Catalogue of Ancillary Resources

Find Machine Learning models, domain ontologies, terminologies, etc. to use with your TDM software


The OpenMinTeD Catalogue of Ancillary Knowledge Resources includes

  • Machine Learning models and computational grammars that can be integrated with TDM components, and
  • annotation resources, i.e. lexica, terminological lists, gazetteers, linguistic and domain ontologies, etc., that can be used for annotating content resources.

These resources, appropriately combined with generic TDM components, give rise to new applications catering for new domains or languages. All the resources include links to the point(s) they can be accessed from and can be deployed to build TDM applications which can then be executed through the OpenMinTeD platform.
The catalogue brings together resources added by users or imported from domain community portals and registries, all described through a harmonised metadata schema that includes for each resource:

  • administrative information, such as its title, a short description, licence or terms of use, provenance information (resource creator, creation dates, funding programs, etc.);
  • technical information, such as data format, size, links to documents that may help the users (e.g. user manuals, video tutorials, publications about the component, etc.), etc.

Users can browse through the catalogue or use the faceted search or a google-like free text query to discover resources according to specific criteria.


Developers of TDM applications, TDM Experts, NLP experts, Researchers


For developers of TDM applications that want to easily discover knowledge resources they can use with their generic software in order to build language or domain-specific applications. Users can easily find them, compare and contrast them with similar resources through the metadata records, integrate them in their applications and test the application in a real-life context.

Related Services

Service Funding

EC funds (H2020 grant 654021 for the OpenMinTeD project) & National funds for the GRNET cloud infrastructure on which the platform operates