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PoolParty Semantic Classifier

Where semantics meets machine learning to complement automated tagging.

Store and reference documents used in the Classifier. Get insights on the content in your library. 

Easily define the settings for training the Semantic Classifier.

The PoolParty Semantic Classifier uses knowledge graphs and machine learning to precisely classify documents and other business objects into their respective domains. The Classifier only requires a small training dataset to understand how to classify documents automatically.

Document classification is a complementary feature to auto-tagging, and can be used in combination with it to automate precise metadata enrichment.

Machine Learning + Semantic AI

Build your knowledge model in PoolParty and improve the quality of your training set by semantic content annotation.

Select machine learning algorithms such as SVM, Deep Learning and Naive Bayes for content classification.
Benefit from a rich feature set such as terms, concepts, shadow concepts which gives you more flexibility when training classifiers.

Content Classification for Knowledge Engineers, Data Scientists and Developers

A user-friendly interface that enables non-technical experts to perform classification tasks and benefit from machine learning.
New resources can be classified via the PoolParty API.

Using PoolParty GraphSearch, machine learning libraries from the Classifier can be easily integrated to semantic applications.

Large content repositories can be classified on top of a Spark cluster.

Application Scenarios for Semantic Content Classification

Reduce the manual effort of classifying inbound documents or news.
Enhance your recommendation services.
Complement your rule-based systems for fraud detection.
Improve your customer retention management by precise sentiment analysis.

Useful Resources


Bringing Machine Learning and Knowledge Graphs Together

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White Paper

How Text Mining and NLP Drives Semantic AI

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