3 ms·

> But had they been adopted for what they are: well defined metadata according to vocabularies and ontologies that following accepted, interoperable standards,

> But had they been adopted for what they are: well defined metadata according to vocabularies and ontologies that following accepted, interoperable standards, the development of ground truth and knowledge graphs would have been much more advanced that the current state. Yes, this is very true. As an addendum, I think many people in industry don't quite realise how prevalent RDF and RDF-based ontologies are in academia. They're one of the main ways researchers integrate data. No one cares about lofty ideals, it's about the usefulness of RDF graph model and the various associated standards built on top of it.

la_fayette 3y ago
Which fields? Most projects i was involved, web scraping and putting the data in to csv was the main approach...
- nologic01 3y ago
  Biology and biomedical research is the main advanced application I am aware of. Its important enough a set of domains to show there is value in semantic technologies (rdf, owl etc) I have been wondering why it got adopted there and not elsewhere. Its not clear. Adoption or not of software and information tools is a mix of cultural and economic reasons beyond pure technical aspects. CSV, json etc are information black holes. Eventually people will realize that the systematic annotation of data within a metadata framework is not fanciful but an enabler of more confident and robust information exchange and processing [1] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4652617/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4652617/
- simongray 3y ago
  The key word in my comment is "integrate", not simply producing data. We're using it for language data in my field, e.g. currently I'm integrating the Danish WordNet with the English one + various other datasets. If you check out the common ontologies in use you will get an idea which areas are covered: https://lov.linkeddata.es/dataset/lov/ https://lov.linkeddata.es/dataset/lov/

la_fayette 3y ago

Which fields? Most projects i was involved, web scraping and putting the data in to csv was the main approach...

nologic01 3y ago
Biology and biomedical research is the main advanced application I am aware of. Its important enough a set of domains to show there is value in semantic technologies (rdf, owl etc) I have been wondering why it got adopted there and not elsewhere. Its not clear. Adoption or not of software and information tools is a mix of cultural and economic reasons beyond pure technical aspects. CSV, json etc are information black holes. Eventually people will realize that the systematic annotation of data within a metadata framework is not fanciful but an enabler of more confident and robust information exchange and processing [1] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4652617/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4652617/
simongray 3y ago
The key word in my comment is "integrate", not simply producing data. We're using it for language data in my field, e.g. currently I'm integrating the Danish WordNet with the English one + various other datasets. If you check out the common ontologies in use you will get an idea which areas are covered: https://lov.linkeddata.es/dataset/lov/ https://lov.linkeddata.es/dataset/lov/