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Automated Content Tagging using Artificial Intelligence (AI)

The use of AI or Machine Learning (ML) to automatically predict content classifications. This is also known as "Auto-tagging" or "Auto-coding" (Healthcare applications) or "Auto-classification".
Enables the following Capabilities
AI automated tagging in Production
Automated tagging integrated with knowledge graph
Ability to automatically tag true content "aboutness"
Ability to use LLM AI tools to enrich knowledge graphs
Used in the following Interventions
Integrate AI Automated Tagging
Integrate an automated tagging solution that applies metadata in the form of "annotations" or "tags" to content automatically.
Other technologies used
Machine Learning (ML)
Artificial Intelligence (AI)
Large Language Model (LLM) AI
Artificial Intelligence (AI) Text Classification
Integrate automated tagging with custom vocabularies
Integrate an automated tagging solution with custom "in-house" vocabularies. This is often required when generic vocabs are not suitable.
Other technologies used
Artificial Intelligence (AI)
Artificial Intelligence (AI) Text Classification
Integrate AI automated tagging with custom metadata models
This intervention is required when a specific in-house metadata model is in use - e.g. a taxonomy or a knowledge graph - in order to give valuable results in a custom domain.
Other technologies used
Artificial Intelligence (AI)
Graph databases
Replace legacy information systems with a KGaaS
Replace legacy information management systems with a Knowledge Graph as a Service (KGaaS) platform, so that you can launch quickly and early, and pivot with ease around an evolvable core business information model.
Other technologies used
Content Graphs
Graph databases
KGaaS - Knowledge Graph as a Service
Knowledge Graphs
Linked Data
Knowledge Graph Creation Tools
Involved in these Products
Tagmatic
Tagmatic is an AI automated text classification SaaS product by Data Language. It learns on the fly, is self-optimising, and language-agnostic.
Tagmatic
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FAQs
What is "AI Content Auto-tagging"?

AI Auto-tagging uses trained machine learning (ML) models to automatically recognize what a media item is about. or to recognize known concepts within the media item content, and to associate the correct "tags" (a "tag" is also known as "annotation" / "code" / "concept" / "entity" / "label") to the content item in question. This can be any kind of media: Text, Image, Video, Audio, etc.

What are the biggest problems with using AI for Auto-Tagging?
  1. Tagging AI that does not let you use your own tags (they just use wikipedia, or ITC or other public vocabularies).
  2. The overhead of re-training models to maintain quality and add new tags as they are created can be high.
  3. Predicting true "aboutness" is hard, and most systems just tag with "mentions" and classic NLP approaches.