Ontotext Metadata Studio

Handle metadata! Control tagging!

Ontotext’s Metadata Studio makes it easy to:

  • Create & curate content metadata as the human benchmark for automatic tagging;
  • Integrate different text mining services;
  • Gain insights about your benchmark corpora and the performance of automatic tagging against them so that you can improve both.

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Why Choose Ontotext’s Metadata Studio?

Transform Content Into Knowledge

  • Benchmark – establish expected quality threshold;
  • Measure – evaluate any third-party text mining service for your current use case;
  • Build or Buy – quickly find out whether your use case could be automated or not.

Generate High Quality Analytics Information

  • Gold Standard Corpora – produce foundational data to design and build a bespoke text mining algorithm fully under your control and supervision;
  • Coverage – gain insights into the coverage and quality of your benchmark corpora;
  • Compare – compare the performance of various text mining services.

Operate With Ever Changing Reference Data

  • In sync with the knowledge – automatically synchronize concepts and instance data;
  • Seamless control – inspect and improve the quality of content metadata in an easy to understand, straightforward manner;
  • Lower cost – substantially reduce the cost and complexity of staying up to date with the data.

Showcase Demonstrators

If you can’t measure it, you can’t improve it. Ontotext Metadata Studio and its report dashboards helps measuring the outcome of the investment into various natural language technologies. The product and its common annotation model becomes a central point to evaluate the organizational natural language strategy and improve the synergy between SMEs and text engineers. – Vassil Momtchev, CTO of Ontotext

Short overview of Ontotext Metadata Studio

Who is Ontotext’s Metadata Studio for?

Non-technical Subject Matter Experts who can create benchmark metadata
NLP engineers and data scientists who can evaluate text mining services and inspect what are their weak and strong sides.
Editorial staff / data quality stakeholders who can manually curate automatically produced metadata
Solution architects who can integrate a secure manual annotation product into their solution


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