Knowledge graphs organize information as interconnected entities and relationships, enabling AI systems to understand context and answer complex queries. This technology powers semantic search, recommendation engines, and intelligent assistants.

Core Components

• Entities: People, places, organizations, concepts represented as nodes in the graph.

• Relationships: Connections between entities defining how they relate to each other.

• Attributes: Properties and metadata describing entities in detail.

• Ontologies: Formal definitions of entity types and relationship semantics.

• Inference Rules: Logic for deriving new knowledge from existing relationships.

Applications

Search engines provide contextual results understanding query intent, enterprise knowledge management connects organizational information, drug discovery identifies potential therapeutic relationships, fraud detection reveals suspicious connection patterns, and conversational AI delivers contextual responses based on knowledge.

Building Knowledge Graphs

Extract entities from unstructured text using NLP, identify relationships through pattern recognition, resolve entities to canonical representations, validate accuracy through expert review, and continuously update with new information.

Benefits

Enhanced semantic search understands meaning not just keywords, better data integration connects disparate information sources, improved AI reasoning enables complex question answering, and knowledge discovery reveals hidden insights through graph analysis.