W-Knowledge transforms unstructured documents — PDFs, Word files, web pages, research papers — into a structured knowledge graph you can query in natural language. It parses documents, extracts entities and relationships, links them into a traversable graph, and then lets you ask questions and get cited answers grounded in your own corpus. Built for research teams, knowledge management practitioners, and organizations drowning in documents they cannot effectively search.
W-Knowledge handles PDFs, DOCX, HTML, plain text, and scanned images (via OCR) in a single ingestion pipeline. Tables, figures, footnotes, and section hierarchy are preserved in the parsed output so extracted entities carry accurate positional context. Bulk ingest entire document repositories and let the pipeline run asynchronously — results appear in the graph as each document completes.
Named entity recognition identifies people, organizations, locations, dates, technical terms, and domain-specific concepts. Relationship extraction then maps how entities relate — "authored by", "subsidiary of", "mentions", "contradicts" — building a rich, traversable graph rather than a flat list of keywords. Custom entity types and relationship schemas let you tailor extraction to your domain.
All extracted entities and relationships are stored in a queryable graph database. Traverse the graph to discover connections across documents that would be invisible in a keyword search — find every document that mentions a specific organization in the context of a particular regulation, or surface all authors who have written about a topic and the papers that cite each other.
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