graphify

To build a knowledge graph from code and documents, use a tool that parses diverse content types and extracts semantic relationships. This Skill constructs a persistent graph from your files, enabling cross-document visualization and queries without manual relationship mapping.

Yes, you can extract a knowledge graph from audio and video files. This Skill processes multimedia content by transcribing and analyzing it, structuring the extracted concepts into a queryable graph alongside your text and code sources.

The best way to visualize connections across multiple research papers is to construct a persistent knowledge graph that detects cross-document relationships. This approach identifies links between concepts in different files, revealing connections you might not have considered.

Yes, you need Python and specific libraries to process diverse content types for graph construction. Dependencies like pdfplumber for documents, whisper for audio, and spacy for semantic extraction are required to parse and structure your files into the graph.

During semantic extraction, a knowledge graph handles ambiguous relationships by tagging every edge with an audit trail. Edges are marked as EXTRACTED, INFERRED, or AMBIGUOUS, providing transparency on the source and confidence of the information.

Yes, you can query shortest paths and node explanations in a persistent knowledge graph. This Skill supports community detection and graph queries, allowing you to ask questions weeks later and explore connections without re-reading the source content.