unstructured-ontology-manager

To add structured metadata to unstructured documents, you assign structured fields like date, topic, and participants to your files. This enables precise, attribute-based searching across your organizational knowledge repositories without complex database schemas.

The best way to search unstructured knowledge base content is by defining structured ontology objects with metadata tags. You can then use natural language to perform attribute-based searches, such as finding all meeting notes from a specific project participant.

Yes, you can bind ontology objects to specific knowledge base directories and mount them to digital employees or assistants. This facilitates seamless integration of unstructured documents with digital assistant workflows through dynamic resource mounting.

No, you do not need a complex database schema. The Skill manages unstructured knowledge base content by defining and applying structured ontology objects and metadata tags directly, relying only on a Redis dependency for backend operations.

An ontology object organizes non-tabular resources by allowing you to create custom categories like Meeting Note, apply structured metadata tags, and perform attribute-based searches. This structures unorganized files for intelligent retrieval.

While effective for attribute-based search, this approach requires defining ontology objects and manually tagging unstructured data. It supports creation and deletion of resources but depends on consistent metadata application for precise retrieval results.