  This vision paper proposes KGNet, an on-demand graph machine learning (GML)
as a service on top of RDF engines to support GML-enabled SPARQL queries. KGNet
automates the training of GML models on a KG by identifying a task-specific
subgraph. This helps reduce the task-irrelevant KG structure and properties for
better scalability and accuracy. While training a GML model on KG, KGNet
collects metadata of trained models in the form of an RDF graph called KGMeta,
which is interlinked with the relevant subgraphs in KG. Finally, all trained
models are accessible via a SPARQL-like query. We call it a GML-enabled query
and refer to it as SPARQLML. KGNet supports SPARQLML on top of existing RDF
engines as an interface for querying and inferencing over KGs using GML models.
The development of KGNet poses research opportunities in several areas,
including meta-sampling for identifying task-specific subgraphs, GML pipeline
automation with computational constraints, such as limited time and memory
budget, and SPARQLML query optimization. KGNet supports different GML tasks,
such as node classification, link prediction, and semantic entity matching. We
evaluated KGNet using two real KGs of different application domains. Compared
to training on the entire KG, KGNet significantly reduced training time and
memory usage while maintaining comparable or improved accuracy. The KGNet
source-code is available for further study
