neighbour-source-scan

To find related documents in a knowledge base, you can automate thematic overlap analysis between source documents. This identifies cross-source relationships and suggests connections to expand your information graph efficiently.

Cross-source relationship detection for an information graph identifies thematic overlaps between source documents. It enables efficient curation by suggesting missed connections, ensuring a comprehensive knowledge base.

Yes, you need Python to detect thematic overlaps between sources. The processing and pattern matching logic required for analyzing thematic overlaps and identifying relationships relies on Python scripts.

Yes, you can incrementally add new sources to a structured knowledge base. After ingesting a new source document, the scan automatically finds related sources that were previously missed, facilitating incremental wiki maintenance.

Yes, source curation supports both pre- and post-Warner & Wäger tagging scenarios. The relationship detection analyzes thematic overlaps effectively regardless of your current tagging implementation stage.