Content Classification

Get automated classification against a standard or your own taxonomy. Ontotext’s Content Classification makes it easier to promote, deliver and reuse your content.

Key Features:

  • Classify content
  • Based on advanced Machine Learning
  • Incorporating active learning
  • Output exposed as REST API or GraphQL interfaces

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How You’ll Benefit

Categorize your content automatically

Enjoy consistent classification tags

Organize your assets

Scale better than your human teams

How it Works

Semantic analysis

Content Classification categorizes unstructured information by performing knowledge graph-powered semantic analysis over the full text of the documents and applying supervised machine learning and rules that automate classification decisions.

Semantic fingerprint

When classifying with big hierarchical taxonomies (up to 10 000 classes on up to 8 levels), the machine learning model leverages the semantic fingerprint of the document, the concept rank and the relationships in the knowledge graph such as class proximity, class co-occurrence, parent-child relationships, etc.

Standard Solution vs Custom Solution

Solutions Comparison Standard
IPTC Classifier
Out of the Box Semantic Tagging
Maintenance Update 2 Times/Year
Optional Support Package
Classifier Customization with Your Taxonomy  
Tagging Customization  
Curation Tool  
Built-in Learning of the System  

Case Studies

More Resources

  • Text Analysis for Content Management

    White Papers

    In this white paper, we’ve provided an overview of how we at Ontotext develop and implement Knowledge Graph-based Content Management solutions.

  • Text Analytics for Enterprise Use

    White Papers

    This exclusive Ontotext white paper describes how semantic technologies make any content intelligent and turn it into revenue for your publishing business.

  • What is Text Analysis?


    Text Analysis is about parsing texts in order to extract machine-readable facts from them. The purpose of Text Analysis is to create sets of structured data out of heaps of unstructured, heterogeneous documents.

  • What Is Machine Learning?


    Machine learning (ML) is about teaching computer programs to recognize images, words and sounds by giving them a lot of examples to learn from. Once developed and trained, such algorithms help create systems that can automatically respond to and interpret data.

  • What is Information Extraction?


    Information extraction is the process of extracting specific information from textual sources. It enables the automation of tasks such as smart content classification, integrated search, management and delivery. It also facilitates data-driven activities such as mining for patterns and trends, uncovering hidden relationships, etc.