MVP of data visualization when using Numerordĭnātĭo; use cases 1) small amount of concepts, but a lot of metadata 2) huge amount of concepts

[fititnt]

This topic is Issue about a minimum viable product (MVP). It may take days, or months, but can track evolution in special to know if something was removed.

While HXLTM https://hxltm.etica.ai/ is focused mostly on production ready data conversion for multilingual terminology, an initial main reason for Numerordĭnātĭo is to aid interlink translations of concepts (so allow better re-usability of translations). However the second (which actually can make it more relevant compared to HXLTM) is aid bootstrapping organized taxonomies. Potentially on a large scale, when the exact decision of head terms may not be viable but least the ranked groups and head terms in some languages (which don't even need to be the same ones) can already exist.

The current version do already allow export to formats supported by HXLTM (such as TBX and TMX) but this part of coding systems to generate interlink translations (also inspired on how SEMICeu shares their result with RDF) can also be more optimized to be a playground to "upgrade" these taxonomies for what people use to exchange ontologies.

Even if these features become used on outside EticaAI, I already believe in most cases individual initiatives focused on just one subject (such as decision of type of gender identity codes and labels) may actually use just something like spreadsheets (as is common with HXL) that could be used to upgrade to everything else. In other words: if multilingual terminology, such as the Translation Initiative for COVID-19 https://github.com/EticaAI/tico-19-hxltm already are rare, this is would be even more rare. Yet some dogfooding on this this could make it easier for organizations focused on a subject to share tabular data while this alone can be used to potentially allow them to still meet mode "ontology" formats such as the ones done by SEMICeu.