neo4jStore(config['store_config'])
        self.schema_manager = SchemaManager(
            config['schema_config']
        )
        self.index_manager = GraphIndexManager(
            config['index_config']
        )
        
    async def update_graph(
        self,
        entities: List[Entity],
        relationships: List[Relationship]
    ) -> UpdateResult:
        # Validate against schema
        await self.schema_manager.validate(entities, relationships)
        
        # Create transaction
        async with self.graph_store.transaction() as tx:
            # Update entities
            entity_ops = await self._update_entities(
                tx,
                entities
            )
            
            # Update relationships
            relation_ops = await self._update_relationships(
                tx,
                relationships
            )
            
            # Update indexes
            await self.index_manager.update_indexes(
                tx,
                entity_ops,
                relation_ops
            )
            
        return UpdateResult(
            entity_ops=entity_ops,
            relation_ops=relation_ops
        )

4. Semantic Reasoning Engine

class ReasoningEngine:
    def __init__(self, config: Dict[str, Any]):
        self.rule_engine = RuleEngine(config['rules_config'])
        self.path_finder = SemanticPathFinder(
            config['path_config']
        )
        self.inference_engine = InferenceEngine(
            config['inference_config']
        )
        
    async def reason(
        self,
        query: Query,
        context: Optional[Dict[str, Any]] = None
    ) -> ReasoningResult:
        # Apply reasoning rules
        expanded_query = await self.rule_engine.apply_rules(
            query,
            context
        )
        
        # Find semantic paths
        paths = await self.path_finder.find_paths(
            expanded_query
        )
        
        # Perform inference
        inferences = await self.inference_engine.infer(
            paths,
            context
        )
        
        return ReasoningResult(
            paths=paths,
            inferences=inferences,
            confidence_scores=self._compute_confidence(inferences)
        )

class InferenceEngine:
    def __init__(self, config: Dict[str, Any]):
        self.reasoner = LogicReasoner(config['logic_config'])
        self.embedder = GraphEmbedder(config['embedding_config'])
        self.scorer = ConfidenceScorer(config['scoring_config'])

Usage Example

# Initialize knowledge graph system
config = {
    'extraction_config': {
        'ner_model': 'bert-large-ner',
        'linking_threshold': 0.85,
        'max_entities': 100
    },
    'graph_config': {
        'store_url': 'neo4j://localhost:7687',
        'schema_file': 'schema.owl',
        'index_config': {
            'entity_indexes': ['name', 'type'],
            'relation_indexes': ['type', 'timestamp']
        }
    }
}

kg_system = KnowledgeGraphSystem(config)

# Process document
text = """
SpaceX, founded by Elon Musk in 2002, successfully launched the Falcon 9 
rocket from Kennedy Space Center. The mission delivered 60 Starlink 
satellites to low Earth orbit.
"""

# Extract knowledge
entities = await kg_system.extract_entities(text)
relationships = await kg_system.mine_relationships(entities, text)

# Update knowledge graph
result = await kg_system.update_graph(entities, relationships)

# Perform reasoning
query = Query(
    start="SpaceX",
    relation="launches",
    end="?satellite",
    constraints={"time": "2024"}
)

reasoning_result = await kg_system.reason(query)

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