Neptune ML predicates used in SPARQL inference queries
The following predicates are used with SPARQL inference:
neptune-ml:timeout predicate
Specifies the timeout for connection with the remote server. Should not be confused with the query request timeout, which is the maximum amount of time the server can take to satisfy a request.
Note that if the query timeout occurs before the service timeout specified by the
neptune-ml:timeout predicate occurs, the service connection is canceled too.
neptune-ml:outputClass predicate
The neptune-ml:outputClass predicate is only used to define the class
of the predicted object for object prediction or predicted subject for subect prediction.
neptune-ml:outputScore predicate
The neptune-ml:outputScore predicate is a positive number that
represents the likelihood that the output of a machine learning model is correct.
neptune-ml:modelType predicate
The neptune-ml:modelType predicate specifies the type of machine learning
model being trained:
OBJECT_CLASSIFICATIONOBJECT_REGRESSIONOBJECT_PREDICTIONSUBJECT_PREDICTION
neptune-ml:input predicate
The neptune-ml:input predicate refers to the list of URIs used as inputs
for Neptune ML.
neptune-ml:output predicate
The neptune-ml:output predicate refers to the list of binding sets
where Neptune ML returns results.
neptune-ml:predicate predicate
The neptune-ml:predicate predicate is used differently depending on
the task being performed:
For object or subject prediction: defines the type of predicate (the edge or relationship type).
For object classification and regression: defines the literal (property) we want to predict.
neptune-ml:batchSize predicate
The neptune-ml:batchSize specifies the input size for the remote
service call.