Semantic Tagging

Enrich your content with Ontotext’s Semantic Tagging. It is powered by a knowledge graph that combines public and private data and enables you to discover mentions of both known and novel concepts and link them to the text in your content.

Key Features:

  • Named Entity Recognition
  • Entity Disambiguation
  • Relation Extraction
  • Based on advanced Machine Learning
  • Output exposed as REST API or GraphQL interfaces
  • Incorporating active learning

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How You’ll Benefit

Discover concepts of interest in your content automatically

Use as a foundation for smarter search capabilities

Customize your content streams

Scale better than your human teams

How it Works

Semantic fingerprint

Our tagging services analyze the text, extract concepts, identify topics, keywords and important relationships, while taking care of properly disambiguating similarly sounding entities. The resulting semantic fingerprint of the document comprises metadata, linked to a knowledge graph that serves as the foundation of all content management solutions.

The Concept Extraction Service

The key ingredient of our Semantic Tagging solution is the Concept Extraction Service (CES), which extracts the essence from your content. CES enriches the processed documents with semantic tags, containing references to a knowledge graph, thus creating meaningful connections between the unstructured text and the structured data.

Standard Solution vs Custom Solution

Solutions Comparison Standard
Extraction of People, Organizations and Location
Relations between People, Organizations and Location
Dataset based on Wikipedia and DBPedia
Dataset Update 2 times/ year
Optional Support Package
Custom Tailored Extraction for your Domain
Build your proprietary Knowledge Graph
Curation tool
Built-in learning of the system

Case Studies