You can start by importing unstructured content you want to classify into EDG, selecting a vocabulary with concepts that will be used to classify this content, and providing training data AutoClassifier will learn on (either by manually tagging some resources, or by importing existing training data where available).

You can learn more by reading introductory blog posts about AutoClassifier here and here, or with an informative webinar featuring a demonstration.

Customers can also utilize our Jumpstart Program as a way to get started. For more information about this program and to discuss evaluation licenses, contact us at sales@topquadrant.com.

Every project and organization has different needs. TopQuadrant provides various flexible services related to unstructured content management goals, including:

• Content and tags sets lifecycle management;
• Classification strategy definition;
• Modeling services for vocabularies that act as AutoClassifier’s sources of classes;
• Integration of the automated classification into a larger workflow.

AutoClassifier and Tagger are built on the TopBraid EDG platform, and can make use of data, text and documents in any format that can be imported into TopBraid EDG. This includes any format that can be converted to RDF, and data sources such as REST APIs. TopQuadrant offers TopBraid Composer as a powerful development environment for such conversions and integrations, and offers training and professional services to support the process. Examples of formats that have been successfully used are XML, PDF, Microsoft.

The main AutoClassifier functionalities can be accessed through SPARQL functions in a SPARQL Endpoint or specific RESTful web services described in the product documentation.

Every tag proposed by AutoClassifier has a confidence value expressed as a percentage. Low-confidence tags can be discarded automatically, and proposed tags can always be reviewed manually.

One approach is to import these documents into an RDF content graph that can afterwards be managed in EDG infrastructure. Another approach is to integrate the functionality into an existing content repository via the AutoClassifier API.

The size of training datasets doesn’t have to increase in proportion with the size of the documents corpus you want to classify. Their quality matters, i.e. training documents should be tagged with concepts of which labelling forms do occur in the document, but you don’t need an exhaustive set of training documents for all available concepts before each of them can be assigned by the auto-classification process. More on training datasets can be found here.

AutoClassifier relies on an algorithm that requires a language-specific stemmer and stopword lists. AutoClassifier currently includes these for English, French, German and Spanish, and we may add more if demand exists. The rest of the tool is language-agnostic. The quality of results should be fairly independent of the language.