Knowledge mining in digital asset management

Cognitive Search
Computer Vision
Face

Solution Idea

If you'd like to see us expand this article with more information, such as potential use cases, alternative services, implementation considerations, or pricing guidance, let us know with GitHub Feedback!

This architecture demonstrates how to use knowledge mining in digital asset management.

Potential use cases

Given the amount of unstructured data created daily, many companies struggle to make use of or find information within their files. One of the key functions of a digital asset management system is to allow assets to be easily retrieved.

Knowledge mining can help with retrieval by providing a search index that enables users to quickly locate what they are looking for.

Architecture

There are three steps in knowledge mining: ingest, enrich, and explore.

Architecture diagram that shows knowledge mining used in digital asset management to make assets discoverable.

  • Ingest

    The ingest step aggregates content from a range of sources, including structured and unstructured data. For digital asset management, sources can be technical content like article and image archives, photos, videos, internal documents, marketing assets, and brochures.

  • Enrich

    The enrich step uses AI capabilities to extract information, find patterns, and deepen understanding. For example, you can enrich the content by using automatic image captioning and object detection with computer vision, celebrity recognition, language translation, and entity recognition.

  • Explore

    The explore step is exploring the data via search, existing business applications, or analytics solutions. For example, you can integrate the search index into a web site.

Components

Key technologies used to implement tools for technical content review and research:

Next steps