Neptune ML predicates used in SPARQL inference queries - Amazon Neptune
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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:





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.