experiments to produce JSON-LD agent data
Starting a new issue for this to track it separately, I will continue to provide updates.
As a first experiment, I have been working on getting json-ld out of NRP for agents and their relationships, partly for my own education, but also so we can eventually actually do that as part of this collaboration. I'm posting my current results, which are NOT DONE.
You can see them here: http://dhen.webfactional.com/accounting/agent-jsonld/. This is our local herbal network, which has a much simpler org structure than Sensorica. We'll get Sensorica too, but need to get the latest code out there.
The code is here: https://github.com/valnet/valuenetwork/blob/master/valuenetwork/valueaccounting/views.py, the last method in the file, apprx line 10624, or do a find on "agent_jsonld". (This is if you are a glutton for punishment and have some time on your hands!)
Status:
- A few known bugs, which involve mostly me figuring out how to get what we want out of the python library we picked. Like a triple within a triple; and ordering for human readability.
- I think I need to separate it into 2 things: a context local to the network for agent types and relationship types, and a pull of agent and relationship data that will reference that context.
- I am just using whatever existing vocab is convenient for the sake of experiment, not making any statements or recommendations. When we get all of that decided, I'll change it.
- LOTS and LOTS of questions I have on how to do this best. At some point if I can get some help from @elf-pavlik about those, that would be great. But I need to do some more work to define and compile them, and also see if I can figure some more out myself. Will post when that is done.