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Ontotext Metadata Studio 3.2 Enables Rapid Text Mining Development Based on an Organization’s Knowledge Graph

Users can use the taxonomical instance data in their knowledge graph to generate explainable and customizable out-of-the-box taxonomy-driven tags.

Note: TDWI’s editors carefully choose vendor-issued press releases about new or upgraded products and services. We have edited and/or condensed this release to highlight key features but make no claims as to the accuracy of the vendor's statements.

Ontotext, a leading global provider of enterprise knowledge graph (EKG) technology and semantic database engines, today released Ontotext Metadata Studio, version 3.2. The latest offering enables users to utilize the taxonomical instance data in their knowledge graph to achieve highly explainable and customizable out-of-the-box taxonomy-driven tagging.

More than just an end-user UI for establishing a set of documents that represent the business’s own version of the Ground Truth for tagging, Ontotext Metadata Studio 3.2 makes it easy for users to quickly determine whether a use case could be automated across any third-party text mining service. It also simplifies orchestrating complex text analysis across various third-party services and evaluates their quality against internal benchmarks or against one another.

With version 3.2, Ontotext Metadata Studio enables nontechnical end users to create, evaluate, and improve the quality of their text analytics service by tagging and linking against their own business domain model. With extensive explainability and control features, users not proficient in text analytics techniques can understand the causal relationships between the underlying data set, the specific text analytics service configuration, and the final output.

This enhancement enables efficient user intervention, putting the human truly in the loop and in control of the whole extraction process. Ontotext Metadata Studio is domain neutral and applicable for various domains and use cases because the application is dependent on the underlying domain model and content to be processed.

“Ontotext Metadata Studio’s built-in text analytics service is dynamically synchronized with the state of the instance data in the knowledge base so any changes applied to the data are instantly reflected in the extraction service’s behavior,” said Vassil Momtchev, chief technology officer, Ontotext. “With numerous UX improvements, Ontotext Metadata Studio 3.2 embraces the agile approach towards text analytics development, enabling short iteration time and fast feedback loops while trying to be as close to the business user as possible.”

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