PoolParty Semantic Classifier
Where Semantics meets Machine Learning
More accurate document classification through a combination of semantic knowledge graphs with machine learning.
A strong double pack: Benefit from 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.
- The combined approach improves the classification results by up to 3% as compared to traditional term-based approaches.
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.
- With the GraphSearch plugin, the ML libraries can be easily adopted for 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.
- Analyze judicial decisions.
Release Webinars & Notes
Bringing Machine Learning and Knowledge Graphs together
Discover the machine learning capabilities of PoolParty semantic software solution. In this webinar, we show how the PoolParty Semantic Classifier can significantly improve recommendation services, matchmaking capabilities, and classification tasks.