Working group: Proposal to document new person types in the SNAP Cookbook
Members: Gabriel Bodard, Greta Hawes, Ulrike Peter, Scott Smith
Proposal
Recommend a range of classes (in lawd: namespace or elsewhere) for person-types/agent-types in SNAP Cookbook. This might allow distinction of historical from mythical figures (e.g. gods, monsters…), but should also permit fallback to higher-order entities (agent/group) for databases that don’t make that distinction internally
LAWD ontology of person-types, as recommended by SNAP:
A type drawn from this list:[1]
lawd:Person
lawd:Deity
lawd:Group
lawd:MythologicalCreature
Or, if you cannot distinguish between the above categories, use the catch-all:
The main objection to the current state of affairs is the hierarchical nature of the ontology: all of the person-types (person, sacred/divine, monster, animal, etc.) should be able to elegantly degrade to either "agent" or "group", suggesting perhaps that all of these classes should be seen as stackable, rather than a hierarchy—with only a top level class enabling compatibility between more and less granular datasets.
Working group: Proposal to document new person types in the SNAP Cookbook
Members: Gabriel Bodard, Greta Hawes, Ulrike Peter, Scott Smith
Proposal
Recommend a range of classes (in
lawd:
namespace or elsewhere) for person-types/agent-types in SNAP Cookbook. This might allow distinction of historical from mythical figures (e.g. gods, monsters…), but should also permit fallback to higher-order entities (agent/group) for databases that don’t make that distinction internallyLAWD ontology of person-types, as recommended by SNAP:
The main objection to the current state of affairs is the hierarchical nature of the ontology: all of the person-types (person, sacred/divine, monster, animal, etc.) should be able to elegantly degrade to either "agent" or "group", suggesting perhaps that all of these classes should be seen as stackable, rather than a hierarchy—with only a top level class enabling compatibility between more and less granular datasets.