Eliminate tagging headaches

Automatic discovery & extraction of meaning key for the semantic Web

Nstein's WCM 4 natively showcases our powerful TME (Text Mining Engine) to help organize, enrich and understand large quantities of content.

Automatic discovery and extraction of meaning provides the only consistent means to comprehensively and accurately tagging content in a consistent manner.

Discover your content's unique semantic footprint

Nstein TME automatically extracts concepts, people, organizations and locations stored in your content based on machine learning and your authority files.

This "semantic footprint" or semantic profile, becomes the foundation of not only better reuse and understanding of content but also is at the core of the semantic Web.

On-demand suggestion

Editors will love the new "Suggest" features exclusively available in the latest release of WCM 4. It uses the power of Nstein's TME to non-intrusively propose metadata including: tags, abstracts for textual content, related content for better referencing, categorization and more.

Out-of-the-box IPTC categorization

Nstein's WCM 4 provides categorization based on industry-specific IPTC taxonomy - and support for your custom taxonomy.

Based on our linguistic analysis, the system automatically categorizes content with the appropriate taxonomy nodes. Users maintain the ability to verify, modify or delete all the metadata created automatically.