Knowledge Graph
A knowledge graph is a structured representation of information that organizes data as entities, their attributes, and the relationships between them, enabling machines to understand and reason about connected information.
Understanding Knowledge Graph
Knowledge graphs transform isolated pieces of information into a connected network. Instead of storing data in separate tables or documents, a knowledge graph represents facts as triples: subject, predicate, object. For example, 'Alice manages Project X,' 'Project X has deadline March 15,' and 'Alice emailed Bob about Project X.' These triples form a web of interconnected facts that AI systems can traverse to answer complex questions, discover hidden patterns, and understand context. Google, Amazon, and LinkedIn all use knowledge graphs to power their services.
How GAIA Uses Knowledge Graph
GAIA builds a personal knowledge graph from your connected tools. It links people to projects, projects to tasks, tasks to emails, emails to calendar events, and so on.
Related Concepts
Graph-Based Memory
Graph-based memory is an AI memory architecture that stores information as interconnected nodes and relationships, enabling rich contextual understanding and persistent knowledge across interactions.
Semantic Search
Semantic search is a search technique that understands the meaning and intent behind a query, returning results based on conceptual relevance rather than exact keyword matches.
Embeddings
Embeddings are dense numerical vector representations of data, such as text, images, or audio, that capture semantic meaning and relationships in a high-dimensional space.
Context Awareness
Context awareness in AI is the ability to understand the full situation surrounding a task or interaction, including who is involved, what has happened before, related projects, deadlines, and the user's preferences and patterns.
