A big part of this technology is automated reasoning (not AI) with #OWL Direct Entailments & more.
In #relationalDB, you may only retrieve what you've explicitly put into the database.
In #graphDB with OWL reasoning, the system INFERS additional info beyond what you put in, based on strict definitions of Descriptive & First Order Logic.
So, you can glean additional inferences, sometimes subtle, based on what you actually input into the DB.
This tech is rooted in #graphDB rather than #relationalDB, although some implementations use relational db for storing triples for each edge of graph (2 vertices + edge relation). You then use #SPARQL instead of #SQL to find information.
What caught my eye was https://babka.social/@kjr/111381934379266332
& https://babka.social/@kjr/111381988654130074
#OBOFoundry is used extensively in bio sciences & research.
A schema-less graph repo could work well here, especially with new standardization around https://www.nist.gov/system/files/documents/2021/10/14/nist-ai-rfi-cubrc_inc_004.pdf
@[email protected] I am currently finishing two projects in Hebrew archival text analysis. One is to detect entities (people, places, locations, dates and organizations) from archival collections and the relationships between them, so we can extract biographic information about the people: when and where born, name of the parents, organizations the person belonged, etc. The second one is just to extract the entities, link them to bibliographic entity files and improve the search of the data and the linking between documents of different archives.
GrazVLDH
Ramesh #NotGoingBack
Jeff Morris ZA