Personal Knowledge Graph Engine
An AI-powered system that automatically builds, maintains, and queries a personal knowledge graph from all your digital information - emails, documents, notes, browsing history - enabling semantic search and knowledge discovery. Vision Transform scattered information into an interconnected knowledge graph that reveals patterns, connections, and insights across your entire digital life. Core Capabilities Automated Ingestion Email integration (Gmail, Outlook) Document parsing (PDF, Word, Markdown) Web browsing history Chat logs (Slack, Discord, WhatsApp) Code repositories Calendar events Task managers (Notion, Obsidian) Intelligent Extraction Entity Recognition: People, places, organizations, concepts Relationship Mapping: How entities connect Temporal Analysis: When information was created/modified Context Preservation: Original source and surrounding information Topic Modeling: Automatic categorization Knowledge Graph Structure Nodes: Entities (people, concepts, documents, projects) Edges: Relationships (mentioned_in, related_to, created_by, depends_on) Properties: Metadata (date, confidence, source, category) Technical Architecture Data Pipeline Ingestion: Multi-source connectors Processing: NLP for entity/relationship extraction Deduplication: Merge equivalent entities Enrichment: External knowledge base linking Storage: Graph database (Neo4j or custom) Indexing: Vector embeddings for semantic search Core Technologies Graph Database: Neo4j or DGraph NLP: spaCy, transformers for entity recognition Vector Database: Pinecone or Weaviate for semantic search LLM: GPT-4 for relationship inference Frontend: React with graph visualization (Cytoscape.
Read more →