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.
- Classify content
- Based on advanced Machine Learning
- Incorporating active learning
- Output exposed as REST API or GraphQL interfaces
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 Solution |
Custom Solution |
|---|---|---|
| IPTC Classifier |  |
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| Out of the Box Semantic Tagging | |
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| Maintenance Update 2 Times/Year | |
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| Optional Support Package | |
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| Classifier Customization with Your Taxonomy | |
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| Tagging Customization | |
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| Curation Tool | |
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| Built-in Learning of the System | |
Case Studies
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Euromoney: Improving Content Production Through a Unified Semantic Publishing Platform
Euromoney’s BCA Research chose Ontotext technology in its quest to create a new publishing and information platform, which would include the latest authoring, storing, and display technologies including semantic search and an RDF triplestore.
More Resources
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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.
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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.
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What is Text Analysis?
Fundamentals
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.
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What Is Machine Learning?
Fundamentals
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.
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What is Information Extraction?
Fundamentals
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.
