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Finto AI also has an API, which makes it easy to integrate with other systems. More details information of the API can be found from this wiki on this page and the page Finto AI open API service, a detailed OpenAPI/Swagger technical documentation is available at https://ai.finto.fi/v1/ui/.
An API integration is already in place at the University of Jyväskylä : students submitting their Master's thesis to the JYX repository get suggestions from Annif that they can use or discard, then a librarian/informatician does a final check. A similar workflow is being piloted in the Osuva repository of the University of Vaasa.
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In the development process of Annif we have discovered and tested several algorithms, and selected the currently best combination for Finto AI (Maui, Omikuji and a TensorFlow based NN ensemble). The algorithms have been trained primarily with metadata from the Finna discovery service, but full text documents have also been used for fine-tuning the models. The development work of Annif of is ongoing and we will offer updates and improvements to Finto AI accordingly.
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Finto AI is based on Annif, a tool for automated subject indexing. You can read more about using Annif in it's GitHub Wiki. To work, Annif needs a controlled vocabulary (subject headings, thesaurus or classification) and existing metadata - Annif can then be used to assign subjects for new documents. This tool is built upon a combination of existing natural language processing and machine learning tools including Maui, Omikuji, fastText and Gensim. It is designed to be multilingual and it can support any subject vocabulary (in SKOS or a simple TSV format). It can be used either via a command-line interface or a microservice-style REST API. In fact, this demo demo API under Api.annif.org and the demo form at annif.org are the basis of Finto AI. As a development tool, Annif offers more methods than Finto AI, successful features will be integrated to Finto AI in time.
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